"""simple docstring"""






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     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

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







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									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=lowerCamelCase__      )
									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-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,image_size=32   ,patch_size=4   ,)
									UpperCAmelCase__					=							PaintByExampleImageEncoder(lowerCamelCase__   ,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 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     List[Any]      ):
									UpperCAmelCase__					=							np.array(image.convert('RGB'      )      )
									UpperCAmelCase__					=							image[None].transpose(0   ,3   ,1   ,2      )
									UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase__      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
									return image







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Union[str, Any]="cpu"   ,lowerCamelCase__       :     List[Any]=0      ):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									UpperCAmelCase__					=							floats_tensor((1, 3, 32, 32)   ,rng=random.Random(lowerCamelCase__      )      ).to(lowerCamelCase__      )
									UpperCAmelCase__					=							image.cpu().permute(0   ,2   ,3   ,1      )[0]
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((64, 64)      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((64, 64)      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).convert('RGB'      ).resize((32, 32)      )

									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									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 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.get_dummy_components()

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

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

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

									assert image.shape == (1, 64, 64, 3)
									UpperCAmelCase__					=							np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]      )

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







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									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(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							pipe(image=lowerCamelCase__   ,mask_image=mask_image[:, 0]   ,**lowerCamelCase__      )
									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(lowerCamelCase__      )      ).convert('RGB'      )
									UpperCAmelCase__					=							Image.fromarray(np.uinta(lowerCamelCase__      )      ).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 		__lowerCAmelCase						(    self       :     int      ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									# make sure here that pndm scheduler skips prk
									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(lowerCamelCase__      )
									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							torch.manual_seed(321      )
									UpperCAmelCase__					=							pipe(
									    image=lowerCamelCase__   ,mask_image=lowerCamelCase__   ,example_image=lowerCamelCase__   ,generator=lowerCamelCase__   ,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([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]      )

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




"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCAmelCase__  :  List[Any]											=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class     snake_case	(			unittest.TestCase				,   __UpperCAmelCase    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							load_tool('summarization'      )
									self.tool.setup()
									UpperCAmelCase__					=							load_tool('summarization'   ,remote=lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)



