###  What does this PR do?
Currently, Accelerate has supported ascend npu([see](https://github.com/huggingface/accelerate/pull/1676)).
This PR enables users to leverage the ascend npu for training and inference of 🤗 Transformers models.
For example, you can run the official glue text-classification task using ascend npu with below command:
```bash
export TASK_NAME=sst2
time python -m torch.distributed.run --nproc_per_node 8 run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir ./output
```

Below are the output logs:
```text
WARNING:__main__:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 2, device: npu:2, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 1, device: npu:1, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 5, device: npu:5, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 3, device: npu:3, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 261.94it/s]
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 7, device: npu:7, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
  0%|                                                                                       | 0/3 [00:00<?, ?it/s]07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 224.74it/s]
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 229.06it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 4, device: npu:4, n_gpu: 1distributed training: True, 16-bits training: False
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 220.52it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
  0%|                                                                                       | 0/3 [00:00<?, ?it/s]07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 6, device: npu:6, n_gpu: 1distributed training: True, 16-bits training: False
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 218.07it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 144.02it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 220.95it/s]
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,044 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,134 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,225 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,255 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,349 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,451 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,487 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
07/18/2023 22:10:04 - WARNING - __main__ - Process rank: 0, device: npu:0, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:04 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./output/runs/Jul18_22-09-51_localhost.localdomain,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=./output,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./output,
save_on_each_node=False,
save_safetensors=False,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
07/18/2023 22:10:04 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
07/18/2023 22:10:04 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 279.65it/s]
[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,346 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,352 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "sst2",
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,353 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,354 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file tokenizer_config.json
[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,355 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,356 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|modeling_utils.py:2600] 2023-07-18 22:10:04,436 >> loading weights file bert-base-cased/pytorch_model.bin
[INFO|modeling_utils.py:3319] 2023-07-18 22:10:06,936 >> Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:06,936 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - INFO - __main__ - Sample 14592 of the training set: {'sentence': 'a great movie ', 'label': 1, 'idx': 14592, 'input_ids': [101, 170, 1632, 2523, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - INFO - __main__ - Sample 3278 of the training set: {'sentence': 'entertaining , if somewhat standardized , action ', 'label': 1, 'idx': 3278, 'input_ids': [101, 15021, 117, 1191, 4742, 18013, 117, 2168, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - INFO - __main__ - Sample 36048 of the training set: {'sentence': 'even when there are lulls , the emotions seem authentic , ', 'label': 1, 'idx': 36048, 'input_ids': [101, 1256, 1165, 1175, 1132, 181, 11781, 1116, 117, 1103, 6288, 3166, 16047, 117, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
