




"""simple docstring"""






from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def   _snake_case    (       lowercase__	):
				return getitem, k







def   _snake_case    (       lowercase__							,					lowercase__	):
				return setitem, k, v







def   _snake_case    (       lowercase__	):
				return delitem, k







def   _snake_case    (       lowercase__							,					lowercase__							,					*lowercase__	):
				try:
								return fun(SCREAMING_SNAKE_CASE_							,					*SCREAMING_SNAKE_CASE_	), None
				except Exception as e:
								return None, e


lowercase__  		=		(
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

lowercase__  		=		[
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

lowercase__  		=		[
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

lowercase__  		=		[
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

lowercase__  		=		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

lowercase__  		=		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]







@pytest.mark.parametrize(
    'operations'							,					(
        pytest.param(_add_items							,					id='add items'	),
        pytest.param(_overwrite_items							,					id='overwrite items'	),
        pytest.param(_delete_items							,					id='delete items'	),
        pytest.param(_access_absent_items							,					id='access absent items'	),
        pytest.param(_add_with_resize_up							,					id='add with resize up'	),
        pytest.param(_add_with_resize_down							,					id='add with resize down'	),
    )							,					)
def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					HashMap(initial_block_size=4	)
				_lowerCamelCase  :    Optional[int]					=					{}
				for _, (fun, *args) in enumerate(SCREAMING_SNAKE_CASE_	):
								_lowerCamelCase  :    int					=					_run_operation(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					*SCREAMING_SNAKE_CASE_	)
								_lowerCamelCase  :    str					=					_run_operation(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					*SCREAMING_SNAKE_CASE_	)
								assert my_res == py_res
								assert str(SCREAMING_SNAKE_CASE_	) == str(SCREAMING_SNAKE_CASE_	)
								assert set(SCREAMING_SNAKE_CASE_	) == set(SCREAMING_SNAKE_CASE_	)
								assert len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)
								assert set(my.items()	) == set(py.items()	)







def   _snake_case    (       ):
				def is_public(lowercase__	) -> bool:
								return not name.startswith('_'	)

				_lowerCamelCase  :    Any					=					{name for name in dir({}	) if is_public(SCREAMING_SNAKE_CASE_	)}
				_lowerCamelCase  :    Union[str, Any]					=					{name for name in dir(HashMap()	) if is_public(SCREAMING_SNAKE_CASE_	)}

				assert dict_public_names > hash_public_names





"""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						,   )