
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase  :					Union[str, Any]						    =							{"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[Any]						    =							["""YolosFeatureExtractor"""]
  lowercase  :					Optional[int]						    =							["""YolosImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					int						    =							[
      """YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """YolosForObjectDetection""",
      """YolosModel""",
      """YolosPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  lowercase  :					Optional[int]						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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 __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      video_classifier(snake_case      )

                  self.assertEqual(
                      snake_case		,[
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                      ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

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

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