[INFO|trainer.py:763] 2023-07-18 22:10:11,652 >> The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[INFO|trainer.py:1686] 2023-07-18 22:10:14,427 >> ***** Running training *****
[INFO|trainer.py:1687] 2023-07-18 22:10:14,427 >>   Num examples = 67,349
[INFO|trainer.py:1688] 2023-07-18 22:10:14,427 >>   Num Epochs = 3
[INFO|trainer.py:1689] 2023-07-18 22:10:14,427 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:1692] 2023-07-18 22:10:14,427 >>   Total train batch size (w. parallel, distributed & accumulation) = 256
[INFO|trainer.py:1693] 2023-07-18 22:10:14,427 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:1694] 2023-07-18 22:10:14,427 >>   Total optimization steps = 792
[INFO|trainer.py:1695] 2023-07-18 22:10:14,429 >>   Number of trainable parameters = 108,311,810
  0%|                                                                                     | 0/792 [00:00<?, ?it/s][W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
  0%|                                                                           | 1/792 [00:24<5:20:41, 24.33s/it] 63%|███████████████████████████████████████████████▎                           | 500/792 [02:39<00:57,  5.10it/s]{'loss': 0.2132, 'learning_rate': 7.373737373737374e-06, 'epoch': 1.89}                                           
 63%|███████████████████████████████████████████████▎                           | 500/792 [02:43<00:57,  5.10it/s][INFO|trainer.py:2807] 2023-07-18 22:13:00,287 >> Saving model checkpoint to ./output/checkpoint-500
[INFO|configuration_utils.py:458] 2023-07-18 22:13:00,289 >> Configuration saved in ./output/checkpoint-500/config.json
[INFO|modeling_utils.py:1851] 2023-07-18 22:13:01,488 >> Model weights saved in ./output/checkpoint-500/pytorch_model.bin
[INFO|tokenization_utils_base.py:2214] 2023-07-18 22:13:01,489 >> tokenizer config file saved in ./output/checkpoint-500/tokenizer_config.json
[INFO|tokenization_utils_base.py:2221] 2023-07-18 22:13:01,489 >> Special tokens file saved in ./output/checkpoint-500/special_tokens_map.json
100%|███████████████████████████████████████████████████████████████████████████| 792/792 [03:44<00:00,  5.38it/s][INFO|trainer.py:1934] 2023-07-18 22:13:58,740 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)


{'train_runtime': 224.3121, 'train_samples_per_second': 900.741, 'train_steps_per_second': 3.531, 'train_loss': 0.17379718356662327, 'epoch': 3.0}
100%|███████████████████████████████████████████████████████████████████████████| 792/792 [03:44<00:00,  3.53it/s]
[INFO|trainer.py:2807] 2023-07-18 22:13:58,745 >> Saving model checkpoint to ./output
[INFO|configuration_utils.py:458] 2023-07-18 22:13:58,747 >> Configuration saved in ./output/config.json
[INFO|modeling_utils.py:1851] 2023-07-18 22:13:59,855 >> Model weights saved in ./output/pytorch_model.bin
[INFO|tokenization_utils_base.py:2214] 2023-07-18 22:13:59,857 >> tokenizer config file saved in ./output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2221] 2023-07-18 22:13:59,857 >> Special tokens file saved in ./output/special_tokens_map.json
***** train metrics *****
  epoch                    =        3.0
  train_loss               =     0.1738
  train_runtime            = 0:03:44.31
  train_samples            =      67349
  train_samples_per_second =    900.741
  train_steps_per_second   =      3.531
07/18/2023 22:13:59 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:763] 2023-07-18 22:13:59,922 >> The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3081] 2023-07-18 22:13:59,926 >> ***** Running Evaluation *****
[INFO|trainer.py:3083] 2023-07-18 22:13:59,926 >>   Num examples = 872
[INFO|trainer.py:3086] 2023-07-18 22:13:59,926 >>   Batch size = 8
 100%|█████████████████████████████████████████████████████████████████████████████| 14/14 [00:07<00:00,  1.85it/s]
***** eval metrics *****
  epoch                   =        3.0
  eval_accuracy           =     0.9186
  eval_loss               =      0.258
  eval_runtime            = 0:00:09.61
  eval_samples            =        872
  eval_samples_per_second =     90.662
  eval_steps_per_second   =      1.456

real	4m38.911s
user	39m59.583s
sys	4m9.578s

