




'''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 							_a				:





             '''simple docstring'''






             @staticmethod
             def        UpperCamelCase_							(      *A,							**A  ):







                   '''simple docstring'''







                   pass






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





       '''simple docstring'''






       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						pipeline(
                 model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification',							)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image_classifier(A,							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(A  ),							[
                     [{'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'}],
                 ],							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						image_classifier([image] * 5,							candidate_labels=['A', 'B', 'C'],							batch_size=2  )
             self.assertEqual(
                 nested_simplify(A  ),							[
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                 ],							)







       @require_tf
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						pipeline(
                 model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification',							framework='tf'  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image_classifier(A,							candidate_labels=['a', 'b', 'c']  )

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

             SCREAMING_SNAKE_CASE   :      Any    					=						image_classifier([image] * 5,							candidate_labels=['A', 'B', 'C'],							batch_size=2  )
             self.assertEqual(
                 nested_simplify(A  ),							[
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                     [
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                         {'score': 0.3_33, 'label': ANY(A  )},
                     ],
                 ],							)







       @slow
       @require_torch
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      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
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						image_classifier(A,							candidate_labels=['cat', 'plane', 'remote']  )

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

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







       @slow
       @require_tf
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						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
             SCREAMING_SNAKE_CASE   :      Tuple    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image_classifier(A,							candidate_labels=['cat', 'plane', 'remote']  )
             self.assertEqual(
                 nested_simplify(A  ),							[
                     {'score': 0.5_11, 'label': 'remote'},
                     {'score': 0.4_85, 'label': 'cat'},
                     {'score': 0.0_04, 'label': 'plane'},
                 ],							)

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








'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}

             if truncation is not None:
                   if "truncation" in tokenize_kwargs:
                         raise ValueError(
                             'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''simple docstring'''







             if return_tensors:
                   return model_outputs[0]
             if self.framework == "pt":
                   return model_outputs[0].tolist()
             elif self.framework == "tf":
                   return model_outputs[0].numpy().tolist()







       def __call__(      self,							*A,							**A  ):







             '''simple docstring'''







             return super().__call__(*A,							**A  )


