
"""simple docstring"""


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 	lowerCamelCase__      (      unittest.TestCase				,			__magic_name__       ):





			'''simple docstring'''



			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				_lowerCAmelCase			=load_tool("""image-segmentation"""						)
				self.tool.setup()
				_lowerCAmelCase			=load_tool("""image-segmentation"""  ,  remote=__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				_lowerCAmelCase			=Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""						)						) / """000000039769.png"""						)
				_lowerCAmelCase			=self.tool(__UpperCAmelCase  ,  """cat"""						)
				self.assertTrue(isinstance(__UpperCAmelCase  ,  Image.Image						)						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				_lowerCAmelCase			=Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""						)						) / """000000039769.png"""						)
				_lowerCAmelCase			=self.remote_tool(__UpperCAmelCase  ,  """cat"""						)
				self.assertTrue(isinstance(__UpperCAmelCase  ,  Image.Image						)						)



			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				_lowerCAmelCase			=Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""						)						) / """000000039769.png"""						)
				_lowerCAmelCase			=self.tool(image=__UpperCAmelCase  ,  label="""cat"""						)
				self.assertTrue(isinstance(__UpperCAmelCase  ,  Image.Image						)						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""						)						) / """000000039769.png"""						)
				_lowerCAmelCase			=self.remote_tool(image=__UpperCAmelCase  ,  label="""cat"""						)
				self.assertTrue(isinstance(__UpperCAmelCase  ,  Image.Image						)						)





"""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,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    '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

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



