


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


__snake_case   							=						'''scheduler_config.json'''



class 	__lowerCamelCase			(_a						):
       _lowercase											=     1
       _lowercase											=     2
       _lowercase											=     3
       _lowercase											=     4
       _lowercase											=     5



@dataclass
class 	__lowerCamelCase			(_a						):
       _lowercase											=     42



class 	__lowerCamelCase			:
       _lowercase											=     SCHEDULER_CONFIG_NAME
       _lowercase											=     ["""dtype"""]
       _lowercase											=     []
       _lowercase											=     True







       @classmethod
       def 		snake_case_	(			cls: Any,A_: Dict[str, Any] = None,A_: Optional[str] = None,A_: Union[str, Any]=False,**A_: List[str],):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			cls.load_config(
                pretrained_model_name_or_path=A_,subfolder=A_,return_unused_kwargs=A_,**A_,)
            __UpperCamelCase,    __UpperCamelCase =			cls.from_config(A_,return_unused_kwargs=A_,**A_					)

            if hasattr(A_,'create_state'					) and getattr(A_,'has_state',A_					):
                 __UpperCamelCase =			scheduler.create_state()

            if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

            return scheduler, state







       def 		snake_case_	(			self: str,A_: Union[str, os.PathLike],A_: bool = False,**A_: Any					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self._get_compatibles()







       @classmethod
       def 		snake_case_	(			cls: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			list(set([cls.__name__] + cls._compatibles					)					)
            __UpperCamelCase =			importlib.import_module(__name__.split('.'					)[0]					)
            __UpperCamelCase =			[
                getattr(A_,A_					) for c in compatible_classes_str if hasattr(A_,A_					)
            ]
            return compatible_classes






def 		_A		(		_lowercase					,  _lowercase )					-> jnp.ndarray:


     """simple docstring"""


     assert len(_lowercase ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowercase ) - x.ndim) )					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase=0.9_99					,  _lowercase=jnp.floataa )					-> jnp.ndarray:


     """simple docstring"""


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

     __UpperCamelCase =			[]
     for i in range(_lowercase ):
          __UpperCamelCase =			i / num_diffusion_timesteps
          __UpperCamelCase =			(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(_lowercase ) / alpha_bar(_lowercase )					,  _lowercase ) )
     return jnp.array(_lowercase					,  dtype=_lowercase )



@flax.struct.dataclass
class 	__lowerCamelCase			:
       _lowercase											=     42
       _lowercase											=     42
       _lowercase											=     42







       @classmethod
       def 		snake_case_	(			cls: Any,A_: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			scheduler.config

            if config.trained_betas is not None:
                 __UpperCamelCase =			jnp.asarray(config.trained_betas,dtype=scheduler.dtype					)
            elif config.beta_schedule == "linear":
                 __UpperCamelCase =			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.
                 __UpperCamelCase =			(
                     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
                 __UpperCamelCase =			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__}'''					)

            __UpperCamelCase =			1.0 - betas

            __UpperCamelCase =			jnp.cumprod(A_,axis=0					)

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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			state.alphas_cumprod

     __UpperCamelCase =			alphas_cumprod[timesteps] ** 0.5
     __UpperCamelCase =			sqrt_alpha_prod.flatten()
     __UpperCamelCase =			broadcast_to_shape_from_left(_lowercase					,  original_samples.shape )

     __UpperCamelCase =			(1 - alphas_cumprod[timesteps]) ** 0.5
     __UpperCamelCase =			sqrt_one_minus_alpha_prod.flatten()
     __UpperCamelCase =			broadcast_to_shape_from_left(_lowercase					,  original_samples.shape )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase =			get_sqrt_alpha_prod(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     __UpperCamelCase =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase,    __UpperCamelCase =			get_sqrt_alpha_prod(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     __UpperCamelCase =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity










'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["pixel_values"]




      def __init__(	self			, lowerCamelCase = True			, lowerCamelCase = 32			, lowerCamelCase=PILImageResampling.BILINEAR			, lowerCamelCase = True			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         __a					  =     do_resize
         __a					  =     do_rescale
         __a					  =     size_divisor
         __a					  =     resample
         super().__init__(**lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         __a  ,      __a					  =     get_image_size(lowerCamelCase   )
         # Rounds the height and width down to the closest multiple of size_divisor
         __a					  =     height // size_divisor * size_divisor
         __a					  =     width // size_divisor * size_divisor
         __a					  =     resize(lowerCamelCase			, (new_h, new_w)			, resample=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )
         return image




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         return rescale(image=lowerCamelCase			, scale=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase=None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = ChannelDimension.FIRST			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     do_resize if do_resize is not None else self.do_resize
         __a					  =     do_rescale if do_rescale is not None else self.do_rescale
         __a					  =     size_divisor if size_divisor is not None else self.size_divisor
         __a					  =     resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'   )

         __a					  =     make_list_of_images(lowerCamelCase   )

         if not valid_images(lowerCamelCase   ):
            raise ValueError('Invalid image(s)'   )

         # All transformations expect numpy arrays.
         __a					  =     [to_numpy_array(lowerCamelCase   ) for img in images]

         if do_resize:
            __a					  =     [self.resize(lowerCamelCase			, size_divisor=lowerCamelCase			, resample=lowerCamelCase   ) for image in images]

         if do_rescale:
            __a					  =     [self.rescale(lowerCamelCase			, scale=1 / 255   ) for image in images]

         __a					  =     [to_channel_dimension_format(lowerCamelCase			, lowerCamelCase   ) for image in images]

         __a					  =     {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase			, tensor_type=lowerCamelCase   )