```

cc @sgugger 
add ascend npu accelerator support. ###  What does this PR do?
Currently, Accelerate has supported ascend npu([see](https://github.com/huggingface/accelerate/pull/1676)).
This PR enables users to leverage the ascend npu for training and inference of 🤗 Transformers models.
For example, you can run the official glue text-classification task using ascend npu with below command:
```bash
export TASK_NAME=sst2
time python -m torch.distributed.run --nproc_per_node 8 run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir ./output
```

Below are the output logs:
```text
WARNING:__main__:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 2, device: npu:2, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 1, device: npu:1, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 5, device: npu:5, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 3, device: npu:3, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 261.94it/s]
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 7, device: npu:7, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
  0%|                                                                                       | 0/3 [00:00<?, ?it/s]07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 224.74it/s]
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 229.06it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 4, device: npu:4, n_gpu: 1distributed training: True, 16-bits training: False
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 220.52it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
  0%|                                                                                       | 0/3 [00:00<?, ?it/s]07/18/2023 22:10:00 - WARNING - __main__ - Process rank: 6, device: npu:6, n_gpu: 1distributed training: True, 16-bits training: False
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 218.07it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 144.02it/s]
07/18/2023 22:10:00 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 220.95it/s]
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,044 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,134 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,225 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,255 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,349 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,451 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:03,487 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
07/18/2023 22:10:04 - WARNING - __main__ - Process rank: 0, device: npu:0, n_gpu: 1distributed training: True, 16-bits training: False
07/18/2023 22:10:04 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./output/runs/Jul18_22-09-51_localhost.localdomain,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=./output,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./output,
save_on_each_node=False,
save_safetensors=False,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset Infos from /root/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
07/18/2023 22:10:04 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
07/18/2023 22:10:04 - WARNING - datasets.builder - Found cached dataset glue (/root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
07/18/2023 22:10:04 - INFO - datasets.info - Loading Dataset info from /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
100%|██████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 279.65it/s]
[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,346 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,352 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "sst2",
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,353 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,354 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1841] 2023-07-18 22:10:04,355 >> loading file tokenizer_config.json
[INFO|configuration_utils.py:710] 2023-07-18 22:10:04,355 >> loading configuration file bert-base-cased/config.json
[INFO|configuration_utils.py:768] 2023-07-18 22:10:04,356 >> Model config BertConfig {
  "_name_or_path": "bert-base-cased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.31.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|modeling_utils.py:2600] 2023-07-18 22:10:04,436 >> loading weights file bert-base-cased/pytorch_model.bin
[INFO|modeling_utils.py:3319] 2023-07-18 22:10:06,936 >> Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:3331] 2023-07-18 22:10:06,936 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:06 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - INFO - __main__ - Sample 14592 of the training set: {'sentence': 'a great movie ', 'label': 1, 'idx': 14592, 'input_ids': [101, 170, 1632, 2523, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - INFO - __main__ - Sample 3278 of the training set: {'sentence': 'entertaining , if somewhat standardized , action ', 'label': 1, 'idx': 3278, 'input_ids': [101, 15021, 117, 1191, 4742, 18013, 117, 2168, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - INFO - __main__ - Sample 36048 of the training set: {'sentence': 'even when there are lulls , the emotions seem authentic , ', 'label': 1, 'idx': 36048, 'input_ids': [101, 1256, 1165, 1175, 1132, 181, 11781, 1116, 117, 1103, 6288, 3166, 16047, 117, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6dd95798535d1820.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-0f938cc5dd2d8410.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
