






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  SCREAMING_SNAKE_CASE			:
        '''simple docstring'''


        @staticmethod
        def 				_A       (				*UpperCAmelCase_       :	int					, **UpperCAmelCase_       :	Optional[Any]      ):
          pass


@is_pipeline_test
@require_vision
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     @require_torch
     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      int							=    pipeline(
           model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"					, )
       SCREAMING_SNAKE_CASE       :      Tuple							=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       SCREAMING_SNAKE_CASE       :      int							=    image_classifier(UpperCAmelCase_					, 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(UpperCAmelCase_      )					, [
               [{"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"}],
           ]					, )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_classifier([image] * 5					, candidate_labels=["A", "B", "C"]					, batch_size=2      )
       self.assertEqual(
           nested_simplify(UpperCAmelCase_      )					, [
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
           ]					, )


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

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

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_classifier([image] * 5					, candidate_labels=["A", "B", "C"]					, batch_size=2      )
       self.assertEqual(
           nested_simplify(UpperCAmelCase_      )					, [
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
               [
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
                   {"score": 0.333, "label": ANY(UpperCAmelCase_      )},
               ],
           ]					, )


     @slow
     @require_torch
     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    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[int]							=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       SCREAMING_SNAKE_CASE       :      int							=    image_classifier(UpperCAmelCase_					, candidate_labels=["cat", "plane", "remote"]      )

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

       SCREAMING_SNAKE_CASE       :      List[Any]							=    image_classifier([image] * 5					, candidate_labels=["cat", "plane", "remote"]					, batch_size=2      )
       self.assertEqual(
           nested_simplify(UpperCAmelCase_      )					, [
               [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ],
           ]
           * 5					, )






     @slow
     @require_tf
     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      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
       SCREAMING_SNAKE_CASE       :      List[Any]							=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_classifier(UpperCAmelCase_					, candidate_labels=["cat", "plane", "remote"]      )
       self.assertEqual(
           nested_simplify(UpperCAmelCase_      )					, [
               {"score": 0.511, "label": "remote"},
               {"score": 0.485, "label": "cat"},
               {"score": 0.004, "label": "plane"},
           ]					, )

       SCREAMING_SNAKE_CASE       :      List[str]							=    image_classifier([image] * 5					, candidate_labels=["cat", "plane", "remote"]					, batch_size=2      )
       self.assertEqual(
           nested_simplify(UpperCAmelCase_      )					, [
               [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ],
           ]
           * 5					, )














# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case    				=		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CpmAntForCausalLM""",
       """CpmAntModel""",
       """CpmAntPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






