




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       lowercase_		(SCREAMING_SNAKE_CASE__   ):



						def __init__( self  :      Tuple				,       snake_case__  :      AutoencoderKL				,       snake_case__  :      CLIPTextModel				,       snake_case__  :      CLIPTokenizer				,       snake_case__  :      UNetaDConditionModel				,       snake_case__  :      DDIMScheduler				,       snake_case__  :      float = 0.93				,       snake_case__  :      bool = True				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       mixing_coeff
								SCREAMING_SNAKE_CASE_							=       leapfrog_steps

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

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



						def   __a				( self  :      Any				,       snake_case__  :      str				,       snake_case__  :      Optional[str] = None				,       snake_case__  :      bool = False     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tokenizer(
								    snake_case__				,       padding='max_length'				,       max_length=self.tokenizer.model_max_length				,       truncation=snake_case__				,       return_tensors='pt'				,       )

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

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

								if do_classifier_free_guidance:
										SCREAMING_SNAKE_CASE_							=       '' if negative_prompt is None else negative_prompt

										SCREAMING_SNAKE_CASE_							=       self.tokenizer(
										    snake_case__				,       padding='max_length'				,       max_length=self.tokenizer.model_max_length				,       truncation=snake_case__				,       return_tensors='pt'				,       )

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

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

								return prompt_embeds



						def   __a				( self  :      Optional[int]				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								SCREAMING_SNAKE_CASE_							=       self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]



						def   __a				( self  :      str				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								SCREAMING_SNAKE_CASE_							=       (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]



						def   __a				( self  :      Optional[Any]				,       snake_case__  :      torch.Tensor     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       int(snake_case__     )

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

								return alpha_prod, 1 - alpha_prod



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self._get_alpha_and_beta(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self._get_alpha_and_beta(snake_case__     )

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

								SCREAMING_SNAKE_CASE_							=       (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype     )



						def   __a				( self  :      Tuple				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self._get_alpha_and_beta(snake_case__     )
								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self._get_alpha_and_beta(snake_case__     )

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

								return model_input, next_model_input.to(base.dtype     )



						@torch.no_grad()
						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      torch.Tensor     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       1 / self.vae.config.scaling_factor * latents
								SCREAMING_SNAKE_CASE_							=       self.vae.decode(snake_case__     ).sample
								SCREAMING_SNAKE_CASE_							=       (image / 2 + 0.5).clamp(0				,       1     )
								return image



						@torch.no_grad()
						def   __a				( self  :      Dict				,       snake_case__  :      Image.Image				,       snake_case__  :      torch.Tensor				,       snake_case__  :      torch.Tensor				,       snake_case__  :      float				,       snake_case__  :      Optional[torch.Generator] = None				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       guidance_scale > 1.0

								SCREAMING_SNAKE_CASE_							=       image.to(device=self.device				,       dtype=text_embeds.dtype     )
								SCREAMING_SNAKE_CASE_							=       self.vae.encode(snake_case__     ).latent_dist.sample(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       self.vae.config.scaling_factor * latent

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

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

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

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

												SCREAMING_SNAKE_CASE_							=       coupled_latents[j]
												SCREAMING_SNAKE_CASE_							=       coupled_latents[k]

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

												SCREAMING_SNAKE_CASE_							=       self.unet(snake_case__				,       snake_case__				,       encoder_hidden_states=snake_case__     ).sample

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

												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.noise_step(
												    base=snake_case__				,       model_input=snake_case__				,       model_output=snake_case__				,       timestep=snake_case__				,       )

												SCREAMING_SNAKE_CASE_							=       model_input

								return coupled_latents






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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       guidance_scale > 1.0

								SCREAMING_SNAKE_CASE_							=       self.image_processor.preprocess(snake_case__     )

								SCREAMING_SNAKE_CASE_							=       self._encode_prompt(snake_case__				,       snake_case__				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       self._encode_prompt(snake_case__				,       snake_case__				,       snake_case__     )

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

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

								SCREAMING_SNAKE_CASE_							=       self.prepare_latents(snake_case__				,       snake_case__				,       snake_case__				,       snake_case__				,       snake_case__     )

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

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

												SCREAMING_SNAKE_CASE_							=       coupled_latents[j]
												SCREAMING_SNAKE_CASE_							=       coupled_latents[k]

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

												SCREAMING_SNAKE_CASE_							=       self.unet(snake_case__				,       snake_case__				,       encoder_hidden_states=snake_case__     ).sample

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

												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.denoise_step(
												    base=snake_case__				,       model_input=snake_case__				,       model_output=snake_case__				,       timestep=snake_case__				,       )

												SCREAMING_SNAKE_CASE_							=       model_input

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

								# either one is fine
								SCREAMING_SNAKE_CASE_							=       coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
										SCREAMING_SNAKE_CASE_							=       (
										    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'				,       snake_case__				,       standard_warn=snake_case__     )
										SCREAMING_SNAKE_CASE_							=       'np'

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

								return image











from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()