


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


UpperCAmelCase_      : Optional[int]  					=       logging.getLogger(__name__)

UpperCAmelCase_      : Dict  					=       tf.data.AUTOTUNE







def        SCREAMING_SNAKE_CASE_						(			)	->		Union[str, Any]:

       """simple docstring"""

       a_					:		Dict							=  argparse.ArgumentParser(description='Train a masked language model on TPU.'				)
       parser.add_argument(
           '--pretrained_model_config'  ,    type=__A  ,    default='roberta-base'  ,    help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'  ,    )
       parser.add_argument(
           '--tokenizer'  ,    type=__A  ,    default='unigram-tokenizer-wikitext'  ,    help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'  ,    )

       parser.add_argument(
           '--per_replica_batch_size'  ,    type=__A  ,    default=8  ,    help='Batch size per TPU core.'  ,    )

       parser.add_argument(
           '--no_tpu'  ,    action='store_true'  ,    help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'  ,    )

       parser.add_argument(
           '--tpu_name'  ,    type=__A  ,    help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'  ,    default='local'  ,    )

       parser.add_argument(
           '--tpu_zone'  ,    type=__A  ,    help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'  ,    )

       parser.add_argument(
           '--gcp_project'  ,    type=__A  ,    help='Google cloud project name. Only used for non-Colab TPU nodes.'				)

       parser.add_argument(
           '--bfloat16'  ,    action='store_true'  ,    help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'  ,    )

       parser.add_argument(
           '--train_dataset'  ,    type=__A  ,    help='Path to training dataset to load. If the path begins with `gs://`'
           ' then the dataset will be loaded from a Google Cloud Storage bucket.'  ,    )

       parser.add_argument(
           '--shuffle_buffer_size'  ,    type=__A  ,    default=2**18  ,    help='Size of the shuffle buffer (in samples)'  ,    )

       parser.add_argument(
           '--eval_dataset'  ,    type=__A  ,    help='Path to evaluation dataset to load. If the path begins with `gs://`'
           ' then the dataset will be loaded from a Google Cloud Storage bucket.'  ,    )

       parser.add_argument(
           '--num_epochs'  ,    type=__A  ,    default=1  ,    help='Number of epochs to train for.'  ,    )

       parser.add_argument(
           '--learning_rate'  ,    type=__A  ,    default=1e-4  ,    help='Learning rate to use for training.'  ,    )

       parser.add_argument(
           '--weight_decay_rate'  ,    type=__A  ,    default=1e-3  ,    help='Weight decay rate to use for training.'  ,    )

       parser.add_argument(
           '--max_length'  ,    type=__A  ,    default=5_12  ,    help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'  ,    )

       parser.add_argument(
           '--mlm_probability'  ,    type=__A  ,    default=0.15  ,    help='Fraction of tokens to mask during training.'  ,    )

       parser.add_argument('--output_dir'  ,    type=__A  ,    required=__A  ,    help='Path to save model checkpoints to.'				)
       parser.add_argument('--hub_model_id'  ,    type=__A  ,    help='Model ID to upload to on the Hugging Face Hub.'				)

       a_					:		Optional[int]							=  parser.parse_args()
       return args







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]				)	->		List[Any]:

       """simple docstring"""

       try:
              if args.tpu_name:
                     a_					:		List[str]							=  tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name  ,    zone=args.tpu_zone  ,    project=args.gcp_project				)
              else:
                     a_					:		List[Any]							=  tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
              raise RuntimeError(
                  'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
                  '--gcp_project. When running on a TPU VM, use --tpu_name local.'				)

       tf.config.experimental_connect_to_cluster(__A				)
       tf.tpu.experimental.initialize_tpu_system(__A				)

       return tpu







def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		List[Any]:

       """simple docstring"""

       a_					:		Dict							=  0
       for file in file_list:
              a_					:		Union[str, Any]							=  file.split('/'				)[-1]
              a_					:		Tuple							=  re.search(R'-\d+-(\d+)\.tfrecord'  ,    __A				).group(1				)
              a_					:		List[str]							=  int(__A				)
              num_samples += sample_count

       return num_samples







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Dict  ,    __A :			str  ,    __A :			str  ,    __A :			Optional[int]  ,    __A :			str=None				)	->		List[Any]:

