




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 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Dict			 =					hf_hub_download(
               repo_id="nateraw/video-demo"     ,  filename="archery.mp4"     ,  repo_type="dataset"     )
           lowerCamelCase   :      str			 =					VideoClassificationPipeline(model=UpperCamelCase__     ,  image_processor=UpperCamelCase__     ,  top_k=2     )
           lowerCamelCase   :      Dict			 =					[
               example_video_filepath,
               "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
           ]
           return video_classifier, examples







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           for example in examples:
                  lowerCamelCase   :      Optional[int]			 =					video_classifier(UpperCamelCase__     )

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







    @require_torch
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      int			 =					"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
           lowerCamelCase   :      Any			 =					VideoMAEFeatureExtractor(
               size={"shortest_edge": 10}     ,  crop_size={"height": 10, "width": 10}     )
           lowerCamelCase   :      Union[str, Any]			 =					pipeline(
               "video-classification"     ,  model=UpperCamelCase__     ,  feature_extractor=UpperCamelCase__     ,  frame_sampling_rate=4     )

           lowerCamelCase   :      Optional[Any]			 =					hf_hub_download(repo_id="nateraw/video-demo"     ,  filename="archery.mp4"     ,  repo_type="dataset"     )
           lowerCamelCase   :      Union[str, Any]			 =					video_classifier(UpperCamelCase__     ,  top_k=2     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=4     )     ,  [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]     ,  )

           lowerCamelCase   :      List[str]			 =					video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]     ,  top_k=2     ,  )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=4     )     ,  [
                   [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
                   [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
               ]     ,  )







    @require_tf
    def     _lowercase     (    self     )     ->      int:
           pass












import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      List[Any]  =   'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__       :      Tuple  =   re.compile(r'\s*\(\s*"(\S[^"]+)"')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> int:
       with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      List[Any]			 =					f.read()

       lowerCamelCase   :      str			 =					content.split("\n"    )
       lowerCamelCase   :      int			 =					[]
       lowerCamelCase   :      List[Any]			 =					0
       while line_idx < len(_SCREAMING_SNAKE_CASE    ):
              if _re_intro_mapping.search(lines[line_idx]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					len(re.search(r"^(\s*)\S" ,lines[line_idx]    ).groups()[0]    ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("    ):
                            new_lines.append(lines[line_idx]    )
                            line_idx += 1

                     lowerCamelCase   :      Optional[int]			 =					[]
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   lowerCamelCase   :      List[str]			 =					line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"    ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]    )    )
                            else:
                                   blocks.append(lines[line_idx]    )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     lowerCamelCase   :      Union[str, Any]			 =					sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : _re_identifier.search(_SCREAMING_SNAKE_CASE    ).groups()[0]    )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]    )
                     line_idx += 1

       if overwrite:
              with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                     f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )
       elif "\n".join(_SCREAMING_SNAKE_CASE    ) != content:
              return True

def      A      (      _SCREAMING_SNAKE_CASE = False    )	-> List[str]:
       lowerCamelCase   :      str			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) for f in os.listdir(_SCREAMING_SNAKE_CASE    ) if f.endswith(".py"    )]
       lowerCamelCase   :      Union[str, Any]			 =					[sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE    ) for fname in fnames]

       if not overwrite and any(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      str			 =					[f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) if d]
              raise ValueError(
                  f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE    )}. Run `make style` to fix'''
                  " this."    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)






