





"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__				:Tuple				   =       5_0_0_0_0
lowercase__				:str				   =       5_0_0_0

lowercase__   ,						lowercase__				:Optional[int]				   =       os.path.split(__file__)
lowercase__				:Optional[Any]				   =       os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  for i in range(UpperCAmelCase_  ):
    __UpperCAmelCase    :						int       =						dataset[i]


@get_duration
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  for i in range(0  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Dict       =						dataset[i : i + batch_size]


@get_duration
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  with dataset.formatted_as(type=UpperCAmelCase_  ):
    for i in range(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[str]       =						dataset[i]


@get_duration
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  with dataset.formatted_as(type=UpperCAmelCase_  ):
    for i in range(0  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						dataset[i : i + batch_size]


def       lowerCamelCase_       (   )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{'''num examples''': SPEED_TEST_N_EXAMPLES}
  __UpperCAmelCase    :						Dict       =						[
      (read, {'''length''': SMALL_TEST}),
      (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
      (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
      (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
      (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
      (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
      (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
      (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
  ]

  __UpperCAmelCase    :						Optional[int]       =						[
      (read, {'''length''': SMALL_TEST}),
      (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_00}),
      (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10_00}),
      (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
      (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
      (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10_00}),
  ]
  with tempfile.TemporaryDirectory() as tmp_dir:
    print('''generating dataset'''  )
    __UpperCAmelCase    :						Optional[int]       =						datasets.Features(
        {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
    __UpperCAmelCase    :						Tuple       =						generate_example_dataset(
        os.path.join(UpperCAmelCase_  ,			'''dataset.arrow'''  )  ,			UpperCAmelCase_  ,			num_examples=UpperCAmelCase_  ,			seq_shapes={'''list''': (1_00,)}  ,			)
    print('''first set of iterations'''  )
    for func, kwargs in functions:
      print(func.__name__  ,			str(UpperCAmelCase_  )  )
      __UpperCAmelCase    :						Dict       =						func(UpperCAmelCase_  ,			**UpperCAmelCase_  )

    print('''shuffling dataset'''  )
    __UpperCAmelCase    :						Optional[int]       =						dataset.shuffle()
    print('''Second set of iterations (after shuffling'''  )
    for func, kwargs in functions_shuffled:
      print('''shuffled '''  ,			func.__name__  ,			str(UpperCAmelCase_  )  )
      __UpperCAmelCase    :						Union[str, Any]       =						func(
          UpperCAmelCase_  ,			**UpperCAmelCase_  )

  with open(UpperCAmelCase_  ,			'''wb'''  ) as f:
    f.write(json.dumps(UpperCAmelCase_  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()






"""simple docstring"""





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__				:int				   =       'src/transformers'
lowercase__				:List[str]				   =       'docs/source/en/tasks'


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


  with open(UpperCAmelCase_  ,			'''r'''  ,			encoding='''utf-8'''  ,			newline='''\n'''  ) as f:
    __UpperCAmelCase    :						Union[str, Any]       =						f.readlines()
  # Find the start prompt.
  __UpperCAmelCase    :						Any       =						0
  while not lines[start_index].startswith(UpperCAmelCase_  ):
    start_index += 1
  start_index += 1

  __UpperCAmelCase    :						Optional[Any]       =						start_index
  while not lines[end_index].startswith(UpperCAmelCase_  ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]  ) <= 1:
    start_index += 1
  while len(lines[end_index]  ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase__				:Any				   =       direct_transformers_import(TRANSFORMERS_PATH)

lowercase__				:List[Any]				   =       {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase__				:Union[str, Any]				   =       {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}


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





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						TASK_GUIDE_TO_MODELS[task_guide]
  __UpperCAmelCase    :						Dict       =						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_  ,			set()  )
  __UpperCAmelCase    :						List[Any]       =						{
      code: name
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if (code in model_maping_names or code in special_model_types)
  }
  return ", ".join([f'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]  ) + "\n"


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						_find_text_in_file(
      filename=os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''  ,			end_prompt='''<!--End of the generated tip-->'''  ,			)

  __UpperCAmelCase    :						List[str]       =						get_model_list_for_task(UpperCAmelCase_  )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			'''w'''  ,			encoding='''utf-8'''  ,			newline='''\n'''  ) as f:
        f.writelines(lines[:start_index] + [new_list] + lines[end_index:]  )
    else:
      raise ValueError(
          f'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
          ''' to fix this.'''  )


if __name__ == "__main__":
  lowercase__				:int				   =       argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

  for task_guide in TASK_GUIDE_TO_MODELS.keys():
    check_model_list_for_task(task_guide, args.fix_and_overwrite)