



'''simple docstring'''






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


lowerCAmelCase: Tuple							=						"scheduler_config.json"

class 							a__( __lowerCAmelCase     ):
    lowercase__			=   1
    lowercase__			=   2
    lowercase__			=   3
    lowercase__			=   4
    lowercase__			=   5

@dataclass
class 							a__( __lowerCAmelCase     ):
    lowercase__			=   42

class 							a__:
    lowercase__			=   SCHEDULER_CONFIG_NAME
    lowercase__			=   ["dtype"]
    lowercase__			=   []
    lowercase__			=   True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  Dict[str, Any] = None						,      __snake_case			:  Optional[str] = None						,      __snake_case			:  int=False						,      **__snake_case			:  Optional[int]						,      ):
      a						: Optional[Any]					       =					cls.load_config(
          pretrained_model_name_or_path=lowerCamelCase__						,      subfolder=lowerCamelCase__						,      return_unused_kwargs=lowerCamelCase__						,      **lowerCamelCase__						,      )
      a						: Dict					       =					cls.from_config(lowerCamelCase__						,      return_unused_kwargs=lowerCamelCase__						,      **lowerCamelCase__     )

      if hasattr(lowerCamelCase__						,      'create_state'     ) and getattr(lowerCamelCase__						,      'has_state'						,      lowerCamelCase__     ):
        a						: Dict					       =					scheduler.create_state()

      if return_unused_kwargs:
        return scheduler, state, unused_kwargs

      return scheduler, state







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Union[str, os.PathLike]						,      __snake_case			:  bool = False						,      **__snake_case			:  Optional[Any]     ):
      self.save_config(save_directory=lowerCamelCase__						,      push_to_hub=lowerCamelCase__						,      **lowerCamelCase__     )







    @property
    def       lowercase_	(				self			:  List[str]     ):
      return self._get_compatibles()







    @classmethod
    def       lowercase_	(				cls			:  Optional[int]     ):
      a						: str					       =					list(set([cls.__name__] + cls._compatibles     )     )
      a						: Union[str, Any]					       =					importlib.import_module(__name__.split('.'     )[0]     )
      a						: int					       =					[
          getattr(lowerCamelCase__						,      lowerCamelCase__     ) for c in compatible_classes_str if hasattr(lowerCamelCase__						,      lowerCamelCase__     )
      ]
      return compatible_classes

def 				lowerCamelCase__	(						_A			,      _A    ):
  assert len(_A    ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_A    ) - x.ndim)    )			,      _A    )
def 				lowerCamelCase__	(						_A			,      _A=0.999			,      _A=jnp.floataa    ):



  def alpha_bar(_A    ):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2    ) ** 2

  a						: Union[str, Any]					       =					[]
  for i in range(_A    ):
    a						: Union[str, Any]					       =					i / num_diffusion_timesteps
    a						: Union[str, Any]					       =					(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__:
    lowercase__			=   42
    lowercase__			=   42
    lowercase__			=   42







    @classmethod
    def       lowercase_	(				cls			:  List[Any]						,      __snake_case			:  Union[str, Any]     ):
      a						: List[Any]					       =					scheduler.config

      if config.trained_betas is not None:
        a						: Tuple					       =					jnp.asarray(config.trained_betas						,      dtype=scheduler.dtype     )
      elif config.beta_schedule == "linear":
        a						: Dict					       =					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						: Optional[Any]					       =					(
            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						: int					       =					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						: Optional[int]					       =					jnp.cumprod(lowerCamelCase__						,      axis=0     )

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

def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: Optional[Any]					       =					state.alphas_cumprod

  a						: Optional[int]					       =					alphas_cumprod[timesteps] ** 0.5
  a						: List[Any]					       =					sqrt_alpha_prod.flatten()
  a						: Union[str, Any]					       =					broadcast_to_shape_from_left(_A			,      original_samples.shape    )

  a						: Any					       =					(1 - alphas_cumprod[timesteps]) ** 0.5
  a						: str					       =					sqrt_one_minus_alpha_prod.flatten()
  a						: Optional[Any]					       =					broadcast_to_shape_from_left(_A			,      original_samples.shape    )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: Dict					       =					get_sqrt_alpha_prod(_A			,      _A			,      _A			,      _A    )
  a						: Union[str, Any]					       =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples

def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: Dict					       =					get_sqrt_alpha_prod(_A			,      _A			,      _A			,      _A    )
  a						: int					       =					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()