






'''simple docstring'''
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase="attention"				):


   """simple docstring"""




   lowercase_						:     List[str]							     =  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]				)
   lowercase_						:     Tuple							     =  k_tmp.reshape(k_tmp.shape[0]    ,		k_tmp.shape[1] * k_tmp.shape[2]				)
   lowercase_						:     int							     =  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]				)
   lowercase_						:     Any							     =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]    ,		o_tmp.shape[2]				)
   lowercase_						:     Any							     =  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]				)
   lowercase_						:     str							     =  q_tmp.reshape(q_tmp.shape[0]    ,		q_tmp.shape[1] * q_tmp.shape[2]				)
   lowercase_						:     int							     =  np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]				)
   lowercase_						:     Any							     =  v_tmp.reshape(v_tmp.shape[0]    ,		v_tmp.shape[1] * v_tmp.shape[2]				)
   return k, o, q, v




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=False				):


   """simple docstring"""




   if split_mlp_wi:
      lowercase_						:     List[str]							     =  params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
      lowercase_						:     Tuple							     =  params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
      lowercase_						:     Optional[Any]							     =  (wi_a, wi_a)
   else:
      lowercase_						:     int							     =  params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

   lowercase_						:     List[Any]							     =  params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
   return wi, wo




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		*, _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = False				):


   """simple docstring"""




   lowercase_						:     Any							     =  traverse_util.flatten_dict(variables["target"]				)
   lowercase_						:     List[str]							     =  {"/".join(_UpperCamelCase				): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   lowercase_						:     int							     =  "encoder/encoder/mlp/wi_0/kernel" in old
   print("Split MLP:"    ,		_UpperCamelCase				)

   lowercase_						:     str							     =  collections.OrderedDict()

   # Shared embeddings.
   lowercase_						:     str							     =  old["token_embedder/embedding"]

   # Encoder.
   for i in range(_UpperCamelCase				):
      # Block i, layer 0 (Self Attention).
      lowercase_						:     Optional[int]							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"pre_attention_layer_norm"				)
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"attention"				)
      lowercase_						:     Union[str, Any]							     =  layer_norm
      lowercase_						:     Union[str, Any]							     =  k.T
      lowercase_						:     int							     =  o.T
      lowercase_						:     str							     =  q.T
      lowercase_						:     Optional[Any]							     =  v.T

      # Block i, layer 1 (MLP).
      lowercase_						:     Any							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		"pre_mlp_layer_norm"				)
      lowercase_     ,		lowercase_						:     Optional[Any]							     =  tax_mlp_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"    ,		_UpperCamelCase				)
      lowercase_						:     int							     =  layer_norm
      if split_mlp_wi:
         lowercase_						:     Dict							     =  wi[0].T
         lowercase_						:     Optional[int]							     =  wi[1].T
      else:
         lowercase_						:     Dict							     =  wi.T
      lowercase_						:     List[str]							     =  wo.T
      if scalable_attention:
         # convert the rel_embedding of each layer
         lowercase_						:     int							     =  tax_relpos_bias_lookup(
             _UpperCamelCase    ,		_UpperCamelCase    ,		"encoder"				).T

   lowercase_						:     int							     =  old["encoder/encoder_norm/scale"]

   if not scalable_attention:
      lowercase_						:     Optional[int]							     =  tax_relpos_bias_lookup(
          _UpperCamelCase    ,		0    ,		"encoder"				).T
      lowercase_						:     List[str]							     =  tax_relpos_bias_lookup(
          _UpperCamelCase    ,		0    ,		"decoder"				).T

   if not is_encoder_only:
      # Decoder.
      for i in range(_UpperCamelCase				):
         # Block i, layer 0 (Self Attention).
         lowercase_						:     Dict							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_self_attention_layer_norm"				)
         lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     List[Any]							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"self_attention"				)
         lowercase_						:     List[str]							     =  layer_norm
         lowercase_						:     Any							     =  k.T
         lowercase_						:     str							     =  o.T
         lowercase_						:     Dict							     =  q.T
         lowercase_						:     List[Any]							     =  v.T