07/18/2023 22:10:11 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d00cbf8279e5d543.arrow
[INFO|trainer.py:763] 2023-07-18 22:10:11,652 >> The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[W LegacyTypeDispatch.h:79] Warning: AutoNonVariableTypeMode is deprecated and will be removed in 1.10 release. For kernel implementations please use AutoDispatchBelowADInplaceOrView instead, If you are looking for a user facing API to enable running your inference-only workload, please use c10::InferenceMode. Using AutoDispatchBelowADInplaceOrView in user code is under risk of producing silent wrong result in some edge cases. See Note [AutoDispatchBelowAutograd] for more details. (function operator())
[INFO|trainer.py:1686] 2023-07-18 22:10:14,427 >> ***** Running training *****
[INFO|trainer.py:1687] 2023-07-18 22:10:14,427 >>   Num examples = 67,349
[INFO|trainer.py:1688] 2023-07-18 22:10:14,427 >>   Num Epochs = 3
[INFO|trainer.py:1689] 2023-07-18 22:10:14,427 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:1692] 2023-07-18 22:10:14,427 >>   Total train batch size (w. parallel, distributed & accumulation) = 256
[INFO|trainer.py:1693] 2023-07-18 22:10:14,427 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:1694] 2023-07-18 22:10:14,427 >>   Total optimization steps = 792
[INFO|trainer.py:1695] 2023-07-18 22:10:14,429 >>   Number of trainable parameters = 108,311,810
  0%|                                                                                     | 0/792 [00:00<?, ?it/s][W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1278] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration,  which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
  0%|                                                                           | 1/792 [00:24<5:20:41, 24.33s/it] 63%|███████████████████████████████████████████████▎                           | 500/792 [02:39<00:57,  5.10it/s]{'loss': 0.2132, 'learning_rate': 7.373737373737374e-06, 'epoch': 1.89}                                           
 63%|███████████████████████████████████████████████▎                           | 500/792 [02:43<00:57,  5.10it/s][INFO|trainer.py:2807] 2023-07-18 22:13:00,287 >> Saving model checkpoint to ./output/checkpoint-500
[INFO|configuration_utils.py:458] 2023-07-18 22:13:00,289 >> Configuration saved in ./output/checkpoint-500/config.json
[INFO|modeling_utils.py:1851] 2023-07-18 22:13:01,488 >> Model weights saved in ./output/checkpoint-500/pytorch_model.bin
[INFO|tokenization_utils_base.py:2214] 2023-07-18 22:13:01,489 >> tokenizer config file saved in ./output/checkpoint-500/tokenizer_config.json
[INFO|tokenization_utils_base.py:2221] 2023-07-18 22:13:01,489 >> Special tokens file saved in ./output/checkpoint-500/special_tokens_map.json
100%|███████████████████████████████████████████████████████████████████████████| 792/792 [03:44<00:00,  5.38it/s][INFO|trainer.py:1934] 2023-07-18 22:13:58,740 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)


{'train_runtime': 224.3121, 'train_samples_per_second': 900.741, 'train_steps_per_second': 3.531, 'train_loss': 0.17379718356662327, 'epoch': 3.0}
100%|███████████████████████████████████████████████████████████████████████████| 792/792 [03:44<00:00,  3.53it/s]
[INFO|trainer.py:2807] 2023-07-18 22:13:58,745 >> Saving model checkpoint to ./output
[INFO|configuration_utils.py:458] 2023-07-18 22:13:58,747 >> Configuration saved in ./output/config.json
[INFO|modeling_utils.py:1851] 2023-07-18 22:13:59,855 >> Model weights saved in ./output/pytorch_model.bin
[INFO|tokenization_utils_base.py:2214] 2023-07-18 22:13:59,857 >> tokenizer config file saved in ./output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2221] 2023-07-18 22:13:59,857 >> Special tokens file saved in ./output/special_tokens_map.json
***** train metrics *****
  epoch                    =        3.0
  train_loss               =     0.1738
  train_runtime            = 0:03:44.31
  train_samples            =      67349
  train_samples_per_second =    900.741
  train_steps_per_second   =      3.531
07/18/2023 22:13:59 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:763] 2023-07-18 22:13:59,922 >> The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: idx, sentence. If idx, sentence are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3081] 2023-07-18 22:13:59,926 >> ***** Running Evaluation *****
[INFO|trainer.py:3083] 2023-07-18 22:13:59,926 >>   Num examples = 872
[INFO|trainer.py:3086] 2023-07-18 22:13:59,926 >>   Batch size = 8
 100%|█████████████████████████████████████████████████████████████████████████████| 14/14 [00:07<00:00,  1.85it/s]
***** eval metrics *****
  epoch                   =        3.0
  eval_accuracy           =     0.9186
  eval_loss               =      0.258
  eval_runtime            = 0:00:09.61
  eval_samples            =        872
  eval_samples_per_second =     90.662
  eval_steps_per_second   =      1.456

real	4m38.911s
user	39m59.583s
sys	4m9.578s

