




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__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Tuple   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Optional[Any] = 0.93   , __lowerCamelCase							:	Union[str, Any] = True   , )   ->	Union[str, Any]:
							a       =     mixing_coeff
							a       =     leapfrog_steps

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

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





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	int = None   , __lowerCamelCase							:	Optional[int] = False				)   ->	str:
							a       =     self.tokenizer(
							    __UpperCAmelCase   , padding="max_length"   , max_length=self.tokenizer.model_max_length   , truncation=__UpperCAmelCase   , 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(
										    __UpperCAmelCase   , padding="max_length"   , max_length=self.tokenizer.model_max_length   , truncation=__UpperCAmelCase   , 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 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	Optional[int]				)   ->	Tuple:
							a       =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							a       =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Tuple				)   ->	Union[str, Any]:
							a       =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							a       =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							# as self.alphas_cumprod is always in cpu
							a       =     int(__UpperCAmelCase				)

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

							return alpha_prod, 1 - alpha_prod





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Optional[Any]   , )   ->	Any:
							a       =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							a       ,						a       =     self._get_alpha_and_beta(__UpperCAmelCase				)
							a       ,						a       =     self._get_alpha_and_beta(__UpperCAmelCase				)

							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 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Any   , )   ->	Optional[Any]:
							a       =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							a       ,						a       =     self._get_alpha_and_beta(__UpperCAmelCase				)
							a       ,						a       =     self._get_alpha_and_beta(__UpperCAmelCase				)

							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 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]				)   ->	Any:
							a       =     1 / self.vae.config.scaling_factor * latents
							a       =     self.vae.decode(__UpperCAmelCase				).sample
							a       =     (image / 2 + 0.5).clamp(0   , 1				)
							return image





				@torch.no_grad()
				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	str   , __lowerCamelCase							:	str   , __lowerCamelCase							:	Optional[Any] = None   , )   ->	int:
							a       =     guidance_scale > 1.0

							a       =     image.to(device=self.device   , dtype=text_embeds.dtype				)
							a       =     self.vae.encode(__UpperCAmelCase				).latent_dist.sample(__UpperCAmelCase				)

							a       =     self.vae.config.scaling_factor * latent

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

							for i, t in tqdm(enumerate(__UpperCAmelCase				)   , total=len(__UpperCAmelCase				)				):
										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(__UpperCAmelCase   , __UpperCAmelCase   , encoder_hidden_states=__UpperCAmelCase				).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=__UpperCAmelCase   , model_input=__UpperCAmelCase   , model_output=__UpperCAmelCase   , timestep=__UpperCAmelCase   , )

													a       =     model_input

							return coupled_latents





				@torch.no_grad()
				def __call__( self							:	int   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	List[Any] = 3.0   , __lowerCamelCase							:	Optional[Any] = 50   , __lowerCamelCase							:	Union[str, Any] = 0.8   , __lowerCamelCase							:	Union[str, Any] = None   , __lowerCamelCase							:	int = None   , __lowerCamelCase							:	Tuple = "pil"   , )   ->	Optional[int]:
							a       =     guidance_scale > 1.0

							a       =     self.image_processor.preprocess(__UpperCAmelCase				)

							a       =     self._encode_prompt(__UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase				)
							a       =     self._encode_prompt(__UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase				)

							self.scheduler.set_timesteps(__UpperCAmelCase   , 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(__UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase				)

							for i, t in tqdm(enumerate(__UpperCAmelCase				)   , total=len(__UpperCAmelCase				)				):
										# 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(__UpperCAmelCase   , __UpperCAmelCase   , encoder_hidden_states=__UpperCAmelCase				).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=__UpperCAmelCase   , model_input=__UpperCAmelCase   , model_output=__UpperCAmelCase   , timestep=__UpperCAmelCase   , )

													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"   , __UpperCAmelCase   , standard_warn=__UpperCAmelCase				)
										a       =     "np"

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

							return image








"""simple docstring"""


import warnings

from .generation import FlaxGenerationMixin







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			# warning at import time
			warnings.warn(
			    '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
			    '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''					,      __magic_name__					,      )



