




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 ):

			"""simple docstring"""







			snake_case_   		=       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    hf_hub_download(
							    repo_id='nateraw/video-demo'			,						filename='archery.mp4'			,						repo_type='dataset'							)
							__lowerCamelCase		       =    VideoClassificationPipeline(model=lowerCamelCase__			,						image_processor=lowerCamelCase__			,						top_k=2							)
							__lowerCamelCase		       =    [
							    example_video_filepath,
							    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
							]
							return video_classifier, examples


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							for example in examples:
											__lowerCamelCase		       =    video_classifier(lowerCamelCase__							)

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


			@require_torch
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
							__lowerCamelCase		       =    VideoMAEFeatureExtractor(
							    size={'shortest_edge': 10}			,						crop_size={'height': 10, 'width': 10}							)
							__lowerCamelCase		       =    pipeline(
							    'video-classification'			,						model=lowerCamelCase__			,						feature_extractor=lowerCamelCase__			,						frame_sampling_rate=4							)

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

							__lowerCamelCase		       =    video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]			,						top_k=2			,						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__			,						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       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							pass




'''simple docstring'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase    :							str      =					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase    :							list[int]      =					[ord(letter) for letter in string.ascii_lowercase]
lowercase    :							set[int]      =					{ord(char) for char in VALID_CHARS}

lowercase    :							list[str]      =					["the", "be", "to", "of", "and", "in", "that", "have"]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str | None:
	_snake_case   						=		""
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42

	for keychar, cipherchar in zip(cycle(__A	)			,				__A	):
		_snake_case   						=		cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(__A	)

	return decoded







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	list[str]:
	_snake_case   						=		[]
	for key in product(__A			,				repeat=3	):
		_snake_case   						=		try_key(__A			,				__A	)
		if encoded is not None:
			possibles.append(__A	)
	return possibles







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	list[str]:
	return [possible for possible in possibles if common_word in possible.lower()]







def 				SCREAMING_SNAKE_CASE__	(   __A = "p059_cipher.txt"	)       ->	int:
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		Path(__A	).parent.joinpath(__A	).read_text(encoding='utf-8'	)

	_snake_case   						=		[int(__A	) for number in data.strip().split(','	)]

	_snake_case   						=		filter_valid_chars(__A	)
	for common_word in COMMON_WORDS:
		_snake_case   						=		filter_common_word(__A			,				__A	)
		if len(__A	) == 1:
			break

	_snake_case   						=		possibles[0]
	return sum(ord(__A	) for char in decoded_text	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

