import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax

  from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Tuple		,	_lowercase       :	List[Any]		,	_lowercase       :	Dict=13		,	_lowercase       :	Any=30		,	_lowercase       :	str=2		,	_lowercase       :	List[Any]=3		,	_lowercase       :	Tuple=True		,	_lowercase       :	Tuple=True		,	_lowercase       :	Any=32		,	_lowercase       :	int=5		,	_lowercase       :	str=4		,	_lowercase       :	Tuple=37		,	_lowercase       :	Union[str, Any]="gelu"		,	_lowercase       :	List[Any]=0.1		,	_lowercase       :	Tuple=0.1		,	_lowercase       :	int=10		,	_lowercase       :	List[str]=0.02		,	):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			parent
    _UpperCamelCase:  Optional[int]    						=			batch_size
    _UpperCamelCase:  List[str]    						=			image_size
    _UpperCamelCase:  int    						=			patch_size
    _UpperCamelCase:  List[str]    						=			num_channels
    _UpperCamelCase:  Optional[Any]    						=			is_training
    _UpperCamelCase:  List[Any]    						=			use_labels
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  Union[str, Any]    						=			num_hidden_layers
    _UpperCamelCase:  Any    						=			num_attention_heads
    _UpperCamelCase:  Optional[Any]    						=			intermediate_size
    _UpperCamelCase:  Optional[Any]    						=			hidden_act
    _UpperCamelCase:  str    						=			hidden_dropout_prob
    _UpperCamelCase:  Optional[Any]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  int    						=			type_sequence_label_size
    _UpperCamelCase:  Optional[Any]    						=			initializer_range

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    _UpperCamelCase:  str    						=			(image_size // patch_size) ** 2
    _UpperCamelCase:  Any    						=			num_patches + 1



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

    _UpperCamelCase:  Dict    						=			ViTConfig(
        image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, pixel_values



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxViTModel(config=_lowercase				)
    _UpperCamelCase:  Dict    						=			model(_lowercase				)
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    _UpperCamelCase:  Tuple    						=			(self.image_size, self.image_size)
    _UpperCamelCase:  Union[str, Any]    						=			(self.patch_size, self.patch_size)
    _UpperCamelCase:  Optional[int]    						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, num_patches + 1, self.hidden_size)				)



   def 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	int		,	_lowercase       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.type_sequence_label_size
    _UpperCamelCase:  Dict    						=			FlaxViTForImageClassification(config=_lowercase				)
    _UpperCamelCase:  Dict    						=			model(_lowercase				)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)				)

    # test greyscale images
    _UpperCamelCase:  Dict    						=			1
    _UpperCamelCase:  Optional[Any]    						=			FlaxViTForImageClassification(_lowercase				)

    _UpperCamelCase:  Optional[Any]    						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    _UpperCamelCase:  Optional[Any]    						=			model(_lowercase				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.prepare_config_and_inputs()
    (
        (
        _UpperCamelCase
    )						,				(
        _UpperCamelCase
    )						,				
    ):  Optional[int]    						=			config_and_inputs
    _UpperCamelCase:  int    						=			{'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxViTModelTester(self				)
    _UpperCamelCase:  Any    						=			ConfigTester(self		,	config_class=_lowercase		,	has_text_modality=_lowercase		,	hidden_size=37				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowercase				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_lowercase				)



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _UpperCamelCase:  Dict    						=			model_class(_lowercase				)
     _UpperCamelCase:  Tuple    						=			inspect.signature(model.__call__				)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _UpperCamelCase:  int    						=			[*signature.parameters.keys()]

     _UpperCamelCase:  List[Any]    						=			['''pixel_values''']
     self.assertListEqual(arg_names[:1]		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__				):
      _UpperCamelCase:  Union[str, Any]    						=			self._prepare_for_class(_lowercase		,	_lowercase				)
      _UpperCamelCase:  Optional[Any]    						=			model_class(_lowercase				)

      @jax.jit
      def model_jitted(_lowercase       :	int		,	**_lowercase       :	Union[str, Any]				):
       return model(pixel_values=_lowercase		,	**_lowercase				)

      with self.subTest('''JIT Enabled'''				):
       _UpperCamelCase:  Tuple    						=			model_jitted(**_lowercase				).to_tuple()

      with self.subTest('''JIT Disabled'''				):
       with jax.disable_jit():
        _UpperCamelCase:  Union[str, Any]    						=			model_jitted(**_lowercase				).to_tuple()

      self.assertEqual(len(_lowercase				)		,	len(_lowercase				)				)
      for jitted_output, output in zip(_lowercase		,	_lowercase				):
       self.assertEqual(jitted_output.shape		,	output.shape				)



   @slow
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  Union[str, Any]    						=			model_class_name.from_pretrained('''google/vit-base-patch16-224'''				)
     _UpperCamelCase:  Optional[int]    						=			model(np.ones((1, 3, 224, 224)				)				)
     self.assertIsNotNone(_lowercase				)
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_           =				logging.getLogger(__name__)




