
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    'split_dict'    ,       [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'    ,       num_bytes=1_337    ,       num_examples=42    ,       dataset_name='my_dataset' )} ),
        SplitDict({'train': SplitInfo(name='train'    ,       num_bytes=1_337    ,       num_examples=42 )} ),
        SplitDict({'train': SplitInfo()} ),
    ]    ,       )
def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     split_dict._to_yaml_list()
		assert len(lowercase ) == len(lowercase )
		UpperCamelCase		=     SplitDict._from_yaml_list(lowercase )
		for split_name, split_info in split_dict.items():
				# dataset_name field is deprecated, and is therefore not part of the YAML dump
				UpperCamelCase		=     None
				# the split name of split_dict takes over the name of the split info object
				UpperCamelCase		=     split_name
		assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'    ,       [SplitInfo(), SplitInfo(dataset_name=lowercase ), SplitInfo(dataset_name='my_dataset' )] )
def       A       (					lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     asdict(SplitDict({'train': split_info} ) )
		assert "dataset_name" in split_dict_asdict["train"]
		assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name


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   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

						self.assertEqual(
						    A_				,      [
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						    ]				,      )



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

				UpperCamelCase		=     hf_hub_download(repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     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'}]				,      )

				UpperCamelCase		=     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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
