





'''simple docstring'''






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    StableDiffusionDiffEditPipeline
					_SCREAMING_SNAKE_CASE							   =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
					_SCREAMING_SNAKE_CASE							   =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
					_SCREAMING_SNAKE_CASE							   =    frozenset(
					    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					_SCREAMING_SNAKE_CASE							   =    frozenset([]  )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											torch.manual_seed(0       )
											_snake_case:   Tuple  					=		UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=32       ,     attention_head_dim=(2, 4)       ,     use_linear_projection=__snake_case       ,     )
											_snake_case:   str  					=		DDIMScheduler(
											    beta_start=0.00_085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear'       ,     clip_sample=__snake_case       ,     set_alpha_to_one=__snake_case       ,     )
											_snake_case:   Optional[int]  					=		DDIMInverseScheduler(
											    beta_start=0.00_085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear'       ,     clip_sample=__snake_case       ,     set_alpha_to_zero=__snake_case       ,     )
											torch.manual_seed(0       )
											_snake_case:   Union[str, Any]  					=		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       ,     sample_size=1_28       ,     )
											torch.manual_seed(0       )
											_snake_case:   Dict  					=		CLIPTextConfig(
											    bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=10_00       ,     hidden_act='gelu'       ,     projection_dim=5_12       ,     )
											_snake_case:   Optional[int]  					=		CLIPTextModel(__snake_case       )
											_snake_case:   Dict  					=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

											_snake_case:   int  					=		{
											    'unet': unet,
											    'scheduler': scheduler,
											    'inverse_scheduler': inverse_scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}

											return components


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Optional[Any]=0       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		floats_tensor((1, 16, 16)       ,     rng=random.Random(__snake_case       )       ).to(__snake_case       )
											_snake_case:   Optional[Any]  					=		floats_tensor((1, 2, 4, 16, 16)       ,     rng=random.Random(__snake_case       )       ).to(__snake_case       )
											if str(__snake_case       ).startswith('mps'       ):
																	_snake_case:   Union[str, Any]  					=		torch.manual_seed(__snake_case       )
											else:
																	_snake_case:   str  					=		torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
											_snake_case:   List[str]  					=		{
											    'prompt': 'a dog and a newt',
											    'mask_image': mask,
											    'image_latents': latents,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'inpaint_strength': 1.0,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}

											return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]=0       ):





											'''simple docstring'''





											_snake_case:   Any  					=		floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(__snake_case       )       ).to(__snake_case       )
											_snake_case:   Any  					=		image.cpu().permute(0       ,     2       ,     3       ,     1       )[0]
											_snake_case:   List[str]  					=		Image.fromarray(np.uinta(__snake_case       )       ).convert('RGB'       )
											if str(__snake_case       ).startswith('mps'       ):
																	_snake_case:   Tuple  					=		torch.manual_seed(__snake_case       )
											else:
																	_snake_case:   Dict  					=		torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
											_snake_case:   Dict  					=		{
											    'image': image,
											    'source_prompt': 'a cat and a frog',
											    'target_prompt': 'a dog and a newt',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'num_maps_per_mask': 2,
											    'mask_encode_strength': 1.0,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}

											return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  Tuple       ,     __snake_case	:  str=0       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(__snake_case       )       ).to(__snake_case       )
											_snake_case:   Optional[int]  					=		image.cpu().permute(0       ,     2       ,     3       ,     1       )[0]
											_snake_case:   List[Any]  					=		Image.fromarray(np.uinta(__snake_case       )       ).convert('RGB'       )
											if str(__snake_case       ).startswith('mps'       ):
																	_snake_case:   Any  					=		torch.manual_seed(__snake_case       )
											else:
																	_snake_case:   List[Any]  					=		torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
											_snake_case:   Dict  					=		{
											    'image': image,
											    'prompt': 'a cat and a frog',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'inpaint_strength': 1.0,
											    'guidance_scale': 6.0,
											    'decode_latents': True,
											    'output_type': 'numpy',
											}
											return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											if not hasattr(self.pipeline_class       ,     '_optional_components'       ):
																	return

