



"""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 	__snake_case (				SCREAMING_SNAKE_CASE__):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_ = 0.93					,					a_ = True					,					):
											a__			 =						mixing_coeff
											a__			 =						leapfrog_steps

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

											a__			 =						2 ** (len(self.vae.config.block_out_channels						) - 1)
											a__			 =						VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)


							def _a (							self					,					a_					,					a_ = None					,					a_ = False						):
											a__			 =						self.tokenizer(
											    a_					,					padding="""max_length"""					,					max_length=self.tokenizer.model_max_length					,					truncation=a_					,					return_tensors="""pt"""					,					)

											a__			 =						self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

											a__			 =						prompt_embeds.to(dtype=self.text_encoder.dtype					,					device=self.device						)

											if do_classifier_free_guidance:
															a__			 =						"""""" if negative_prompt is None else negative_prompt

															a__			 =						self.tokenizer(
															    a_					,					padding="""max_length"""					,					max_length=self.tokenizer.model_max_length					,					truncation=a_					,					return_tensors="""pt"""					,					)

															a__			 =						self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

															a__			 =						torch.cat([negative_prompt_embeds, prompt_embeds]						)

											return prompt_embeds


							def _a (							self					,					a_					,					a_						):
											a__			 =						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
											a__			 =						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

											return [x, y]


							def _a (							self					,					a_					,					a_						):
											a__			 =						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
											a__			 =						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

											return [x, y]


							def _a (							self					,					a_						):
											a__			 =						int(a_						)

											a__			 =						self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

											return alpha_prod, 1 - alpha_prod


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					):
											a__			 =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__ ,     a__			 =						self._get_alpha_and_beta(a_						)
											a__ ,     a__			 =						self._get_alpha_and_beta(a_						)

											a__			 =						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__			 =						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

											a__			 =						(base - b_t * model_output) / a_t

											return model_input, next_model_input.to(base.dtype						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					):
											a__			 =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__ ,     a__			 =						self._get_alpha_and_beta(a_						)
											a__ ,     a__			 =						self._get_alpha_and_beta(a_						)

											a__			 =						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__			 =						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
											a__			 =						a_t * base + b_t * model_output

											return model_input, next_model_input.to(base.dtype						)


							@torch.no_grad()
							def _a (							self					,					a_						):
											a__			 =						1 / self.vae.config.scaling_factor * latents
											a__			 =						self.vae.decode(a_						).sample
											a__			 =						(image / 2 + 0.5).clamp(0					,					1						)
											return image


							@torch.no_grad()
							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_ = None					,					):
											a__			 =						guidance_scale > 1.0

											a__			 =						image.to(device=self.device					,					dtype=text_embeds.dtype						)
											a__			 =						self.vae.encode(a_						).latent_dist.sample(a_						)

											a__			 =						self.vae.config.scaling_factor * latent

											a__			 =						[latent.clone(), latent.clone()]

											for i, t in tqdm(enumerate(a_						)					,					total=len(a_						)						):
															a__			 =						self.noise_mixing_layer(x=coupled_latents[0]					,					y=coupled_latents[1]						)

															# j - model_input index, k - base index
															for j in range(2						):
																			a__			 =						j ^ 1

																			if self.leapfrog_steps:
																							if i % 2 == 0:
																											a__ ,     a__			 =						j, k

																			a__			 =						coupled_latents[j]
																			a__			 =						coupled_latents[k]

																			a__			 =						torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

																			a__			 =						self.unet(a_					,					a_					,					encoder_hidden_states=a_						).sample

																			if do_classifier_free_guidance:
																							a__ ,     a__			 =						noise_pred.chunk(2						)
																							a__			 =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__ ,     a__			 =						self.noise_step(
																			    base=a_					,					model_input=a_					,					model_output=a_					,					timestep=a_					,					)

																			a__			 =						model_input

											return coupled_latents







							@torch.no_grad()
							def __call__(							self					,					a_					,					a_					,					a_					,					a_ = 3.0					,					a_ = 50					,					a_ = 0.8					,					a_ = None					,					a_ = None					,					a_ = "pil"					,					):
											a__			 =						guidance_scale > 1.0

											a__			 =						self.image_processor.preprocess(a_						)

											a__			 =						self._encode_prompt(a_					,					a_					,					a_						)
											a__			 =						self._encode_prompt(a_					,					a_					,					a_						)

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

											a__			 =						num_inference_steps - int(num_inference_steps * strength						)
											a__			 =						self.scheduler.timesteps[t_limit:]
											a__			 =						fwd_timesteps.flip(0						)

											a__			 =						self.prepare_latents(a_					,					a_					,					a_					,					a_					,					a_						)

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

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

																			a__			 =						coupled_latents[j]
																			a__			 =						coupled_latents[k]

																			a__			 =						torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

																			a__			 =						self.unet(a_					,					a_					,					encoder_hidden_states=a_						).sample

																			if do_classifier_free_guidance:
																							a__ ,     a__			 =						noise_pred.chunk(2						)
																							a__			 =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__ ,     a__			 =						self.denoise_step(
																			    base=a_					,					model_input=a_					,					model_output=a_					,					timestep=a_					,					)

																			a__			 =						model_input

															a__			 =						self.denoise_mixing_layer(x=coupled_latents[0]					,					y=coupled_latents[1]						)

											# either one is fine
											a__			 =						coupled_latents[0]

											if output_type not in ["latent", "pt", "np", "pil"]:
															a__			 =						(
															    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"""					,					a_					,					standard_warn=a_						)
															a__			 =						"""np"""

											if output_type == "latent":
															a__			 =						final_latent
											else:
															a__			 =						self.decode_latents(a_						)
															a__			 =						self.image_processor.postprocess(a_					,					output_type=a_						)

											return image






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