       """simple docstring"""

       a_					:		Union[str, Any]							=  count_samples(__A				)
       a_					:		int							=  tf.data.Dataset.from_tensor_slices(__A				)
       if shuffle:
              a_					:		Optional[int]							=  dataset.shuffle(len(__A				)				)
       a_					:		Tuple							=  tf.data.TFRecordDataset(__A  ,    num_parallel_reads=__A				)
       # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
       a_					:		Optional[Any]							=  dataset.apply(tf.data.experimental.assert_cardinality(__A				)				)
       a_					:		Dict							=  dataset.map(__A  ,    num_parallel_calls=__A				)
       if shuffle:
              assert shuffle_buffer_size is not None
              a_					:		Dict							=  dataset.shuffle(args.shuffle_buffer_size				)
       a_					:		Dict							=  dataset.batch(__A  ,    drop_remainder=__A				)
       a_					:		Tuple							=  dataset.map(__A  ,    num_parallel_calls=__A				)
       a_					:		Tuple							=  dataset.prefetch(__A				)
       return dataset







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Tuple:

       """simple docstring"""

       if not args.no_tpu:
              a_					:		Dict							=  initialize_tpu(__A				)
              a_					:		int							=  tf.distribute.TPUStrategy(__A				)
       else:
              a_					:		int							=  tf.distribute.OneDeviceStrategy(device='/gpu:0'				)

       if args.bfloataa:
              tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'				)

       a_					:		Tuple							=  AutoTokenizer.from_pretrained(args.tokenizer				)
       a_					:		List[str]							=  AutoConfig.from_pretrained(args.pretrained_model_config				)
       a_					:		Any							=  tokenizer.vocab_size

       a_					:		List[Any]							=  tf.io.gfile.glob(os.path.join(args.train_dataset  ,    '*.tfrecord'				)				)
       if not training_records:
              raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""				)
       a_					:		Any							=  tf.io.gfile.glob(os.path.join(args.eval_dataset  ,    '*.tfrecord'				)				)
       if not eval_records:
              raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""				)

       a_					:		List[Any]							=  count_samples(__A				)

       a_					:		str							=  num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
       a_					:		Union[str, Any]							=  steps_per_epoch * args.num_epochs

       with strategy.scope():
              a_					:		Union[str, Any]							=  TFAutoModelForMaskedLM.from_config(__A				)
              model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
              a_							,		a_					:		List[str]							=  create_optimizer(
                  num_train_steps=__A  ,    num_warmup_steps=total_train_steps // 20  ,    init_lr=args.learning_rate  ,    weight_decay_rate=args.weight_decay_rate  ,    )

              # Transformers models compute the right loss for their task by default when labels are passed, and will
              # use this for training unless you specify your own loss function in compile().
              model.compile(optimizer=__A  ,    metrics=['accuracy']				)

       def decode_fn(__A :			Dict				):
              a_					:		Union[str, Any]							=  {
                  'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa  ,    shape=(args.max_length,)				),
                  'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa  ,    shape=(args.max_length,)				),
              }
              return tf.io.parse_single_example(__A  ,    __A				)

       # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
       # use their methods in our data pipeline.
       a_					:		Dict							=  DataCollatorForLanguageModeling(
           tokenizer=__A  ,    mlm_probability=args.mlm_probability  ,    mlm=__A  ,    return_tensors='tf'				)

       def mask_with_collator(__A :			Optional[Any]				):
              # TF really needs an isin() function
              a_					:		Optional[Any]							=  (
                  ~tf.cast(batch['attention_mask']  ,    tf.bool				)
                  | (batch['input_ids'] == tokenizer.cls_token_id)
                  | (batch['input_ids'] == tokenizer.sep_token_id)
              )
              a_							,		a_					:		Optional[int]							=  data_collator.tf_mask_tokens(
                  batch['input_ids']  ,    vocab_size=len(__A				)  ,    mask_token_id=tokenizer.mask_token_id  ,    special_tokens_mask=__A  ,    )
              return batch

