



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      UpperCAmelCase_						(     UpperCamelCase_					,					unittest.TestCase ):

						'''simple docstring'''




						UpperCamelCase__    :				Dict       						=						PaintByExamplePipeline
						UpperCamelCase__    :				str       						=						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase__    :				List[str]       						=						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase__    :				Any       						=						frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






						def  _A			(					self	):







													'''simple docstring'''

													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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   ,							)
													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
													torch.manual_seed(0	)
													__SCREAMING_SNAKE_CASE								=      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	)
													__SCREAMING_SNAKE_CASE								=      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   ,							)
													__SCREAMING_SNAKE_CASE								=      PaintByExampleImageEncoder(_A   ,							proj_size=32	)
													__SCREAMING_SNAKE_CASE								=      CLIPImageProcessor(crop_size=32   ,							size=32	)

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






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      np.array(image.convert('RGB'	)	)
													__SCREAMING_SNAKE_CASE								=      image[None].transpose(0   ,							3   ,							1   ,							2	)
													__SCREAMING_SNAKE_CASE								=      torch.from_numpy(_A	).to(dtype=torch.floataa	) / 1_2_7.5 - 1.0
													return image






						def  _A			(					self   ,							_A="cpu"   ,							_A=0	):







													'''simple docstring'''

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

													if str(_A	).startswith('mps'	):
																				__SCREAMING_SNAKE_CASE								=      torch.manual_seed(_A	)
													else:
																				__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(_A	)
													__SCREAMING_SNAKE_CASE								=      {
													    '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  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      self.get_dummy_components()

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

													__SCREAMING_SNAKE_CASE								=      self.get_dummy_inputs()
													__SCREAMING_SNAKE_CASE								=      pipe(**_A	)
													__SCREAMING_SNAKE_CASE								=      output.images

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

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE								=      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  _A			(					self	):







													'''simple docstring'''

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

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

													__SCREAMING_SNAKE_CASE								=      pipe(image=_A   ,							mask_image=mask_image[:, 0]   ,							**_A	)
													__SCREAMING_SNAKE_CASE								=      output.images

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

													__SCREAMING_SNAKE_CASE								=      Image.fromarray(np.uinta(_A	)	).convert('RGB'	)
													__SCREAMING_SNAKE_CASE								=      Image.fromarray(np.uinta(_A	)	).convert('RGB'	)

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

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






						def  _A			(					self	):







													'''simple docstring'''

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







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

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self	):







													'''simple docstring'''

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

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

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

													__SCREAMING_SNAKE_CASE								=      output.images

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

													assert image.shape == (1, 512, 512, 3)
													__SCREAMING_SNAKE_CASE								=      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











import unittest

from cer import CER


lowerCAmelCase__	:  Dict 		=CER()

class      UpperCAmelCase_						(     unittest.TestCase ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['White House']
													__SCREAMING_SNAKE_CASE								=      ['white house']
													# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weae     wolf']
													# S = 1, D = 1, I = 0, N = 8, CER = 0.25
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.2_5	) < 1e-6	)

													# consecutive whitespaces case 1
													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)

													# consecutive whitespaces case 2
													__SCREAMING_SNAKE_CASE								=      ['were   wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weaewolf']
													# S = 1, D = 0, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereawolf']
													# S = 0, D = 1, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereolf']
													# S = 0, D = 0, I = 1, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father', 'doge']
													__SCREAMING_SNAKE_CASE								=      ['werxwolf', 'I       am your father', 'doge']
													# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['My hovercraft', 'is full of eels']
													__SCREAMING_SNAKE_CASE								=      ['My hovercraft is full', ' of eels']
													# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
													# whitespace at the front of " of eels" will be strip during preporcessing
													# so need to insert 2 whitespaces
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A   ,							concatenate_texts=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      [' 能吞虾玻璃而 不霜身体啦']
													# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃', '而不伤身体']
													__SCREAMING_SNAKE_CASE								=      ['我    能 吞 下 玻 璃', '而不伤身体']
													# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertFalse(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['']
													__SCREAMING_SNAKE_CASE								=      ['Hypothesis']
													with self.assertRaises(_A	):
																				cer.compute(predictions=_A   ,							references=_A	)


if __name__ == "__main__":
			unittest.main()






