

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				PaintByExamplePipeline
 __SCREAMING_SNAKE_CASE												=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE												=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE												=				frozenset([]					)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




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






  """simple docstring"""





  torch.manual_seed(0 )
  UpperCAmelCase__							=				UNetaDConditionModel(
      block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=9       ,       out_channels=4       ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,       cross_attention_dim=32       ,       )
  UpperCAmelCase__							=				PNDMScheduler(skip_prk_steps=__a )
  torch.manual_seed(0 )
  UpperCAmelCase__							=				AutoencoderKL(
      block_out_channels=[32, 64]       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,       latent_channels=4       ,       )
  torch.manual_seed(0 )
  UpperCAmelCase__							=				CLIPVisionConfig(
      hidden_size=32       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1E-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       image_size=32       ,       patch_size=4       ,       )
  UpperCAmelCase__							=				PaintByExampleImageEncoder(__a       ,       proj_size=32 )
  UpperCAmelCase__							=				CLIPImageProcessor(crop_size=32       ,       size=32 )

  UpperCAmelCase__							=				{
      'unet': unet,
      'scheduler': scheduler,
      'vae': vae,
      'image_encoder': image_encoder,
      'safety_checker': None,
      'feature_extractor': feature_extractor,
  }
  return components




 def   UpperCamelCase__							(self       ,       __a ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				np.array(image.convert('RGB' ) )
  UpperCAmelCase__							=				image[None].transpose(0       ,       3       ,       1       ,       2 )
  UpperCAmelCase__							=				torch.from_numpy(__a ).to(dtype=torch.floataa ) / 1_27.5 - 1.0
  return image




 def   UpperCamelCase__							(self       ,       __a="cpu"       ,       __a=0 ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				floats_tensor((1, 3, 32, 32)       ,       rng=random.Random(__a ) ).to(__a )
  UpperCAmelCase__							=				image.cpu().permute(0       ,       2       ,       3       ,       1 )[0]
  UpperCAmelCase__							=				Image.fromarray(np.uinta(__a ) ).convert('RGB' ).resize((64, 64) )
  UpperCAmelCase__							=				Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((64, 64) )
  UpperCAmelCase__							=				Image.fromarray(np.uinta(__a ) ).convert('RGB' ).resize((32, 32) )

  if str(__a ).startswith('mps' ):
   UpperCAmelCase__							=				torch.manual_seed(__a )
  else:
   UpperCAmelCase__							=				torch.Generator(device=__a ).manual_seed(__a )
  UpperCAmelCase__							=				{
      'example_image': example_image,
      'image': init_image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }
  return inputs




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.get_dummy_components()

  # make sure here that pndm scheduler skips prk
  UpperCAmelCase__							=				PaintByExamplePipeline(**__a )
  UpperCAmelCase__							=				pipe.to('cpu' )
  pipe.set_progress_bar_config(disable=__a )

  UpperCAmelCase__							=				self.get_dummy_inputs()
  UpperCAmelCase__							=				pipe(**__a )
  UpperCAmelCase__							=				output.images

  UpperCAmelCase__							=				image[0, -3:, -3:, -1]

  assert image.shape == (1, 64, 64, 3)
  UpperCAmelCase__							=				np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03] )

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




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				'cpu'
  UpperCAmelCase__							=				self.get_dummy_inputs()
  inputs.pop('mask_image' )
  UpperCAmelCase__							=				self.convert_to_pt(inputs.pop('image' ) )
  UpperCAmelCase__							=				image.clamp(0       ,       1 ) / 2

  # make sure here that pndm scheduler skips prk
  UpperCAmelCase__							=				PaintByExamplePipeline(**self.get_dummy_components() )
  UpperCAmelCase__							=				pipe.to(__a )
  pipe.set_progress_bar_config(disable=__a )

  UpperCAmelCase__							=				pipe(image=__a       ,       mask_image=mask_image[:, 0]       ,       **__a )
  UpperCAmelCase__							=				output.images

  UpperCAmelCase__							=				image.cpu().permute(0       ,       2       ,       3       ,       1 )[0]
  UpperCAmelCase__							=				mask_image.cpu().permute(0       ,       2       ,       3       ,       1 )[0]

  UpperCAmelCase__							=				Image.fromarray(np.uinta(__a ) ).convert('RGB' )
  UpperCAmelCase__							=				Image.fromarray(np.uinta(__a ) ).convert('RGB' )

  UpperCAmelCase__							=				pipe(**self.get_dummy_inputs() )
  UpperCAmelCase__							=				output.images

  assert out_a.shape == (1, 64, 64, 3)
  assert np.abs(out_a.flatten() - out_a.flatten() ).max() < 5E-2




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






  """simple docstring"""





  super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







@slow
@require_torch_gpu
class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/paint_by_example/dog_in_bucket.png' )
  UpperCAmelCase__							=				load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/paint_by_example/mask.png' )
  UpperCAmelCase__							=				load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/paint_by_example/panda.jpg' )

  UpperCAmelCase__							=				PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example' )
  UpperCAmelCase__							=				pipe.to(__a )
  pipe.set_progress_bar_config(disable=__a )

  UpperCAmelCase__							=				torch.manual_seed(321 )
  UpperCAmelCase__							=				pipe(
      image=__a       ,       mask_image=__a       ,       example_image=__a       ,       generator=__a       ,       guidance_scale=5.0       ,       num_inference_steps=50       ,       output_type='np'       ,       )

  UpperCAmelCase__							=				output.images

  UpperCAmelCase__							=				image[0, -3:, -3:, -1]

  assert image.shape == (1, 512, 512, 3)
  UpperCAmelCase__							=				np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74] )

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






import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types



class     lowercase						(       unittest.TestCase ,				_UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				load_tool('translation' )
  self.tool.setup()
  UpperCAmelCase__							=				load_tool('translation'       ,       remote=__a )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

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

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




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

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

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

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




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']

  UpperCAmelCase__							=				[]

  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
  UpperCAmelCase__							=				self.tool(*__a )

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

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



