

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 	A_		(	unittest.TestCase    ):
				_UpperCAmelCase   :  Optional[int]										=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
				def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str):
					__lowerCamelCase    :    List[Any] 	=			hf_hub_download(
					    repo_id='nateraw/video-demo'       ,filename='archery.mp4'       ,repo_type='dataset')
					__lowerCamelCase    :    str 	=			VideoClassificationPipeline(model=SCREAMING_SNAKE_CASE__       ,image_processor=SCREAMING_SNAKE_CASE__       ,top_k=2)
					__lowerCamelCase    :    Optional[Any] 	=			[
					    example_video_filepath,
					    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
					]
					return video_classifier, examples
				def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]):
					for example in examples:
						__lowerCamelCase    :    str 	=			video_classifier(SCREAMING_SNAKE_CASE__)

						self.assertEqual(
						    SCREAMING_SNAKE_CASE__       ,[
						        {'score': ANY(SCREAMING_SNAKE_CASE__), 'label': ANY(SCREAMING_SNAKE_CASE__)},
						        {'score': ANY(SCREAMING_SNAKE_CASE__), 'label': ANY(SCREAMING_SNAKE_CASE__)},
						    ]       ,)
				@require_torch
				def    lowerCAmelCase					(		self		:      Optional[int]):
					__lowerCamelCase    :    Tuple 	=			"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
					__lowerCamelCase    :    Dict 	=			VideoMAEFeatureExtractor(
					    size={'shortest_edge': 1_0}       ,crop_size={'height': 1_0, 'width': 1_0})
					__lowerCamelCase    :    int 	=			pipeline(
					    'video-classification'       ,model=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,frame_sampling_rate=4)

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

					__lowerCamelCase    :    Union[str, Any] 	=			video_classifier(
					    [
					        video_file_path,
					        video_file_path,
					    ]       ,top_k=2       ,)
					self.assertEqual(
					    nested_simplify(SCREAMING_SNAKE_CASE__       ,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    lowerCAmelCase					(		self		:      Union[str, Any]):
					pass







from typing import TYPE_CHECKING

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


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Mask2FormerForUniversalSegmentation""",
					    """Mask2FormerModel""",
					    """Mask2FormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