```

cc @sgugger 
# coding=utf-8
# Copyright 2022 Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Flax XLM-RoBERTa model."""

from typing import Callable, Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxBaseModelOutputWithPooling,
    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxQuestionAnsweringModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xlm_roberta import XLMRobertaConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "xlm-roberta-base"
_CONFIG_FOR_DOC = "XLMRobertaConfig"

remat = nn_partitioning.remat

FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]


# Copied from transformers.models.roberta.modeling_flax_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx):
    """
    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
    are ignored. This is modified from fairseq's `utils.make_positions`.

    Args:
        input_ids: jnp.ndarray
        padding_idx: int

    Returns: jnp.ndarray
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = (input_ids != padding_idx).astype("i4")

    if mask.ndim > 2:
        mask = mask.reshape((-1, mask.shape[-1]))
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
        incremental_indices = incremental_indices.reshape(input_ids.shape)
    else:
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask

    return incremental_indices.astype("i4") + padding_idx


XLM_ROBERTA_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`XLMRobertaConfig`]): Model configuration class with all the parameters of the
            model. Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
"""

XLM_ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->XLMRoberta
class FlaxXLMRobertaEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->XLMRoberta
class FlaxXLMRobertaSelfAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))

    @nn.compact
    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states: Optional[jnp.array] = None,
        init_cache: bool = False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        # get query proj
        query_states = self.query(hidden_states)
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            # self_attention
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->XLMRoberta
class FlaxXLMRobertaSelfOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->XLMRoberta
class FlaxXLMRobertaAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.self = FlaxXLMRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        self.output = FlaxXLMRobertaSelfOutput(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states=None,
        init_cache=False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
        attn_outputs = self.self(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            key_value_states=key_value_states,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_outputs[1],)

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->XLMRoberta
class FlaxXLMRobertaIntermediate(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->XLMRoberta
class FlaxXLMRobertaOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->XLMRoberta
class FlaxXLMRobertaLayer(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxXLMRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
        self.intermediate = FlaxXLMRobertaIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxXLMRobertaOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxXLMRobertaAttention(self.config, causal=False, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
    ):
        # Self Attention
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=encoder_hidden_states,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]

        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None:
                outputs += (cross_attention_outputs[1],)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->XLMRoberta
class FlaxXLMRobertaLayerCollection(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxXLMRobertaCheckpointLayer = remat(FlaxXLMRobertaLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxXLMRobertaCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxXLMRobertaLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # Check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
                    f"       {head_mask.shape[0]}."
                )

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states,
                attention_mask,
                head_mask[i] if head_mask is not None else None,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                deterministic,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->XLMRoberta
class FlaxXLMRobertaEncoder(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxXLMRobertaLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return self.layer(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->XLMRoberta
class FlaxXLMRobertaPooler(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        cls_hidden_state = hidden_states[:, 0]
        cls_hidden_state = self.dense(cls_hidden_state)
        return nn.tanh(cls_hidden_state)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaLMHead with Roberta->XLMRoberta
class FlaxXLMRobertaLMHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.decoder = nn.Dense(
            self.config.vocab_size,
            dtype=self.dtype,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.dense(hidden_states)
        hidden_states = ACT2FN["gelu"](hidden_states)
        hidden_states = self.layer_norm(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaClassificationHead with Roberta->XLMRoberta
class FlaxXLMRobertaClassificationHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.out_proj = nn.Dense(
            self.config.num_labels,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.dense(hidden_states)
        hidden_states = nn.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaPreTrainedModel with Roberta->XLMRoberta, roberta->xlm-roberta, ROBERTA->XLM_ROBERTA
class FlaxXLMRobertaPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = XLMRobertaConfig
    base_model_prefix = "xlm-roberta"

    module_class: nn.Module = None

    def __init__(
        self,
        config: XLMRobertaConfig,
        input_shape: Tuple = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.ones_like(input_ids)
        position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False
            )

        random_params = module_init_outputs["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        past_key_values: dict = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        if self.config.add_cross_attention:
            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
            # changed by FlaxXLMRobertaAttention module
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else:
                mutable = False

            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
                mutable=mutable,
            )

            # add updated cache to model output
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        else:
            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
            )

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->XLMRoberta
class FlaxXLMRobertaModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxXLMRobertaEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxXLMRobertaEncoder(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.pooler = FlaxXLMRobertaPooler(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids: Optional[jnp.ndarray] = None,
        position_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # make sure `token_type_ids` is correctly initialized when not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # make sure `position_ids` is correctly initialized when not passed
        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        hidden_states = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        outputs = self.encoder(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            deterministic=deterministic,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    "The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaModel(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaModule


append_call_sample_docstring(FlaxXLMRobertaModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForMaskedLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForMaskedLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings("""XLM RoBERTa Model with a `language modeling` head on top.""", XLM_ROBERTA_START_DOCSTRING)
class FlaxXLMRobertaForMaskedLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMaskedLMModule


append_call_sample_docstring(
    FlaxXLMRobertaForMaskedLM,
    _CHECKPOINT_FOR_DOC,
    FlaxBaseModelOutputWithPooling,
    _CONFIG_FOR_DOC,
    mask="<mask>",
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForSequenceClassificationModule with Roberta->XLMRoberta
class FlaxXLMRobertaForSequenceClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.classifier = FlaxXLMRobertaClassificationHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForSequenceClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForSequenceClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForMultipleChoiceModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

        if not return_dict:
            return (reshaped_logits,) + outputs[2:]