       a_					:		Optional[Any]							=  args.per_replica_batch_size * strategy.num_replicas_in_sync

       a_					:		Dict							=  prepare_dataset(
           __A  ,    decode_fn=__A  ,    mask_fn=__A  ,    batch_size=__A  ,    shuffle=__A  ,    shuffle_buffer_size=args.shuffle_buffer_size  ,    )

       a_					:		Any							=  prepare_dataset(
           __A  ,    decode_fn=__A  ,    mask_fn=__A  ,    batch_size=__A  ,    shuffle=__A  ,    )

       a_					:		int							=  []
       if args.hub_model_id:
              callbacks.append(
                  PushToHubCallback(output_dir=args.output_dir  ,    hub_model_id=args.hub_model_id  ,    tokenizer=__A				)				)

       model.fit(
           __A  ,    validation_data=__A  ,    epochs=args.num_epochs  ,    callbacks=__A  ,    )

       model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
   UpperCAmelCase_      : List[Any]  					=       parse_args()
   main(args)










import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a		:       List[Any]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	'segformer'
      def __init__(      self					:			Dict		,      __lowercase					:			str=3		,      __lowercase					:			Dict=4		,      __lowercase					:			Any=[2, 2, 2, 2]		,      __lowercase					:			Optional[int]=[8, 4, 2, 1]		,      __lowercase					:			List[str]=[32, 64, 160, 256]		,      __lowercase					:			Union[str, Any]=[7, 3, 3, 3]		,      __lowercase					:			Optional[int]=[4, 2, 2, 2]		,      __lowercase					:			Any=[1, 2, 5, 8]		,      __lowercase					:			List[str]=[4, 4, 4, 4]		,      __lowercase					:			Any="gelu"		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.0		,      __lowercase					:			Optional[Any]=0.1		,      __lowercase					:			int=0.02		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			Optional[Any]=1e-6		,      __lowercase					:			Tuple=256		,      __lowercase					:			List[Any]=255		,      **__lowercase					:			Union[str, Any]		,      )							->      List[Any]:
        super().__init__(**__lowercase					)

        if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
          warnings.warn(
              """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
              """ removed, as the behaviour will default to that of reshape_last_stage = True."""		,      __lowercase		,      )

        __UpperCAmelCase							:	Tuple           =  num_channels
        __UpperCAmelCase							:	Any           =  num_encoder_blocks
        __UpperCAmelCase							:	List[Any]           =  depths
        __UpperCAmelCase							:	Dict           =  sr_ratios
        __UpperCAmelCase							:	int           =  hidden_sizes
        __UpperCAmelCase							:	Optional[Any]           =  patch_sizes
        __UpperCAmelCase							:	List[Any]           =  strides
        __UpperCAmelCase							:	List[str]           =  mlp_ratios
        __UpperCAmelCase							:	Tuple           =  num_attention_heads
        __UpperCAmelCase							:	List[str]           =  hidden_act
        __UpperCAmelCase							:	Tuple           =  hidden_dropout_prob
        __UpperCAmelCase							:	Dict           =  attention_probs_dropout_prob
        __UpperCAmelCase							:	Any           =  classifier_dropout_prob
        __UpperCAmelCase							:	Optional[int]           =  initializer_range
        __UpperCAmelCase							:	Optional[Any]           =  drop_path_rate
        __UpperCAmelCase							:	int           =  layer_norm_eps
        __UpperCAmelCase							:	Dict           =  decoder_hidden_size
        __UpperCAmelCase							:	Tuple           =  kwargs.get("""reshape_last_stage"""		,      __lowercase					)
        __UpperCAmelCase							:	int           =  semantic_loss_ignore_index


class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Union[str, Any]							       =	version.parse('1.11'   )
      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]					)
      @property
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      float:
        return 1e-4




      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      int:
        return 12


