

import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image



class SCREAMING_SNAKE_CASE	(  unittest.TestCase   ,							a_ ):
  """simple docstring"""






  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						load_tool("""image-captioning"""    )
     self.tool.setup()
     __lowerCAmelCase  :			Union[str, Any]					    =						load_tool("""image-captioning"""       ,							remote=lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			str					    =						Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
     __lowerCAmelCase  :			Optional[Any]					    =						self.tool(lowerCAmelCase    )
     self.assertEqual(lowerCAmelCase       ,							"""two cats sleeping on a couch"""    )







  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			List[Any]					    =						Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
     __lowerCAmelCase  :			int					    =						self.remote_tool(lowerCAmelCase    )
     self.assertEqual(lowerCAmelCase       ,							"""two cats sleeping on a couch"""    )







  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Any:






     """simple docstring"""







     __lowerCAmelCase  :			List[Any]					    =						Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
     __lowerCAmelCase  :			List[Any]					    =						self.tool(image=lowerCAmelCase    )
     self.assertEqual(lowerCAmelCase       ,							"""two cats sleeping on a couch"""    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	Any:






     """simple docstring"""







     __lowerCAmelCase  :			int					    =						Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""    )    ) / """000000039769.png"""    )
     __lowerCAmelCase  :			Dict					    =						self.remote_tool(image=lowerCAmelCase    )
     self.assertEqual(lowerCAmelCase       ,							"""two cats sleeping on a couch"""    )








'''simple docstring'''


# Copyright 2023 The HuggingFace 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_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

