
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'''





	snake_case_          =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






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






	def 							UpperCamelCase_    (			self   :					str				,A   :					Union[str, Any]				,A   :					Dict					):
					for example in examples:
									__A       =					video_classifier(A					)

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






	@require_torch
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
					__A       =					VideoMAEFeatureExtractor(
					    size={"shortest_edge": 10}				,crop_size={"height": 10, "width": 10}					)
					__A       =					pipeline(
					    "video-classification"				,model=A				,feature_extractor=A				,frame_sampling_rate=4					)

					__A       =					hf_hub_download(repo_id="nateraw/video-demo"				,filename="archery.mp4"				,repo_type="dataset"					)
					__A       =					video_classifier(A				,top_k=2					)
					self.assertEqual(
					    nested_simplify(A				,decimals=4					)				,[{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}]				,)

					__A       =					video_classifier(
					    [
					        video_file_path,
					        video_file_path,
					    ]				,top_k=2				,)
					self.assertEqual(
					    nested_simplify(A				,decimals=4					)				,[
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					        [{"score": 0.51_99, "label": "LABEL_0"}, {"score": 0.48_01, "label": "LABEL_1"}],
					    ]				,)







	@require_tf
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					pass







"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__       :					Tuple						=			{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MraForMaskedLM""",
		    """MraForMultipleChoice""",
		    """MraForQuestionAnswering""",
		    """MraForSequenceClassification""",
		    """MraForTokenClassification""",
		    """MraLayer""",
		    """MraModel""",
		    """MraPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		__magic_name__       :					Any						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



