





"""simple docstring"""





from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests


def       a__					(					_SCREAMING_SNAKE_CASE = "isbn/0140328726"    ):




		"""simple docstring"""


		UpperCamelCase   =	olid.strip().strip("/"    )  # Remove leading/trailing whitespace & slashes
		if new_olid.count("/"    ) != 1:
				UpperCamelCase   =	F"{olid} is not a valid Open Library olid"
				raise ValueError(_SCREAMING_SNAKE_CASE    )
		return requests.get(F"https://openlibrary.org/{new_olid}.json"    ).json()


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	{
		    "title": "Title",
		    "publish_date": "Publish date",
		    "authors": "Authors",
		    "number_of_pages": "Number of pages:",
		    "first_sentence": "First sentence",
		    "isbn_10": "ISBN (10)",
		    "isbn_13": "ISBN (13)",
		}
		UpperCamelCase   =	{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
		UpperCamelCase   =	[
		    get_openlibrary_data(author["key"]    )["name"] for author in data["Authors"]
		]
		UpperCamelCase   =	data["First sentence"]["value"]
		for key, value in data.items():
				if isinstance(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	", ".join(_SCREAMING_SNAKE_CASE    )
		return data


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						while True:
												lowerCAmelCase__     			=			input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
												if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																		break

												if len(isbn) not in (10, 13) or not isbn.isdigit():
																		print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
																		continue

												print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

												try:
																		lowerCAmelCase__     			=			summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
																		print('''\n'''.join(f'''{key}: {value}''' for key, value in book_summary.items()))
												except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																		print(f'''Sorry, there are no results for ISBN: {isbn}.''')













"""simple docstring"""





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_ =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   str:
								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     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								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     snake_case_   (self		) ->   int:
								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     snake_case_   (self		) ->   Optional[Any]:
								pass






