






"""simple docstring"""






import itertools
import string
from collections.abc import Generator, Iterable



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       iter(lowerCAmelCase__				)
				while True:
								A__							 =       tuple(itertools.islice(lowerCAmelCase__       ,lowerCAmelCase__				)				)
								if not chunk:
												return
								yield chunk



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       ''.join([c.upper() for c in dirty if c in string.ascii_letters]				)
				A__							 =       ''

				if len(lowerCAmelCase__				) < 2:
								return dirty

				for i in range(len(lowerCAmelCase__				) - 1				):
								clean += dirty[i]

								if dirty[i] == dirty[i + 1]:
												clean += "X"

				clean += dirty[-1]

				if len(lowerCAmelCase__				) & 1:
								clean += "X"

				return clean



def  __lowerCamelCase					(					lowerCAmelCase__				):
				# I and J are used interchangeably to allow
				# us to use a 5x5 table (25 letters)
				A__							 =       'ABCDEFGHIKLMNOPQRSTUVWXYZ'
				# we're using a list instead of a '2d' array because it makes the math
				# for setting up the table and doing the actual encoding/decoding simpler
				A__							 =       []

				# copy key chars into the table if they are in `alphabet` ignoring duplicates
				for char in key.upper():
								if char not in table and char in alphabet:
												table.append(lowerCAmelCase__				)

    # fill the rest of the table in with the remaining alphabet chars
				for char in alphabet:
								if char not in table:
												table.append(lowerCAmelCase__				)

				return table



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       generate_table(lowerCAmelCase__				)
				A__							 =       prepare_input(lowerCAmelCase__				)
				A__							 =       ''

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase__       ,2				):
								A__ ,			A__							 =       divmod(table.index(lowerCAmelCase__				)       ,5				)
								A__ ,			A__							 =       divmod(table.index(lowerCAmelCase__				)       ,5				)

								if rowa == rowa:
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
								elif cola == cola:
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
								else:  # rectangle
												ciphertext += table[rowa * 5 + cola]
												ciphertext += table[rowa * 5 + cola]

				return ciphertext







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       generate_table(lowerCAmelCase__				)
				A__							 =       ''

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(lowerCAmelCase__       ,2				):
								A__ ,			A__							 =       divmod(table.index(lowerCAmelCase__				)       ,5				)
								A__ ,			A__							 =       divmod(table.index(lowerCAmelCase__				)       ,5				)

								if rowa == rowa:
												plaintext += table[rowa * 5 + (cola - 1) % 5]
												plaintext += table[rowa * 5 + (cola - 1) % 5]
								elif cola == cola:
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
								else:  # rectangle
												plaintext += table[rowa * 5 + cola]
												plaintext += table[rowa * 5 + cola]

				return plaintext











"""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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					StableDiffusionDiffEditPipeline
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							int   			=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							str   			=					frozenset(
	    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	SCREAMING_SNAKE_CASE_:							int   			=					frozenset([]       )


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       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=__a  ,					)
					A__							 =       DDIMScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_one=__a  ,					)
					A__							 =       DDIMInverseScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_zero=__a  ,					)
					torch.manual_seed(0						)
					A__							 =       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=128  ,					)
					torch.manual_seed(0						)
					A__							 =       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=1000  ,					hidden_act='gelu'  ,					projection_dim=512  ,					)
					A__							 =       CLIPTextModel(__a						)
					A__							 =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       floats_tensor((1, 2, 4, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe(**__a						)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(__a						)
									A__							 =       self.pipeline_class.from_pretrained(__a						)
									pipe_loaded.to(__a						)
									pipe_loaded.set_progress_bar_config(disable=__a						)

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe_loaded(**__a						)[0]

					A__							 =       np.abs(output - output_loaded						).max()
					self.assertLess(__a  ,					1E-4						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_mask_inputs(__a						)
					A__							 =       pipe.generate_mask(**__a						)
					A__							 =       mask[0, -3:, -3:]

					self.assertEqual(mask.shape  ,					(1, 16, 16)						)
					A__							 =       np.array([0] * 9						)
					A__							 =       np.abs(mask_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)
					self.assertEqual(mask[0, -3, -4]  ,					0						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inversion_inputs(__a						)
					A__							 =       pipe.invert(**__a						).images
					A__							 =       image[0, -1, -3:, -3:]

					self.assertEqual(image.shape  ,					(2, 32, 32, 3)						)
					A__							 =       np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]  ,					)
					A__							 =       np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()

					A__							 =       {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
					A__							 =       DPMSolverMultistepScheduler(**__a						)
					A__							 =       DPMSolverMultistepInverseScheduler(**__a						)

					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inversion_inputs(__a						)
					A__							 =       pipe.invert(**__a						).images
					A__							 =       image[0, -1, -3:, -3:]

					self.assertEqual(image.shape  ,					(2, 32, 32, 3)						)
					A__							 =       np.array(
					    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]  ,					)
					A__							 =       np.abs(image_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)





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



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	@classmethod
	def       _UpperCAmelCase						(      cls						):


					"""simple docstring"""

					A__							 =       load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'						)

					A__							 =       raw_image.convert('RGB'						).resize((768, 768)						)

					A__							 =       raw_image


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

					A__							 =       StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'  ,					safety_checker=__a  ,					torch_dtype=torch.floataa						)
					A__							 =       DDIMScheduler.from_config(pipe.scheduler.config						)
					A__							 =       DDIMInverseScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

					A__							 =       pipe.invert(
					    prompt=__a  ,					image=self.raw_image  ,					inpaint_strength=0.7  ,					generator=__a						).latents

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    np.array(
					        load_image(
					            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					            '/diffedit/pears.png'						).resize((768, 768)						)						)
					    / 255
					)
					assert np.abs((expected_image - image).max()						) < 5E-1






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

					A__							 =       StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'  ,					safety_checker=__a  ,					torch_dtype=torch.floataa						)
					A__							 =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
					A__							 =       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

					A__							 =       pipe.invert(
					    prompt=__a  ,					image=self.raw_image  ,					inpaint_strength=0.7  ,					generator=__a  ,					num_inference_steps=25  ,					).latents

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					num_inference_steps=25  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    np.array(
					        load_image(
					            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					            '/diffedit/pears.png'						).resize((768, 768)						)						)
					    / 255
					)
					assert np.abs((expected_image - image).max()						) < 5E-1


