





'''simple docstring'''







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class    __a (SCREAMING_SNAKE_CASE__       ):

							def __init__(							self							:       int    ,       __magic_name__							:       AutoencoderKL    ,       __magic_name__							:       CLIPTextModel    ,       __magic_name__							:       CLIPTokenizer    ,       __magic_name__							:       UNetaDConditionModel    ,       __magic_name__							:       DDIMScheduler    ,       __magic_name__							:       float = 0.9_3    ,       __magic_name__							:       bool = True    ,       )   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					mixing_coeff
													UpperCAmelCase_     : Optional[Any]         =					leapfrog_steps

													super().__init__()
													self.register_modules(
													    vae=__magic_name__    ,       text_encoder=__magic_name__    ,       tokenizer=__magic_name__    ,       unet=__magic_name__    ,       scheduler=__magic_name__    ,       )

													UpperCAmelCase_     : Optional[int]         =					2 ** (len(self.vae.config.block_out_channels				) - 1)
													UpperCAmelCase_     : Optional[Any]         =					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor				)

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       str    ,       __magic_name__							:       Optional[str] = None    ,       __magic_name__							:       bool = False				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.tokenizer(
													    __magic_name__    ,       padding='''max_length'''    ,       max_length=self.tokenizer.model_max_length    ,       truncation=__magic_name__    ,       return_tensors='''pt'''    ,       )

													UpperCAmelCase_     : List[Any]         =					self.text_encoder(text_inputs.input_ids.to(self.device				)				).last_hidden_state

													UpperCAmelCase_     : List[str]         =					prompt_embeds.to(dtype=self.text_encoder.dtype    ,       device=self.device				)

													if do_classifier_free_guidance:
																			UpperCAmelCase_     : int         =					'''''' if negative_prompt is None else negative_prompt

																			UpperCAmelCase_     : str         =					self.tokenizer(
																			    __magic_name__    ,       padding='''max_length'''    ,       max_length=self.tokenizer.model_max_length    ,       truncation=__magic_name__    ,       return_tensors='''pt'''    ,       )

																			UpperCAmelCase_     : Optional[Any]         =					self.text_encoder(uncond_input.input_ids.to(self.device				)				).last_hidden_state

																			UpperCAmelCase_     : Optional[int]         =					torch.cat([negative_prompt_embeds, prompt_embeds]				)

													return prompt_embeds

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
													UpperCAmelCase_     : Optional[int]         =					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

													return [x, y]

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
													UpperCAmelCase_     : Union[str, Any]         =					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

													return [x, y]

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       torch.Tensor				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					int(__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

													return alpha_prod, 1 - alpha_prod

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       )   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													UpperCAmelCase_  ,    UpperCAmelCase_     : str         =					self._get_alpha_and_beta(__magic_name__				)
													UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[int]         =					self._get_alpha_and_beta(__magic_name__				)

													UpperCAmelCase_     : str         =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
													UpperCAmelCase_     : Dict         =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

													UpperCAmelCase_     : int         =					(base - b_t * model_output) / a_t

													return model_input, next_model_input.to(base.dtype				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       )   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

													UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[Any]         =					self._get_alpha_and_beta(__magic_name__				)
													UpperCAmelCase_  ,    UpperCAmelCase_     : int         =					self._get_alpha_and_beta(__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
													UpperCAmelCase_     : Union[str, Any]         =					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
													UpperCAmelCase_     : str         =					a_t * base + b_t * model_output

													return model_input, next_model_input.to(base.dtype				)

							@torch.no_grad()
							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       torch.Tensor				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					1 / self.vae.config.scaling_factor * latents
													UpperCAmelCase_     : int         =					self.vae.decode(__magic_name__				).sample
													UpperCAmelCase_     : Optional[int]         =					(image / 2 + 0.5).clamp(0    ,       1				)
													return image

							@torch.no_grad()
							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       Image.Image    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       torch.Tensor    ,       __magic_name__							:       float    ,       __magic_name__							:       Optional[torch.Generator] = None    ,       )   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					guidance_scale > 1.0

													UpperCAmelCase_     : Optional[Any]         =					image.to(device=self.device    ,       dtype=text_embeds.dtype				)
													UpperCAmelCase_     : int         =					self.vae.encode(__magic_name__				).latent_dist.sample(__magic_name__				)

													UpperCAmelCase_     : Tuple         =					self.vae.config.scaling_factor * latent

													UpperCAmelCase_     : Optional[Any]         =					[latent.clone(), latent.clone()]

													for i, t in tqdm(enumerate(__magic_name__				)    ,       total=len(__magic_name__				)				):
																			UpperCAmelCase_     : List[Any]         =					self.noise_mixing_layer(x=coupled_latents[0]    ,       y=coupled_latents[1]				)

																			# j - model_input index, k - base index
																			for j in range(2				):
																									UpperCAmelCase_     : List[Any]         =					j ^ 1

