


"""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


lowerCAmelCase__      				=   ['''text''', '''image''', '''audio''']
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				):


  '''simple docstring'''







  lowerCAmelCase     :     Any								=							[]

  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((5_1_2, 5_1_2)				)				)
    elif input_type == "audio":
      inputs.append(torch.ones(3_0_0_0				)				)
    elif isinstance(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				):
      inputs.append(create_inputs(SCREAMING_SNAKE_CASE				)				)
    else:
      raise ValueError(f"""Invalid type requested: {input_type}"""				)

  return inputs
def 					a__			(				SCREAMING_SNAKE_CASE    :    List				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							[]

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

  return output_types





@is_tool_test
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def        lowercase__							(						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		,     snake_case__						):
             for __input in _input:
               self.assertTrue(__input in authorized_types						)
           else:
             self.assertTrue(_input in authorized_types						)

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							create_inputs(self.tool.inputs						)
         lowerCAmelCase     :     str								=							self.tool(*snake_case__						)

         # There is a single output
         if len(self.tool.outputs						) == 1:
           lowerCAmelCase     :     Union[str, Any]								=							[outputs]

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

       def        lowercase__							(						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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							create_inputs(self.tool.inputs						)
         lowerCAmelCase     :     str								=							self.tool(*snake_case__						)

         if not isinstance(snake_case__		,     snake_case__						):
           lowerCAmelCase     :     int								=							[outputs]

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

         for output, output_type in zip(snake_case__		,     self.tool.outputs						):
           lowerCAmelCase     :     List[str]								=							AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(snake_case__		,     snake_case__						)						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							create_inputs(self.tool.inputs						)

         lowerCAmelCase     :     Any								=							[]

         for _input, input_type in zip(snake_case__		,     self.tool.inputs						):
           if isinstance(snake_case__		,     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
         lowerCAmelCase     :     int								=							self.tool(*snake_case__						)

         if not isinstance(snake_case__		,     snake_case__						):
           lowerCAmelCase     :     List[Any]								=							[outputs]

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










from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    VersatileDiffusionDualGuidedPipeline,
						    VersatileDiffusionImageVariationPipeline,
						    VersatileDiffusionPipeline,
						    VersatileDiffusionTextToImagePipeline,
						)
else:
						from .modeling_text_unet import UNetFlatConditionModel
						from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
						from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
						from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
						from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