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
 parser.add_argument(
     '''--dataset_name'''	,       type=lowercase	,       default='''wikitext'''	,       help='''Name of the training. Explore datasets at: hf.co/datasets.'''	,       )
 parser.add_argument(
     '''--dataset_config'''	,       type=lowercase	,       default='''wikitext-103-raw-v1'''	,       help='''Configuration name of the dataset.'''		)
 parser.add_argument(
     '''--tokenizer_name_or_path'''	,       type=lowercase	,       default='''sayakpaul/unigram-tokenizer-wikitext'''	,       help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''	,       )
 parser.add_argument(
     '''--shard_size'''	,       type=lowercase	,       default=1_000	,       help='''Number of entries to go in a single shard.'''	,       )
 parser.add_argument('''--split'''	,       type=lowercase	,       default='''train'''	,       choices=['''train''', '''test''', '''validation''']		)
 parser.add_argument(
     '''--limit'''	,       default=lowercase	,       type=lowercase	,       help='''Limit the number of shards (used for debugging).'''	,       )
 parser.add_argument(
     '''--max_length'''	,       type=lowercase	,       default=512	,       help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
     ''' sequence length that is a multiple of 8.'''	,       )
 parser.add_argument(
     '''--output_dir'''	,       default='''tf-tpu'''	,       type=lowercase	,       help='''Output directory where the TFRecord shards will be saved. If the'''
     ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
     ''' shards will be directly saved to a Google Cloud Storage bucket.'''	,       )

 _UpperCamelCase:  str    						=			parser.parse_args()
 return args




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 def fn(lowercase:				List[Any]		):
  return tokenizer(examples['''text''']		)

 return fn




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:  Tuple    						=			{
      '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]		)		),
      '''attention_mask''': tf.train.Feature(
          intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]		)		),
  }
  _UpperCamelCase:  Optional[int]    						=			tf.train.Features(feature=lowercase		)
  _UpperCamelCase:  Optional[int]    						=			tf.train.Example(features=lowercase		)
  _UpperCamelCase:  List[Any]    						=			example.SerializeToString()
  records.append(lowercase		)
 return records




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			datasets.load_dataset(args.dataset_name	,       args.dataset_config	,       split=args.split		)

 if args.limit is not None:
  _UpperCamelCase:  str    						=			min(len(lowercase		)	,       args.limit		)
  _UpperCamelCase:  Optional[Any]    						=			dataset.select(range(lowercase		)		)
  print(F"""Limiting the dataset to {args.limit} entries."""		)

 _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained(args.tokenizer_name_or_path		)

 # Handle output directory creation.
 # For serializing into a Google Cloud Storage Bucket, one needs to first
 # create a bucket.
 if "gs" not in args.output_dir:
  if not os.path.exists(args.output_dir		):
   os.makedirs(args.output_dir		)
  _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       args.split		)
  if not os.path.exists(lowercase		):
   os.makedirs(lowercase		)
 else:
  _UpperCamelCase:  str    						=			os.path.join(args.output_dir	,       args.split		)

 # Tokenize the whole dataset at once.
 _UpperCamelCase:  Union[str, Any]    						=			tokenize_function(lowercase		)
 _UpperCamelCase:  List[Any]    						=			dataset.map(lowercase	,       batched=lowercase	,       num_proc=4	,       remove_columns=['''text''']		)

 # We need to concatenate all our texts together, and then split the result
 # into chunks of a fixed size, which we will call block_size. To do this, we
 # will use the map method again, with the option batched=True. When we use batched=True,
 # the function we pass to map() will be passed multiple inputs at once, allowing us
 # to group them into more or fewer examples than we had in the input.
 # This allows us to create our new fixed-length samples. The advantage of this
 # method is that we don't lose a whole lot of content from the dataset compared to the
 # case where we simply tokenize with a pre-defined max_length.

 def group_texts(lowercase:				int		):
  # Concatenate all texts.
  _UpperCamelCase:  List[str]    						=			{k: sum(examples[k]	,       []		) for k in examples.keys()}
  _UpperCamelCase:  Any    						=			len(concatenated_examples[list(examples.keys()		)[0]]		)
  # We drop the small remainder, though you could add padding instead if the model supports it
  # In this, as in all things, we advise you to follow your heart 🫀
  _UpperCamelCase:  Optional[int]    						=			(total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  _UpperCamelCase:  Tuple    						=			{
      k: [t[i : i + args.max_length] for i in range(0	,       lowercase	,       args.max_length		)]
      for k, t in concatenated_examples.items()
  }
  return result

 _UpperCamelCase:  Dict    						=			dataset_tokenized.map(lowercase	,       batched=lowercase	,       batch_size=1_000	,       num_proc=4		)

 _UpperCamelCase:  List[str]    						=			0
 _UpperCamelCase:  Tuple    						=			0
 for shard in range(0	,       len(lowercase		)	,       args.shard_size		):
  _UpperCamelCase:  List[str]    						=			grouped_dataset[shard : shard + args.shard_size]
  _UpperCamelCase:  Optional[Any]    						=			len(dataset_snapshot['''input_ids''']		)
  _UpperCamelCase:  str    						=			os.path.join(lowercase	,       F"""dataset-{shard_count}-{records_containing}.tfrecord"""		)
  _UpperCamelCase:  Any    						=			get_serialized_examples(lowercase		)

  with tf.io.TFRecordWriter(lowercase		) as out_file:
   for i in range(len(lowercase		)		):
    _UpperCamelCase:  List[Any]    						=			serialized_examples[i]
    out_file.write(lowercase		)
   print('''Wrote file {} containing {} records'''.format(lowercase	,       lowercase		)		)

  shard_count += 1
  total_records += records_containing

 with open(F"""split-{args.split}-records-count.txt"""	,       '''w'''		) as f:
  print(F"""Total {args.split} records: {total_records}"""	,       file=lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_           =				parse_args()
  main(args)