																									if self.leapfrog_steps:
																															if i % 2 == 0:
																																					UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					j, k

																									UpperCAmelCase_     : Dict         =					coupled_latents[j]
																									UpperCAmelCase_     : Dict         =					coupled_latents[k]

																									UpperCAmelCase_     : List[str]         =					torch.cat([model_input] * 2				) if do_classifier_free_guidance else model_input

																									UpperCAmelCase_     : str         =					self.unet(__magic_name__    ,       __magic_name__    ,       encoder_hidden_states=__magic_name__				).sample

																									if do_classifier_free_guidance:
																															UpperCAmelCase_  ,    UpperCAmelCase_     : int         =					noise_pred.chunk(2				)
																															UpperCAmelCase_     : List[Any]         =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					self.noise_step(
																									    base=__magic_name__    ,       model_input=__magic_name__    ,       model_output=__magic_name__    ,       timestep=__magic_name__    ,       )

																									UpperCAmelCase_     : Optional[int]         =					model_input

													return coupled_latents



							@torch.no_grad()
							def __call__(							self							:       Optional[Any]    ,       __magic_name__							:       str    ,       __magic_name__							:       str    ,       __magic_name__							:       Image.Image    ,       __magic_name__							:       float = 3.0    ,       __magic_name__							:       int = 50    ,       __magic_name__							:       float = 0.8    ,       __magic_name__							:       Optional[str] = None    ,       __magic_name__							:       Optional[torch.Generator] = None    ,       __magic_name__							:       Optional[str] = "pil"    ,       )   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					guidance_scale > 1.0

													UpperCAmelCase_     : str         =					self.image_processor.preprocess(__magic_name__				)

													UpperCAmelCase_     : int         =					self._encode_prompt(__magic_name__    ,       __magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : str         =					self._encode_prompt(__magic_name__    ,       __magic_name__    ,       __magic_name__				)

													self.scheduler.set_timesteps(__magic_name__    ,       self.device				)

													UpperCAmelCase_     : List[str]         =					num_inference_steps - int(num_inference_steps * strength				)
													UpperCAmelCase_     : int         =					self.scheduler.timesteps[t_limit:]
													UpperCAmelCase_     : Union[str, Any]         =					fwd_timesteps.flip(0				)

													UpperCAmelCase_     : Optional[int]         =					self.prepare_latents(__magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__				)

													for i, t in tqdm(enumerate(__magic_name__				)    ,       total=len(__magic_name__				)				):
																			# j - model_input index, k - base index
																			for k in range(2				):
																									UpperCAmelCase_     : str         =					k ^ 1

																									if self.leapfrog_steps:
																															if i % 2 == 1:
																																					UpperCAmelCase_  ,    UpperCAmelCase_     : Tuple         =					j, k

																									UpperCAmelCase_     : Dict         =					coupled_latents[j]
																									UpperCAmelCase_     : Optional[int]         =					coupled_latents[k]

																									UpperCAmelCase_     : Optional[int]         =					torch.cat([model_input] * 2				) if do_classifier_free_guidance else model_input

																									UpperCAmelCase_     : Tuple         =					self.unet(__magic_name__    ,       __magic_name__    ,       encoder_hidden_states=__magic_name__				).sample

																									if do_classifier_free_guidance:
																															UpperCAmelCase_  ,    UpperCAmelCase_     : Dict         =					noise_pred.chunk(2				)
																															UpperCAmelCase_     : List[Any]         =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									UpperCAmelCase_  ,    UpperCAmelCase_     : int         =					self.denoise_step(
																									    base=__magic_name__    ,       model_input=__magic_name__    ,       model_output=__magic_name__    ,       timestep=__magic_name__    ,       )

																									UpperCAmelCase_     : Any         =					model_input

																			UpperCAmelCase_     : Optional[Any]         =					self.denoise_mixing_layer(x=coupled_latents[0]    ,       y=coupled_latents[1]				)

													# either one is fine
													UpperCAmelCase_     : List[Any]         =					coupled_latents[0]

													if output_type not in ["latent", "pt", "np", "pil"]:
																			UpperCAmelCase_     : List[Any]         =					(
																			    F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
																			    '''`pil`, `np`, `pt`, `latent`'''
																			)
																			deprecate('''Unsupported output_type'''    ,       '''1.0.0'''    ,       __magic_name__    ,       standard_warn=__magic_name__				)
																			UpperCAmelCase_     : Any         =					'''np'''

													if output_type == "latent":
																			UpperCAmelCase_     : Tuple         =					final_latent
													else:
																			UpperCAmelCase_     : str         =					self.decode_latents(__magic_name__				)
																			UpperCAmelCase_     : Optional[Any]         =					self.image_processor.postprocess(__magic_name__    ,       output_type=__magic_name__				)

													return image








'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



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







													"""simple docstring"""
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