         # Block i, layer 1 (Cross Attention).
         lowercase_						:     List[Any]							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_cross_attention_layer_norm"				)
         lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  tax_attention_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"encoder_decoder_attention"				)
         lowercase_						:     List[str]							     =  layer_norm
         lowercase_						:     str							     =  k.T
         lowercase_						:     Optional[Any]							     =  o.T
         lowercase_						:     Optional[Any]							     =  q.T
         lowercase_						:     List[str]							     =  v.T

         # Block i, layer 2 (MLP).
         lowercase_						:     Tuple							     =  tax_layer_norm_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		"pre_mlp_layer_norm"				)
         lowercase_     ,		lowercase_						:     Any							     =  tax_mlp_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"    ,		_UpperCamelCase				)
         lowercase_						:     int							     =  layer_norm
         if split_mlp_wi:
            lowercase_						:     List[str]							     =  wi[0].T
            lowercase_						:     Any							     =  wi[1].T
         else:
            lowercase_						:     int							     =  wi.T
         lowercase_						:     int							     =  wo.T

         if scalable_attention:
            # convert the rel_embedding of each layer
            lowercase_						:     int							     =  tax_relpos_bias_lookup(_UpperCamelCase    ,		_UpperCamelCase    ,		"decoder"				).T

      lowercase_						:     str							     =  old["decoder/decoder_norm/scale"]

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         lowercase_						:     List[Any]							     =  old["decoder/logits_dense/kernel"].T

   return new




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      lowercase_						:     int							     =  state_dict["shared.weight"]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         lowercase_						:     Tuple							     =  state_dict["shared.weight"]

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("Using shared word embeddings as lm_head."				)
         lowercase_						:     List[Any]							     =  state_dict["shared.weight"]

   return state_dict




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     int							     =  checkpoints.load_tax_checkpoint(_UpperCamelCase				)
   lowercase_						:     Any							     =  convert_tax_to_pytorch(
       _UpperCamelCase    ,		num_layers=config.num_layers    ,		is_encoder_only=_UpperCamelCase    ,		scalable_attention=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  make_state_dict(_UpperCamelCase    ,		_UpperCamelCase				)
   model.load_state_dict(_UpperCamelCase    ,		strict=_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = False    ,		_UpperCamelCase = False    ,		):


   """simple docstring"""




   lowercase_						:     Dict							     =  MTaConfig.from_json_file(_UpperCamelCase				)
   print(F"""Building PyTorch model from configuration: {config}"""				)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      lowercase_						:     Any							     =  UMTaEncoderModel(_UpperCamelCase				)
   else:
      lowercase_						:     Dict							     =  UMTaForConditionalGeneration(_UpperCamelCase				)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
   model.save_pretrained(_UpperCamelCase				)

   # Verify that we can load the checkpoint.
   model.from_pretrained(_UpperCamelCase				)
   print("Done"				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
 )
 parser.add_argument(
     '--scalable_attention',
     action='store_true',
     help='Whether the model uses scaled attention (umt5 model)',
     default=False,
 )
 UpperCamelCase__			=      parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )












'''simple docstring'''
import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__			=      16
UpperCamelCase__			=      32




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16				):


   """simple docstring"""




   lowercase_						:     int							     =  AutoTokenizer.from_pretrained("bert-base-cased"				)
   lowercase_						:     Optional[Any]							     =  load_dataset("glue"    ,		"mrpc"				)

   def tokenize_function(_UpperCamelCase				):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_						:     Any							     =  tokenizer(examples["sentence1"]    ,		examples["sentence2"]    ,		truncation=_UpperCamelCase    ,		max_length=_UpperCamelCase				)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      lowercase_						:     str							     =  datasets.map(
          _UpperCamelCase    ,		batched=_UpperCamelCase    ,		remove_columns=["idx", "sentence1", "sentence2"]    ,		)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowercase_						:     str							     =  tokenized_datasets.rename_column("label"    ,		"labels"				)