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
    a softmax) e.g. for RocStories/SWAG tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForMultipleChoice(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMultipleChoiceModule


overwrite_call_docstring(
    FlaxXLMRobertaForMultipleChoice, XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxXLMRobertaForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForTokenClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxTokenClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
    for Named-Entity-Recognition (NER) tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForTokenClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForTokenClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForQuestionAnsweringModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForQuestionAnsweringModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]

        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        if not return_dict:
            return (start_logits, end_logits) + outputs[1:]

        return FlaxQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForQuestionAnswering(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForQuestionAnsweringModule


append_call_sample_docstring(
    FlaxXLMRobertaForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        token_type_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLM with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs


append_call_sample_docstring(
    FlaxXLMRobertaForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)

# coding=utf-8
# Copyright 2022 Facebook AI Research and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Flax XLM-RoBERTa model."""

from typing import Callable, Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxBaseModelOutputWithPooling,
    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxQuestionAnsweringModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xlm_roberta import XLMRobertaConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "xlm-roberta-base"
_CONFIG_FOR_DOC = "XLMRobertaConfig"

remat = nn_partitioning.remat

FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "xlm-roberta-base",
    "xlm-roberta-large",
    # See all XLM-RoBERTa models at https://huggingface.co/models?filter=xlm-roberta
]


# Copied from transformers.models.roberta.modeling_flax_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx):
    """
    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
    are ignored. This is modified from fairseq's `utils.make_positions`.

    Args:
        input_ids: jnp.ndarray
        padding_idx: int

    Returns: jnp.ndarray
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = (input_ids != padding_idx).astype("i4")

    if mask.ndim > 2:
        mask = mask.reshape((-1, mask.shape[-1]))
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
        incremental_indices = incremental_indices.reshape(input_ids.shape)
    else:
        incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask

    return incremental_indices.astype("i4") + padding_idx


XLM_ROBERTA_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`XLMRobertaConfig`]): Model configuration class with all the parameters of the
            model. Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
"""

XLM_ROBERTA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->XLMRoberta
class FlaxXLMRobertaEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->XLMRoberta
class FlaxXLMRobertaSelfAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))

    @nn.compact
    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states: Optional[jnp.array] = None,
        init_cache: bool = False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        # get query proj
        query_states = self.query(hidden_states)
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            # self_attention
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->XLMRoberta
class FlaxXLMRobertaSelfOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->XLMRoberta
class FlaxXLMRobertaAttention(nn.Module):
    config: XLMRobertaConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.self = FlaxXLMRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        self.output = FlaxXLMRobertaSelfOutput(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states=None,
        init_cache=False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
        attn_outputs = self.self(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            key_value_states=key_value_states,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_outputs[1],)

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->XLMRoberta
class FlaxXLMRobertaIntermediate(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->XLMRoberta
class FlaxXLMRobertaOutput(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer with Bert->XLMRoberta
class FlaxXLMRobertaLayer(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxXLMRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
        self.intermediate = FlaxXLMRobertaIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxXLMRobertaOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxXLMRobertaAttention(self.config, causal=False, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
    ):
        # Self Attention
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=encoder_hidden_states,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]

        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None:
                outputs += (cross_attention_outputs[1],)
        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->XLMRoberta
class FlaxXLMRobertaLayerCollection(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxXLMRobertaCheckpointLayer = remat(FlaxXLMRobertaLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxXLMRobertaCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxXLMRobertaLayer(self.config, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # Check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
                    f"       {head_mask.shape[0]}."
                )

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states,
                attention_mask,
                head_mask[i] if head_mask is not None else None,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                deterministic,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->XLMRoberta
class FlaxXLMRobertaEncoder(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxXLMRobertaLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return self.layer(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->XLMRoberta
class FlaxXLMRobertaPooler(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )

