

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    __UpperCAmelCase	(			lowerCamelCase__		):



   def __init__(				self				:	int,						__A				:	AutoencoderKL,						__A				:	CLIPTextModel,						__A				:	CLIPTokenizer,						__A				:	UNetaDConditionModel,						__A				:	DDIMScheduler,						__A				:	float = 0.9_3,						__A				:	bool = True,						):
     UpperCAmelCase			:				str										=					mixing_coeff
     UpperCAmelCase			:				Dict										=					leapfrog_steps

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

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



   def 			__magic_name__       (				self				:	Any,						__A				:	str,						__A				:	Optional[str] = None,						__A				:	bool = False		):
     UpperCAmelCase			:				Union[str, Any]										=					self.tokenizer(
         __A,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						truncation=__A,						return_tensors='''pt''',						)

     UpperCAmelCase			:				List[str]										=					self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

     UpperCAmelCase			:				Union[str, Any]										=					prompt_embeds.to(dtype=self.text_encoder.dtype,						device=self.device		)

     if do_classifier_free_guidance:
       UpperCAmelCase			:				List[str]										=					'''''' if negative_prompt is None else negative_prompt

       UpperCAmelCase			:				Optional[Any]										=					self.tokenizer(
           __A,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						truncation=__A,						return_tensors='''pt''',						)

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

       UpperCAmelCase			:				Optional[Any]										=					torch.cat([negative_prompt_embeds, prompt_embeds]		)

     return prompt_embeds



   def 			__magic_name__       (				self				:	List[str],						__A				:	torch.Tensor,						__A				:	torch.Tensor		):
     UpperCAmelCase			:				Dict										=					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     UpperCAmelCase			:				str										=					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	torch.Tensor,						__A				:	torch.Tensor		):
     UpperCAmelCase			:				str										=					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     UpperCAmelCase			:				int										=					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]



   def 			__magic_name__       (				self				:	Dict,						__A				:	torch.Tensor		):
     # as self.alphas_cumprod is always in cpu
     UpperCAmelCase			:				int										=					int(__A		)

     UpperCAmelCase			:				Union[str, Any]										=					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod



   def 			__magic_name__       (				self				:	List[Any],						__A				:	torch.Tensor,						__A				:	torch.Tensor,						__A				:	torch.Tensor,						__A				:	torch.Tensor,						):
     UpperCAmelCase			:				str										=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     UpperCAmelCase					,		UpperCAmelCase			:				Optional[Any]										=					self._get_alpha_and_beta(__A		)
     UpperCAmelCase					,		UpperCAmelCase			:				Union[str, Any]										=					self._get_alpha_and_beta(__A		)

     UpperCAmelCase			:				Any										=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     UpperCAmelCase			:				str										=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     UpperCAmelCase			:				Any										=					(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype		)



   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	torch.Tensor,						__A				:	torch.Tensor,						__A				:	torch.Tensor,						__A				:	torch.Tensor,						):
     UpperCAmelCase			:				Optional[Any]										=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     UpperCAmelCase					,		UpperCAmelCase			:				Optional[Any]										=					self._get_alpha_and_beta(__A		)
     UpperCAmelCase					,		UpperCAmelCase			:				Union[str, Any]										=					self._get_alpha_and_beta(__A		)

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

     return model_input, next_model_input.to(base.dtype		)



   @torch.no_grad()
   def 			__magic_name__       (				self				:	str,						__A				:	torch.Tensor		):
     UpperCAmelCase			:				str										=					1 / self.vae.config.scaling_factor * latents
     UpperCAmelCase			:				int										=					self.vae.decode(__A		).sample
     UpperCAmelCase			:				int										=					(image / 2 + 0.5).clamp(0,						1		)
     return image



   @torch.no_grad()
   def 			__magic_name__       (				self				:	Dict,						__A				:	Image.Image,						__A				:	torch.Tensor,						__A				:	torch.Tensor,						__A				:	float,						__A				:	Optional[torch.Generator] = None,						):
     UpperCAmelCase			:				List[Any]										=					guidance_scale > 1.0

     UpperCAmelCase			:				Tuple										=					image.to(device=self.device,						dtype=text_embeds.dtype		)
     UpperCAmelCase			:				Optional[Any]										=					self.vae.encode(__A		).latent_dist.sample(__A		)

     UpperCAmelCase			:				Dict										=					self.vae.config.scaling_factor * latent

     UpperCAmelCase			:				List[str]										=					[latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(__A		),						total=len(__A		)		):
       UpperCAmelCase			:				str										=					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			:				Optional[Any]										=					j ^ 1

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

         UpperCAmelCase			:				str										=					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(__A,						__A,						encoder_hidden_states=__A		).sample

