




"""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


SCREAMING_SNAKE_CASE_	     =       ['text', 'image', 'audio']





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	[]

    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(3000    )    )
        elif isinstance(_lowercase			,_lowercase    ):
            inputs.append(create_inputs(_lowercase    )    )
        else:
            raise ValueError(f'Invalid type requested: {input_type}'    )

    return inputs





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	[]

    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 				snake_case_			:







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   List[Any]:
       self.assertTrue(hasattr(self.tool       ,					'''inputs'''))
       self.assertTrue(hasattr(self.tool       ,					'''outputs'''))

       UpperCamelCase              =	self.tool.inputs
       for _input in inputs:
           if isinstance(_input       ,					lowerCamelCase_):
               for __input in _input:
                   self.assertTrue(__input in authorized_types)
           else:
               self.assertTrue(_input in authorized_types)

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







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	create_inputs(self.tool.inputs)
       UpperCamelCase              =	self.tool(*lowerCamelCase_)

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

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







   def   UpperCAmelCase__			(		self)				->   str:
       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)				->   Any:
       UpperCamelCase              =	create_inputs(self.tool.inputs)
       UpperCamelCase              =	self.tool(*lowerCamelCase_)

       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[outputs]

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

       for output, output_type in zip(lowerCamelCase_       ,					self.tool.outputs):
           UpperCamelCase              =	AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(lowerCamelCase_       ,					lowerCamelCase_))







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	create_inputs(self.tool.inputs)

       UpperCamelCase              =	[]

       for _input, input_type in zip(lowerCamelCase_       ,					self.tool.inputs):
           if isinstance(lowerCamelCase_       ,					lowerCamelCase_):
               _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
       UpperCamelCase              =	self.tool(*lowerCamelCase_)

       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[outputs]

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


'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






