






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   __lowerCAmelCase			(					unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Optional[Any]:
      """simple docstring"""





      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 	_snake_case ( self					,			snake_case					,			snake_case      )				->  str:
      """simple docstring"""





      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 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      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": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "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": 0.5_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      )				->  Dict:
      """simple docstring"""





      pass








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

