


'''simple docstring'''



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


__lowercase      :		List[str]			=      ['text', 'image', 'audio']






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Tuple  ):
					__a     :			int          =    []

					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(_lowercase		, _lowercase  ):
															inputs.append(create_inputs(_lowercase  )  )
										else:
															raise ValueError(F"""Invalid type requested: {input_type}"""  )

					return inputs






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  int  ):
					__a     :			Dict          =    []

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

					return output_types





@is_tool_test
class 				__UpperCamelCase							:




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





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

						__a     :			str          =    self.tool.inputs
						for _input in inputs:
											if isinstance(_input		,							_SCREAMING_SNAKE_CASE				):
																for __input in _input:
																					self.assertTrue(__input in authorized_types				)
											else:
																self.assertTrue(_input in authorized_types				)

						__a     :			Union[str, Any]          =    self.tool.outputs
						for _output in outputs:
											self.assertTrue(_output in authorized_types				)




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Union[str, Any]          =    create_inputs(self.tool.inputs				)
						__a     :			Any          =    self.tool(*_SCREAMING_SNAKE_CASE				)

						# There is a single output
						if len(self.tool.outputs				) == 1:
											__a     :			Optional[int]          =    [outputs]

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




	def  __UpperCAmelCase				(  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  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Optional[Any]          =    create_inputs(self.tool.inputs				)
						__a     :			str          =    self.tool(*_SCREAMING_SNAKE_CASE				)

						if not isinstance(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE				):
											__a     :			Union[str, Any]          =    [outputs]

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

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





	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			Union[str, Any]          =    create_inputs(self.tool.inputs				)

						__a     :			Tuple          =    []

						for _input, input_type in zip(_SCREAMING_SNAKE_CASE		,							self.tool.inputs				):
											if isinstance(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE				):
																_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
						__a     :			List[Any]          =    self.tool(*_SCREAMING_SNAKE_CASE				)

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

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









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )