






'''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        _UpperCAmelCase						(		unittest.TestCase			):




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    int           =     UNetaDModel(
           block_out_channels=(3_2, 6_4)  ,     layers_per_block=2  ,     sample_size=3_2  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,     up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,     )
       return model





    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.dummy_uncond_unet
       lowercase_	:    Dict           =     KarrasVeScheduler()

       lowercase_	:    str           =     KarrasVePipeline(unet=a  ,     scheduler=a			)
       pipe.to(a			)
       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    List[str]           =     torch.manual_seed(0			)
       lowercase_	:    List[str]           =     pipe(num_inference_steps=2  ,     generator=a  ,     output_type="numpy"			).images

       lowercase_	:    List[str]           =     torch.manual_seed(0			)
       lowercase_	:    Union[str, Any]           =     pipe(num_inference_steps=2  ,     generator=a  ,     output_type="numpy"  ,     return_dict=a			)[0]

       lowercase_	:    Dict           =     image[0, -3:, -3:, -1]
       lowercase_	:    List[Any]           =     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 3_2, 3_2, 3)
       lowercase_	:    Tuple           =     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        _UpperCAmelCase						(		unittest.TestCase			):




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     "google/ncsnpp-celebahq-256"
       lowercase_	:    List[Any]           =     UNetaDModel.from_pretrained(a			)
       lowercase_	:    Dict           =     KarrasVeScheduler()

       lowercase_	:    Dict           =     KarrasVePipeline(unet=a  ,     scheduler=a			)
       pipe.to(a			)
       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    str           =     torch.manual_seed(0			)
       lowercase_	:    Any           =     pipe(num_inference_steps=2_0  ,     generator=a  ,     output_type="numpy"			).images

       lowercase_	:    Optional[int]           =     image[0, -3:, -3:, -1]
       assert image.shape == (1, 2_5_6, 2_5_6, 3)
       lowercase_	:    Optional[int]           =     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












'''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


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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[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((512, 512)							)							)
      elif input_type == "audio":
         inputs.append(torch.ones(3000							)							)
      elif isinstance(_UpperCamelCase						,		_UpperCamelCase							):
         inputs.append(create_inputs(_UpperCamelCase							)							)
      else:
         raise ValueError(F"""Invalid type requested: {input_type}"""							)

   return inputs




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     []

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

   return output_types





@is_tool_test
class        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






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

       lowercase_	:    Optional[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			)

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




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

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

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




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''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 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

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

       for output, output_type in zip(a  ,     self.tool.outputs			):
          lowercase_	:    int           =     AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(a  ,     a			)			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       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
       lowercase_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

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




