

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   lowercase_   :



							@staticmethod
							def 						UpperCamelCase_		(      *A__				:    int      , **A__				:    Union[str, Any]		)   ->					Optional[int]:
									pass



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



						@require_torch
						def 						UpperCamelCase_		(      self				:    str		)   ->					int:
								_snake_case						=							pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''      , )
								_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
								_snake_case						=							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''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
								        [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
								    ]      , )

								_snake_case						=							image_classifier([image] * 5      , candidate_labels=['''A''', '''B''', '''C''']      , batch_size=2		)
								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								    ]      , )



						@require_tf
						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Optional[Any]:
								_snake_case						=							pipeline(
								    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''      , framework='''tf'''		)
								_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
								_snake_case						=							image_classifier(_lowercase      , candidate_labels=['''a''', '''b''', '''c''']		)

								self.assertEqual(
								    nested_simplify(_lowercase		)      , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]      , )

								_snake_case						=							image_classifier([image] * 5      , candidate_labels=['''A''', '''B''', '''C''']      , batch_size=2		)
								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								        [
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								            {'''score''': 0.333, '''label''': ANY(_lowercase		)},
								        ],
								    ]      , )



						@slow
						@require_torch
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
								_snake_case						=							pipeline(
								    task='''zero-shot-image-classification'''      , model='''openai/clip-vit-base-patch32'''      , )
								# This is an image of 2 cats with remotes and no planes
								_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
								_snake_case						=							image_classifier(_lowercase      , candidate_labels=['''cat''', '''plane''', '''remote''']		)

								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        {'''score''': 0.511, '''label''': '''remote'''},
								        {'''score''': 0.485, '''label''': '''cat'''},
								        {'''score''': 0.004, '''label''': '''plane'''},
								    ]      , )

								_snake_case						=							image_classifier([image] * 5      , candidate_labels=['''cat''', '''plane''', '''remote''']      , batch_size=2		)
								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        [
								            {'''score''': 0.511, '''label''': '''remote'''},
								            {'''score''': 0.485, '''label''': '''cat'''},
								            {'''score''': 0.004, '''label''': '''plane'''},
								        ],
								    ]
								    * 5      , )







						@slow
						@require_tf
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
								_snake_case						=							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
								_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
								_snake_case						=							image_classifier(_lowercase      , candidate_labels=['''cat''', '''plane''', '''remote''']		)
								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        {'''score''': 0.511, '''label''': '''remote'''},
								        {'''score''': 0.485, '''label''': '''cat'''},
								        {'''score''': 0.004, '''label''': '''plane'''},
								    ]      , )

								_snake_case						=							image_classifier([image] * 5      , candidate_labels=['''cat''', '''plane''', '''remote''']      , batch_size=2		)
								self.assertEqual(
								    nested_simplify(_lowercase		)      , [
								        [
								            {'''score''': 0.511, '''label''': '''remote'''},
								            {'''score''': 0.485, '''label''': '''cat'''},
								            {'''score''': 0.004, '''label''': '''plane'''},
								        ],
								    ]
								    * 5      , )




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

