



from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image


_snake_case		=  ["text", "image", "audio"]






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    []

							for input_type in input_types:
														if input_type == "text":
																					inputs.append("Text input"  )
														elif input_type == "image":
																					inputs.append(
																					    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  ).resize((512, 512)  )  )
														elif input_type == "audio":
																					inputs.append(torch.ones(3_000  )  )
														elif isinstance(_lowerCamelCase				, _lowerCamelCase  ):
																					inputs.append(create_inputs(_lowerCamelCase  )  )
														else:
																					raise ValueError(F"Invalid type requested: {input_type}"  )

							return inputs






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    []

							for output in outputs:
														if isinstance(_lowerCamelCase				, (str, AgentText)  ):
																					output_types.append("text"  )
														elif isinstance(_lowerCamelCase				, (Image.Image, AgentImage)  ):
																					output_types.append("image"  )
														elif isinstance(_lowerCamelCase				, (torch.Tensor, AgentAudio)  ):
																					output_types.append("audio"  )
														else:
																					raise ValueError(F"Invalid output: {output}"  )

							return output_types



@is_tool_test
class   UpperCAmelCase_    :







							def    snake_case__      (   self):

														'''simple docstring'''

														self.assertTrue(hasattr(self.tool,			"inputs"))
														self.assertTrue(hasattr(self.tool,			"outputs"))

														_lowerCAmelCase     :							List[Any]    =    self.tool.inputs
														for _input in inputs:
																					if isinstance(_input,			__a):
																												for __input in _input:
																																			self.assertTrue(__input in authorized_types)
																					else:
																												self.assertTrue(_input in authorized_types)

														_lowerCAmelCase     :							str    =    self.tool.outputs
														for _output in outputs:
																					self.assertTrue(_output in authorized_types)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    create_inputs(self.tool.inputs)
														_lowerCAmelCase     :							Dict    =    self.tool(*__a)

														# There is a single output
														if len(self.tool.outputs) == 1:
																					_lowerCAmelCase     :							Dict    =    [outputs]

														self.assertListEqual(output_types(__a),			self.tool.outputs)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.assertTrue(hasattr(self.tool,			"description"))
														self.assertTrue(hasattr(self.tool,			"default_checkpoint"))
														self.assertTrue(self.tool.description.startswith("This is a tool that"))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    create_inputs(self.tool.inputs)
														_lowerCAmelCase     :							Any    =    self.tool(*__a)

														if not isinstance(__a,			__a):
																					_lowerCAmelCase     :							str    =    [outputs]

														self.assertEqual(len(__a),			len(self.tool.outputs))

														for output, output_type in zip(__a,			self.tool.outputs):
																					_lowerCAmelCase     :							Any    =    AGENT_TYPE_MAPPING[output_type]
																					self.assertTrue(isinstance(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    create_inputs(self.tool.inputs)

														_lowerCAmelCase     :							Tuple    =    []

														for _input, input_type in zip(__a,			self.tool.inputs):
																					if isinstance(__a,			__a):
																												_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
																					else:
																												_inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
														_lowerCAmelCase     :							Dict    =    self.tool(*__a)

														if not isinstance(__a,			__a):
																					_lowerCAmelCase     :							Any    =    [outputs]

														self.assertEqual(len(__a),			len(self.tool.outputs))




# 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, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





