




"""simple docstring"""






import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:






			class lowerCAmelCase__			:






						'''simple docstring'''



						@staticmethod
						def       A_					(						*lowercase						,   **lowercase						):
										pass






@is_pipeline_test
@require_vision
class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   )
							_lowerCamelCase  :    List[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    List[str]					=					image_classifier(lowercase						,   candidate_labels=['a', 'b', 'c']						)

							# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
							# python and torch versions.
							self.assertIn(
							    nested_simplify(lowercase						)						,   [
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
							    ]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   framework='tf'						)
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[int]					=					image_classifier(lowercase						,   candidate_labels=['a', 'b', 'c']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@slow
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					pipeline(
							    task='zero-shot-image-classification'						,   model='openai/clip-vit-base-patch32'						,   )
							# This is an image of 2 cats with remotes and no planes
							_lowerCamelCase  :    Optional[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[Any]					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )






			@slow
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					pipeline(
							    task='zero-shot-image-classification'						,   model='openai/clip-vit-base-patch32'						,   framework='tf'						)
							# This is an image of 2 cats with remotes and no planes
							_lowerCamelCase  :    List[str]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Tuple					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )





"""simple docstring"""






from pathlib import Path

import fire







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					Path(lowercase__	)
				_lowerCamelCase  :    List[Any]					=					Path(lowercase__	)
				dest_dir.mkdir(exist_ok=lowercase__	)
				for path in src_dir.iterdir():
								_lowerCamelCase  :    str					=					[x.rstrip() for x in list(path.open().readlines()	)][:n]
								_lowerCamelCase  :    Any					=					dest_dir.joinpath(path.name	)
								print(lowercase__	)
								dest_path.open('w'	).write('\n'.join(lowercase__	)	)


if __name__ == "__main__":
			fire.Fire(minify)