											_snake_case:   List[str]  					=		self.get_dummy_components()
											_snake_case:   str  					=		self.pipeline_class(**__snake_case       )
											pipe.to(__snake_case       )
											pipe.set_progress_bar_config(disable=__snake_case       )

											# set all optional components to None and update pipeline config accordingly
											for optional_component in pipe._optional_components:
																	setattr(__snake_case       ,     __snake_case       ,     __snake_case       )
											pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}       )

											_snake_case:   List[Any]  					=		self.get_dummy_inputs(__snake_case       )
											_snake_case:   Optional[Any]  					=		pipe(**__snake_case       )[0]

											with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(__snake_case       )
																	_snake_case:   Any  					=		self.pipeline_class.from_pretrained(__snake_case       )
																	pipe_loaded.to(__snake_case       )
																	pipe_loaded.set_progress_bar_config(disable=__snake_case       )

											for optional_component in pipe._optional_components:
																	self.assertTrue(
																	    getattr(__snake_case       ,     __snake_case       ) is None       ,     f'''`{optional_component}` did not stay set to None after loading.'''       ,     )

											_snake_case:   Any  					=		self.get_dummy_inputs(__snake_case       )
											_snake_case:   List[str]  					=		pipe_loaded(**__snake_case       )[0]

											_snake_case:   List[str]  					=		np.abs(output - output_loaded       ).max()
											self.assertLess(__snake_case       ,     1e-4       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		'cpu'

											_snake_case:   int  					=		self.get_dummy_components()
											_snake_case:   Union[str, Any]  					=		self.pipeline_class(**__snake_case       )
											pipe.to(__snake_case       )
											pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Dict  					=		self.get_dummy_mask_inputs(__snake_case       )
											_snake_case:   Dict  					=		pipe.generate_mask(**__snake_case       )
											_snake_case:   Dict  					=		mask[0, -3:, -3:]

											self.assertEqual(mask.shape       ,     (1, 16, 16)       )
											_snake_case:   Tuple  					=		np.array([0] * 9       )
											_snake_case:   Any  					=		np.abs(mask_slice.flatten() - expected_slice       ).max()
											self.assertLessEqual(__snake_case       ,     1e-3       )
											self.assertEqual(mask[0, -3, -4]       ,     0       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		'cpu'

											_snake_case:   Tuple  					=		self.get_dummy_components()
											_snake_case:   Optional[int]  					=		self.pipeline_class(**__snake_case       )
											pipe.to(__snake_case       )
											pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   int  					=		self.get_dummy_inversion_inputs(__snake_case       )
											_snake_case:   Optional[Any]  					=		pipe.invert(**__snake_case       ).images
											_snake_case:   Optional[Any]  					=		image[0, -1, -3:, -3:]

											self.assertEqual(image.shape       ,     (2, 32, 32, 3)       )
											_snake_case:   str  					=		np.array(
											    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]       ,     )
											_snake_case:   Optional[int]  					=		np.abs(image_slice.flatten() - expected_slice       ).max()
											self.assertLessEqual(__snake_case       ,     1e-3       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											super().test_inference_batch_single_identical(expected_max_diff=5e-3       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		'cpu'

											_snake_case:   Optional[Any]  					=		self.get_dummy_components()

											_snake_case:   int  					=		{'beta_start': 0.00_085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
											_snake_case:   str  					=		DPMSolverMultistepScheduler(**__snake_case       )
											_snake_case:   Any  					=		DPMSolverMultistepInverseScheduler(**__snake_case       )

											_snake_case:   Union[str, Any]  					=		self.pipeline_class(**__snake_case       )
											pipe.to(__snake_case       )
											pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Dict  					=		self.get_dummy_inversion_inputs(__snake_case       )
											_snake_case:   Optional[Any]  					=		pipe.invert(**__snake_case       ).images
											_snake_case:   Dict  					=		image[0, -1, -3:, -3:]

											self.assertEqual(image.shape       ,     (2, 32, 32, 3)       )
											_snake_case:   Any  					=		np.array(
											    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]       ,     )
											_snake_case:   str  					=		np.abs(image_slice.flatten() - expected_slice       ).max()
											self.assertLessEqual(__snake_case       ,     1e-3       )