   def collate_fn(_UpperCamelCase				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase_						:     List[str]							     =  128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowercase_						:     List[str]							     =  16
      elif accelerator.mixed_precision != "no":
         lowercase_						:     Tuple							     =  8
      else:
         lowercase_						:     Dict							     =  None

      return tokenizer.pad(
          _UpperCamelCase    ,		padding="longest"    ,		max_length=_UpperCamelCase    ,		pad_to_multiple_of=_UpperCamelCase    ,		return_tensors="pt"    ,		)

   # Instantiate dataloaders.
   lowercase_						:     Optional[int]							     =  DataLoader(
       tokenized_datasets["train"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)
   lowercase_						:     str							     =  DataLoader(
       tokenized_datasets["validation"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 UpperCamelCase__			=      mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,		_UpperCamelCase				) == "1":
      lowercase_						:     Tuple							     =  2
   # Initialize accelerator
   lowercase_						:     Tuple							     =  Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_						:     str							     =  config["lr"]
   lowercase_						:     Optional[int]							     =  int(config["num_epochs"]				)
   lowercase_						:     List[str]							     =  int(config["seed"]				)
   lowercase_						:     List[Any]							     =  int(config["batch_size"]				)

   lowercase_						:     List[Any]							     =  evaluate.load("glue"    ,		"mrpc"				)

   # New Code #
   # We now can define an inner training loop function. It should take a batch size as the only parameter,
   # and build the dataloaders in there.
   # It also gets our decorator
   @find_executable_batch_size(starting_batch_size=_UpperCamelCase				)
   def inner_training_loop(_UpperCamelCase				):
      # And now just move everything below under this function
      # We need to bring in the Accelerator object from earlier
      nonlocal accelerator
      # And reset all of its attributes that could hold onto any memory:
      accelerator.free_memory()

      # Then we can declare the model, optimizer, and everything else:
      set_seed(_UpperCamelCase				)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase_						:     List[str]							     =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,		return_dict=_UpperCamelCase				)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      lowercase_						:     List[Any]							     =  model.to(accelerator.device				)

      # Instantiate optimizer
      lowercase_						:     Tuple							     =  AdamW(params=model.parameters()    ,		lr=_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     Any							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase				)

      # Instantiate scheduler
      lowercase_						:     Union[str, Any]							     =  get_linear_schedule_with_warmup(
          optimizer=_UpperCamelCase    ,		num_warmup_steps=100    ,		num_training_steps=(len(_UpperCamelCase				) * num_epochs)    ,		)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  accelerator.prepare(
          _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

      # Now we train the model
      for epoch in range(_UpperCamelCase				):
         model.train()
         for step, batch in enumerate(_UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            lowercase_						:     Optional[Any]							     =  model(**_UpperCamelCase				)
            lowercase_						:     List[Any]							     =  outputs.loss
            accelerator.backward(_UpperCamelCase				)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(_UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
               lowercase_						:     Optional[int]							     =  model(**_UpperCamelCase				)
            lowercase_						:     Union[str, Any]							     =  outputs.logits.argmax(dim=-1				)
            lowercase_     ,		lowercase_						:     str							     =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=_UpperCamelCase    ,		references=_UpperCamelCase    ,		)

         lowercase_						:     int							     =  metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""    ,		_UpperCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  argparse.ArgumentParser(description="Simple example of training script."				)
   parser.add_argument(
       "--mixed_precision"    ,		type=_UpperCamelCase    ,		default=_UpperCamelCase    ,		choices=["no", "fp16", "bf16", "fp8"]    ,		help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."    ,		)
   parser.add_argument("--cpu"    ,		action="store_true"    ,		help="If passed, will train on the CPU."				)
   lowercase_						:     int							     =  parser.parse_args()
   lowercase_						:     Optional[Any]							     =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()