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

         UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.noise_step(
             base=__A,						model_input=__A,						model_output=__A,						timestep=__A,						)

         UpperCAmelCase			:				Any										=					model_input

     return coupled_latents





   @torch.no_grad()
   def __call__(				self				:	Union[str, Any],						__A				:	str,						__A				:	str,						__A				:	Image.Image,						__A				:	float = 3.0,						__A				:	int = 5_0,						__A				:	float = 0.8,						__A				:	Optional[str] = None,						__A				:	Optional[torch.Generator] = None,						__A				:	Optional[str] = "pil",						):
     UpperCAmelCase			:				Dict										=					guidance_scale > 1.0

     UpperCAmelCase			:				int										=					self.image_processor.preprocess(__A		)

     UpperCAmelCase			:				Dict										=					self._encode_prompt(__A,						__A,						__A		)
     UpperCAmelCase			:				int										=					self._encode_prompt(__A,						__A,						__A		)

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

     UpperCAmelCase			:				Optional[Any]										=					num_inference_steps - int(num_inference_steps * strength		)
     UpperCAmelCase			:				List[Any]										=					self.scheduler.timesteps[t_limit:]
     UpperCAmelCase			:				List[Any]										=					fwd_timesteps.flip(0		)

     UpperCAmelCase			:				List[str]										=					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		):
         UpperCAmelCase			:				Optional[Any]										=					k ^ 1

         if self.leapfrog_steps:
           if i % 2 == 1:
             UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					j, k

         UpperCAmelCase			:				List[Any]										=					coupled_latents[j]
         UpperCAmelCase			:				str										=					coupled_latents[k]

         UpperCAmelCase			:				Union[str, Any]										=					torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

         UpperCAmelCase			:				Union[str, Any]										=					self.unet(__A,						__A,						encoder_hidden_states=__A		).sample

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

         UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					self.denoise_step(
             base=__A,						model_input=__A,						model_output=__A,						timestep=__A,						)

         UpperCAmelCase			:				Dict										=					model_input

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

     # either one is fine
     UpperCAmelCase			:				Optional[int]										=					coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       UpperCAmelCase			:				str										=					(
           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		)
       UpperCAmelCase			:				Optional[int]										=					'''np'''

     if output_type == "latent":
       UpperCAmelCase			:				List[Any]										=					final_latent
     else:
       UpperCAmelCase			:				int										=					self.decode_latents(__A		)
       UpperCAmelCase			:				List[Any]										=					self.image_processor.postprocess(__A,						output_type=__A		)

     return image








import timeit





class    __UpperCAmelCase	:



   def __init__(				self				:	Dict,						__A				:	Union[str, Any]		):
     if isinstance(__A,						__A		):  # Initializes a matrix identical to the one provided.
       UpperCAmelCase			:				str										=					arg
       UpperCAmelCase			:				Optional[Any]										=					len(__A		)
     else:  # Initializes a square matrix of the given size and set values to zero.
       UpperCAmelCase			:				List[str]										=					arg
       UpperCAmelCase			:				List[Any]										=					[[0 for _ in range(self.n		)] for _ in range(self.n		)]





   def __mul__(				self				:	Dict,						__A				:	List[Any]		):
     UpperCAmelCase			:				Dict										=					Matrix(self.n		)
     for i in range(self.n		):
       for j in range(self.n		):
         for k in range(self.n		):
           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
     return matrix






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase			:				Tuple										=					Matrix([[1, 0], [0, 1]]     )
  while b > 0:
    if b & 1:
      matrix *= a
    a *= a
    b >>= 1
  return matrix

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa
  UpperCAmelCase			:				List[str]										=					Matrix([[1, 1], [1, 0]]     )
  UpperCAmelCase			:				Union[str, Any]										=					modular_exponentiation(UpperCAmelCase							,					n - 2     )
  return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		Optional[Any]:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa

  UpperCAmelCase			:				str										=					fa
  UpperCAmelCase			:				List[str]										=					fa
  n -= 2

  while n > 0:
    UpperCAmelCase					,		UpperCAmelCase			:				str										=					fn_a + fn_a, fn_a
    n -= 1

  return fn_a

def      a__	(						)    ->		Any:
  UpperCAmelCase			:				int										=					'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
  UpperCAmelCase			:				Dict										=					'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				Tuple										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print('''With matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		int:
  UpperCAmelCase			:				str										=					'''
from random import randint
from __main__ import simple_fibonacci
'''
  UpperCAmelCase			:				List[str]										=					'''simple_fibonacci(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				List[str]										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print(
      '''Without matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		List[str]:
  matrix_exponentiation_time()
  simple_fibonacci_time()


if __name__ == "__main__":
     main()