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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





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


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  int       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'       )

											_snake_case:   str  					=		raw_image.convert('RGB'       ).resize((7_68, 7_68)       )

											_snake_case:   List[Any]  					=		raw_image


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		torch.manual_seed(0       )

											_snake_case:   str  					=		StableDiffusionDiffEditPipeline.from_pretrained(
											    'stabilityai/stable-diffusion-2-1'       ,     safety_checker=__snake_case       ,     torch_dtype=torch.floataa       )
											_snake_case:   Union[str, Any]  					=		DDIMScheduler.from_config(pipe.scheduler.config       )
											_snake_case:   Optional[int]  					=		DDIMInverseScheduler.from_config(pipe.scheduler.config       )
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Optional[int]  					=		'a bowl of fruit'
											_snake_case:   Tuple  					=		'a bowl of pears'

											_snake_case:   Dict  					=		pipe.generate_mask(
											    image=self.raw_image       ,     source_prompt=__snake_case       ,     target_prompt=__snake_case       ,     generator=__snake_case       ,     )

											_snake_case:   Tuple  					=		pipe.invert(
											    prompt=__snake_case       ,     image=self.raw_image       ,     inpaint_strength=0.7       ,     generator=__snake_case       ).latents

											_snake_case:   List[str]  					=		pipe(
											    prompt=__snake_case       ,     mask_image=__snake_case       ,     image_latents=__snake_case       ,     generator=__snake_case       ,     negative_prompt=__snake_case       ,     inpaint_strength=0.7       ,     output_type='numpy'       ,     ).images[0]

											_snake_case:   Optional[Any]  					=		(
											    np.array(
											        load_image(
											            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											            '/diffedit/pears.png'       ).resize((7_68, 7_68)       )       )
											    / 2_55
											)
											assert np.abs((expected_image - image).max()       ) < 5e-1




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		torch.manual_seed(0       )

											_snake_case:   Any  					=		StableDiffusionDiffEditPipeline.from_pretrained(
											    'stabilityai/stable-diffusion-2-1'       ,     safety_checker=__snake_case       ,     torch_dtype=torch.floataa       )
											_snake_case:   Optional[Any]  					=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
											_snake_case:   Dict  					=		DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config       )
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   int  					=		'a bowl of fruit'
											_snake_case:   str  					=		'a bowl of pears'

											_snake_case:   Union[str, Any]  					=		pipe.generate_mask(
											    image=self.raw_image       ,     source_prompt=__snake_case       ,     target_prompt=__snake_case       ,     generator=__snake_case       ,     )

											_snake_case:   int  					=		pipe.invert(
											    prompt=__snake_case       ,     image=self.raw_image       ,     inpaint_strength=0.7       ,     generator=__snake_case       ,     num_inference_steps=25       ,     ).latents

											_snake_case:   Optional[int]  					=		pipe(
											    prompt=__snake_case       ,     mask_image=__snake_case       ,     image_latents=__snake_case       ,     generator=__snake_case       ,     negative_prompt=__snake_case       ,     inpaint_strength=0.7       ,     num_inference_steps=25       ,     output_type='numpy'       ,     ).images[0]

											_snake_case:   Optional[int]  					=		(
											    np.array(
											        load_image(
											            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											            '/diffedit/pears.png'       ).resize((7_68, 7_68)       )       )
											    / 2_55
											)
											assert np.abs((expected_image - image).max()       ) < 5e-1












'''simple docstring'''






A   : str             =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A   : Union[str, Any]             =			{value: key for key, value in encode_dict.items()}





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('encode() accepts only letters of the alphabet and spaces'						)
						return encoded





def       lowercase_      (		lowercase__						)   ->str:
						if set(lowercase__						) - {"A", "B", " "} != set():
												raise Exception('decode() accepts only \'A\', \'B\' and spaces'						)
						_snake_case:   List[Any]  					=		''
						for word in coded.split():
												while len(lowercase__						) != 0:
																		decoded += decode_dict[word[:5]]
																		_snake_case:   Optional[int]  					=		word[5:]
												decoded += " "
						return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()