    def __call__(self, hidden_states):
        cls_hidden_state = hidden_states[:, 0]
        cls_hidden_state = self.dense(cls_hidden_state)
        return nn.tanh(cls_hidden_state)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaLMHead with Roberta->XLMRoberta
class FlaxXLMRobertaLMHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.decoder = nn.Dense(
            self.config.vocab_size,
            dtype=self.dtype,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.dense(hidden_states)
        hidden_states = ACT2FN["gelu"](hidden_states)
        hidden_states = self.layer_norm(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaClassificationHead with Roberta->XLMRoberta
class FlaxXLMRobertaClassificationHead(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.out_proj = nn.Dense(
            self.config.num_labels,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.dense(hidden_states)
        hidden_states = nn.tanh(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaPreTrainedModel with Roberta->XLMRoberta, roberta->xlm-roberta, ROBERTA->XLM_ROBERTA
class FlaxXLMRobertaPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = XLMRobertaConfig
    base_model_prefix = "xlm-roberta"

    module_class: nn.Module = None

    def __init__(
        self,
        config: XLMRobertaConfig,
        input_shape: Tuple = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.ones_like(input_ids)
        position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs, input_ids, attention_mask, token_type_ids, position_ids, head_mask, return_dict=False
            )

        random_params = module_init_outputs["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        past_key_values: dict = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        if self.config.add_cross_attention:
            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
            # changed by FlaxXLMRobertaAttention module
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else:
                mutable = False

            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
                mutable=mutable,
            )

            # add updated cache to model output
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        else:
            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
            )

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->XLMRoberta
class FlaxXLMRobertaModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxXLMRobertaEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxXLMRobertaEncoder(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.pooler = FlaxXLMRobertaPooler(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids: Optional[jnp.ndarray] = None,
        position_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # make sure `token_type_ids` is correctly initialized when not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # make sure `position_ids` is correctly initialized when not passed
        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        hidden_states = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        outputs = self.encoder(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            deterministic=deterministic,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]
        pooled = self.pooler(hidden_states) if self.add_pooling_layer else None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    "The bare XLM RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaModel(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaModule


append_call_sample_docstring(FlaxXLMRobertaModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForMaskedLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForMaskedLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings("""XLM RoBERTa Model with a `language modeling` head on top.""", XLM_ROBERTA_START_DOCSTRING)
class FlaxXLMRobertaForMaskedLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMaskedLMModule


append_call_sample_docstring(
    FlaxXLMRobertaForMaskedLM,
    _CHECKPOINT_FOR_DOC,
    FlaxBaseModelOutputWithPooling,
    _CONFIG_FOR_DOC,
    mask="<mask>",
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForSequenceClassificationModule with Roberta->XLMRoberta
class FlaxXLMRobertaForSequenceClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.classifier = FlaxXLMRobertaClassificationHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForSequenceClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForSequenceClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForMultipleChoiceModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

        if not return_dict:
            return (reshaped_logits,) + outputs[2:]

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
    a softmax) e.g. for RocStories/SWAG tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForMultipleChoice(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForMultipleChoiceModule


overwrite_call_docstring(
    FlaxXLMRobertaForMultipleChoice, XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxXLMRobertaForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForTokenClassificationModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxTokenClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
    for Named-Entity-Recognition (NER) tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForTokenClassification(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForTokenClassificationModule


append_call_sample_docstring(
    FlaxXLMRobertaForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForQuestionAnsweringModule with Bert->XLMRoberta, with self.bert->self.roberta
class FlaxXLMRobertaForQuestionAnsweringModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]

        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        if not return_dict:
            return (start_logits, end_logits) + outputs[1:]

        return FlaxQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
class FlaxXLMRobertaForQuestionAnswering(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForQuestionAnsweringModule


append_call_sample_docstring(
    FlaxXLMRobertaForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLMModule with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLMModule(nn.Module):
    config: XLMRobertaConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.roberta = FlaxXLMRobertaModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.lm_head = FlaxXLMRobertaLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        token_type_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.roberta(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.roberta.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.lm_head(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    """
    XLM Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    XLM_ROBERTA_START_DOCSTRING,
)
# Copied from transformers.models.roberta.modeling_flax_roberta.FlaxRobertaForCausalLM with Roberta->XLMRoberta
class FlaxXLMRobertaForCausalLM(FlaxXLMRobertaPreTrainedModel):
    module_class = FlaxXLMRobertaForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs


append_call_sample_docstring(
    FlaxXLMRobertaForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)
