



'''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
lowerCAmelCase:					Union[str, Any]						       =						'''src/transformers'''
lowerCAmelCase:					str						       =						'''docs/source/en/tasks'''
def       lowerCamelCase__       (						_A ,			_A ,			_A				):






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

  a						:					Dict        =     start_index
  while not lines[end_index].startswith(__lowercase				):
    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.
lowerCAmelCase:					Dict						       =						direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase:					Optional[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`).
lowerCAmelCase:					Dict						       =						{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}
def       lowerCamelCase__       (						_A				):
  a						:					Optional[Any]        =     TASK_GUIDE_TO_MODELS[task_guide]
  a						:					List[Any]        =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowercase ,			set()				)
  a						:					int        =     {
      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__       (						_A ,			_A=False				):
  a   ,	a   ,	a   ,	a						:					List[str]        =     _find_text_in_file(
      filename=os.path.join(__lowercase ,			__lowercase				) ,			start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->' ,			end_prompt='<!--End of the generated tip-->' ,			)

  a						:					List[Any]        =     get_model_list_for_task(__lowercase				)

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(__lowercase ,			__lowercase				) ,			'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__":
     lowerCAmelCase:					str						       =						argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowerCAmelCase:					Optional[int]						       =						parser.parse_args()

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






'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
