





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase      :       Union[str, Any]									=  'scheduler_config.json'





class  A__    (							_lowerCamelCase	):




   """simple docstring"""



   __A   :				Dict         =  1
   __A   :				Tuple         =  2
   __A   :				Optional[int]         =  3
   __A   :				Optional[Any]         =  4
   __A   :				int         =  5





@dataclass
class  A__    (							_lowerCamelCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  4_2





class  A__    :




   """simple docstring"""



   __A   :				Dict         =  SCHEDULER_CONFIG_NAME
   __A   :				Any         =  ['''dtype''']
   __A   :				Optional[int]         =  []
   __A   :				int         =  True

   @classmethod
   def 							__lowercase		(					cls					,  lowercase = None					,  lowercase = None					,  lowercase=False					,  **lowercase					,  )			->						str:







          '''simple docstring'''

          a__ ,							a__						:	str =				cls.load_config(
              pretrained_model_name_or_path=_UpperCamelCase					,  subfolder=_UpperCamelCase					,  return_unused_kwargs=_UpperCamelCase					,  **_UpperCamelCase					,  )
          a__ ,							a__						:	Any =				cls.from_config(_UpperCamelCase					,  return_unused_kwargs=_UpperCamelCase					,  **_UpperCamelCase)

          if hasattr(_UpperCamelCase					,  'create_state') and getattr(_UpperCamelCase					,  'has_state'					,  _UpperCamelCase):
                 a__						:	Dict =				scheduler.create_state()

          if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

          return scheduler, state

   def 							__lowercase		(					self					,  lowercase					,  lowercase = False					,  **lowercase)			->						str:







          '''simple docstring'''

          self.save_config(save_directory=_UpperCamelCase					,  push_to_hub=_UpperCamelCase					,  **_UpperCamelCase)

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







          '''simple docstring'''

          return self._get_compatibles()

   @classmethod
   def 							__lowercase		(					cls)			->						str:







          '''simple docstring'''

          a__						:	Tuple =				list(set([cls.__name__] + cls._compatibles))
          a__						:	Optional[int] =				importlib.import_module(__name__.split('.')[0])
          a__						:	Dict =				[
              getattr(_UpperCamelCase					,  _UpperCamelCase) for c in compatible_classes_str if hasattr(_UpperCamelCase					,  _UpperCamelCase)
          ]
          return compatible_classes



def A_	(		A__		,      A__				)  ->		jnp.ndarray:
       assert len(A__				) >= x.ndim
       return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A__				) - x.ndim)				)		,      A__				)



def A_	(		A__		,      A__=0.9_99		,      A__=jnp.floataa				)  ->		jnp.ndarray:






       def alpha_bar(A__				):
              return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

       a__						:	Tuple =				[]
       for i in range(A__				):
              a__						:	Optional[Any] =				i / num_diffusion_timesteps
              a__						:	List[str] =				(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar(A__				) / alpha_bar(A__				)		,      A__				)				)
       return jnp.array(A__		,      dtype=A__				)





@flax.struct.dataclass
class  A__    :




   """simple docstring"""



   __A   :				Union[str, Any]         =  4_2
   __A   :				str         =  4_2
   __A   :				List[Any]         =  4_2

   @classmethod
   def 							__lowercase		(					cls					,  lowercase)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				scheduler.config

          if config.trained_betas is not None:
                 a__						:	str =				jnp.asarray(config.trained_betas					,  dtype=scheduler.dtype)
          elif config.beta_schedule == "linear":
                 a__						:	List[Any] =				jnp.linspace(config.beta_start					,  config.beta_end					,  config.num_train_timesteps					,  dtype=scheduler.dtype)
          elif config.beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 a__						:	Tuple =				(
                     jnp.linspace(
                         config.beta_start**0.5					,  config.beta_end**0.5					,  config.num_train_timesteps					,  dtype=scheduler.dtype)
                     ** 2
                 )
          elif config.beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 a__						:	str =				betas_for_alpha_bar(config.num_train_timesteps					,  dtype=scheduler.dtype)
          else:
                 raise NotImplementedError(
                     F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}')

          a__						:	Union[str, Any] =				1.0 - betas

          a__						:	Dict =				jnp.cumprod(_UpperCamelCase					,  axis=0)

          return cls(
              alphas=_UpperCamelCase					,  betas=_UpperCamelCase					,  alphas_cumprod=_UpperCamelCase					,  )



def A_	(		A__		,      A__		,      A__		,      A__				)  ->		Dict:
       a__						:	int =				state.alphas_cumprod

       a__						:	Tuple =				alphas_cumprod[timesteps] ** 0.5
       a__						:	Union[str, Any] =				sqrt_alpha_prod.flatten()
       a__						:	Any =				broadcast_to_shape_from_left(A__		,      original_samples.shape				)

       a__						:	Optional[int] =				(1 - alphas_cumprod[timesteps]) ** 0.5
       a__						:	int =				sqrt_one_minus_alpha_prod.flatten()
       a__						:	Any =				broadcast_to_shape_from_left(A__		,      original_samples.shape				)

       return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def A_	(		A__		,      A__		,      A__		,      A__				)  ->		str:
       a__ ,							a__						:	Union[str, Any] =				get_sqrt_alpha_prod(A__		,      A__		,      A__		,      A__				)
       a__						:	Optional[Any] =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples



def A_	(		A__		,      A__		,      A__		,      A__				)  ->		List[Any]:
       a__ ,							a__						:	Union[str, Any] =				get_sqrt_alpha_prod(A__		,      A__		,      A__		,      A__				)
       a__						:	Optional[int] =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
       return velocity









from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





