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_:					List[str]     =					"src/transformers"
lowerCAmelCase_:					Any     =					"docs/source/en/tasks"







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



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

     lowercase__				 =							start_index
     while not lines[end_index].startswith(A      ):
          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_:					Dict     =					{
    "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_:					Union[str, Any]     =					{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							TASK_GUIDE_TO_MODELS[task_guide]
     lowercase__				 =							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A    ,				set()      )
     lowercase__				 =							{
         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 					__a   (		A    ,				A=False      ):






     '''simple docstring'''



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

     lowercase__				 =							get_model_list_for_task(A      )

     if current_list != new_list:
          if overwrite:
               with open(os.path.join(A    ,				A      )    ,				"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_:					Tuple     =					argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCAmelCase_:					Dict     =					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"""


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							hf_hub_download(
            repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							VideoClassificationPipeline(model=_UpperCAmelCase,				image_processor=_UpperCAmelCase,				top_k=2  )
        lowercase__				 =							[
            example_video_filepath,
            "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
        ]
        return video_classifier, examples


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

             self.assertEqual(
                 _UpperCAmelCase,				[
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                 ],				)


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




