




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 		lowercase							(     unittest.TestCase	):
     _a											=		MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							Optional[Any]:
           _A							:  Union[str, Any]    =		hf_hub_download(
               repo_id="""nateraw/video-demo"""	,  filename="""archery.mp4"""	,  repo_type="""dataset"""       )
           _A							:  int    =		VideoClassificationPipeline(model=_a	,  image_processor=_a	,  top_k=2       )
           _A							:  Dict    =		[
               example_video_filepath,
               """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
           ]
           return video_classifier, examples

     def       a__			(						self	,  _a	,  _a       )		->							str:
           for example in examples:
                 _A							:  Dict    =		video_classifier(_a       )

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

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

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

           _A							:  Optional[Any]    =		video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]	,  top_k=2	,  )
           self.assertEqual(
               nested_simplify(_a	,  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       a__			(						self       )		->							Dict:
           pass







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

