
'''simple docstring'''

import argparse
import logging
import os
import re

import tensorflow as tf

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


__snake_case:  str								=			logging.getLogger(__name__)

__snake_case:  int								=			tf.data.AUTOTUNE






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Optional[Any]      =							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=512     ,		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_      :	str      =							parser.parse_args()
  return args






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  try:
    if args.tpu_name:
      a_      :	List[Any]      =							tf.distribute.cluster_resolver.TPUClusterResolver(
          args.tpu_name     ,		zone=args.tpu_zone     ,		project=args.gcp_project   )
    else:
      a_      :	int      =							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 	_snake_case						(      A_       :    Union[str, Any]   ):
  """simple docstring"""







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

  return num_samples






def 	_snake_case						(      A_       :    Any     ,		A_       :    Optional[int]     ,		A_       :    List[str]     ,		A_       :    int     ,		A_       :    int     ,		A_       :    Optional[Any]=None   ):
  """simple docstring"""







  a_      :	Dict      =							count_samples(A_   )
  a_      :	Union[str, Any]      =							tf.data.Dataset.from_tensor_slices(A_   )
  if shuffle:
    a_      :	Tuple      =							dataset.shuffle(len(A_   )   )
  a_      :	int      =							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_      :	Dict      =							dataset.apply(tf.data.experimental.assert_cardinality(A_   )   )
  a_      :	Optional[Any]      =							dataset.map(A_     ,		num_parallel_calls=A_   )
  if shuffle:
    assert shuffle_buffer_size is not None
    a_      :	List[str]      =							dataset.shuffle(args.shuffle_buffer_size   )
  a_      :	Optional[Any]      =							dataset.batch(A_     ,		drop_remainder=A_   )
  a_      :	Dict      =							dataset.map(A_     ,		num_parallel_calls=A_   )
  a_      :	List[str]      =							dataset.prefetch(A_   )
  return dataset






def 	_snake_case						(      A_       :    Any   ):
  """simple docstring"""







  if not args.no_tpu:
    a_      :	str      =							initialize_tpu(A_   )
    a_      :	Optional[int]      =							tf.distribute.TPUStrategy(A_   )
  else:
    a_      :	Any      =							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_      :	Any      =							AutoConfig.from_pretrained(args.pretrained_model_config   )
  a_      :	Optional[int]      =							tokenizer.vocab_size

  a_      :	Dict      =							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_      :	List[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_      :	str      =							count_samples(A_   )

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

  with strategy.scope():
    a_      :	List[str]      =							TFAutoModelForMaskedLM.from_config(A_   )
    model(model.dummy_inputs   )  # Pass some dummy inputs through the model to ensure all the weights are built
    a_      , a_      :	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_       :    Any   ):
    a_      :	Tuple      =							{
        """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_      :	Union[str, Any]      =							DataCollatorForLanguageModeling(
      tokenizer=A_     ,		mlm_probability=args.mlm_probability     ,		mlm=A_     ,		return_tensors="""tf"""   )

  def mask_with_collator(A_       :    Union[str, Any]   ):
    # TF really needs an isin() function
    a_      :	List[Any]      =							(
        ~tf.cast(batch["""attention_mask"""]     ,		tf.bool   )
        | (batch["""input_ids"""] == tokenizer.cls_token_id)
        | (batch["""input_ids"""] == tokenizer.sep_token_id)
    )
    a_      , a_      :	Dict      =							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_      :	List[Any]      =							args.per_replica_batch_size * strategy.num_replicas_in_sync

  a_      :	int      =							prepare_dataset(
      A_     ,		decode_fn=A_     ,		mask_fn=A_     ,		batch_size=A_     ,		shuffle=A_     ,		shuffle_buffer_size=args.shuffle_buffer_size     ,		)

  a_      :	Tuple      =							prepare_dataset(
      A_     ,		decode_fn=A_     ,		mask_fn=A_     ,		batch_size=A_     ,		shuffle=A_     ,		)

  a_      :	List[Any]      =							[]
  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__":
    __snake_case:  int								=			parse_args()
    main(args)




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=7							,		lowerCAmelCase_=3							,		lowerCAmelCase_=18							,		lowerCAmelCase_=30							,		lowerCAmelCase_=4_00							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	Any      =							num_channels
       a_      :	Tuple      =							image_size
       a_      :	Any      =							min_resolution
       a_      :	Dict      =							max_resolution
       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size if size is not None else {"""height""": 18, """width""": 20}
       a_      :	int      =							do_thumbnail
       a_      :	Optional[int]      =							do_align_axis
       a_      :	Tuple      =							do_pad
       a_      :	int      =							do_normalize
       a_      :	Tuple      =							image_mean
       a_      :	Optional[Any]      =							image_std






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DonutImageProcessor if is_vision_available() else None


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DonutImageProcessingTester(self )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_resize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""size""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_thumbnail""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_align_long_axis""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_pad""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_normalize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_mean""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_std""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,		{"""height""": 18, """width""": 20} )

       a_      :	str      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=42 )
       self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42} )

       # Previous config had dimensions in (width, height) order
       a_      :	Any      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=(42, 84) )
       self.assertEqual(image_processor.size							,		{"""height""": 84, """width""": 42} )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       a_      :	str      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		Image.Image )

       # Test not batched input
       a_      :	Dict      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	int      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a_      :	int      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		numpify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       # Test not batched input
       a_      :	Optional[Any]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Dict      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)






     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a_      :	Optional[Any]      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		torchify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )

       # Test not batched input
       a_      :	Optional[int]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Any      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


