






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


UpperCamelCase    							=      'scheduler_config.json'







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					1
  lowercase_						:			Optional[Any]						=					2
  lowercase_						:			Dict						=					3
  lowercase_						:			List[str]						=					4
  lowercase_						:			List[str]						=					5







@dataclass
class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			jnp.ndarray







class 		_A						:
  lowercase_						:			Any						=					SCHEDULER_CONFIG_NAME
  lowercase_						:			str						=					['''dtype''']
  lowercase_						:			Any						=					[]
  lowercase_						:			Dict						=					True

  @classmethod
  def    a					(    cls						:	Union[str, Any]   ,				lowerCamelCase__						:	Dict[str, Any] = None   ,				lowerCamelCase__						:	Optional[str] = None   ,				lowerCamelCase__						:	Optional[int]=False   ,				**lowerCamelCase__						:	Dict   ,				):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					cls.load_config(
         pretrained_model_name_or_path=lowerCamelCase__   ,				subfolder=lowerCamelCase__   ,				return_unused_kwargs=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					cls.from_config(lowerCamelCase__   ,				return_unused_kwargs=lowerCamelCase__   ,				**lowerCamelCase__			)

     if hasattr(lowerCamelCase__   ,				"""create_state"""			) and getattr(lowerCamelCase__   ,				"""has_state"""   ,				lowerCamelCase__			):
        __UpperCamelCase	:			Tuple					=					scheduler.create_state()

     if return_unused_kwargs:
        return scheduler, state, unused_kwargs

     return scheduler, state

  def    a					(    self						:	str   ,				lowerCamelCase__						:	Union[str, os.PathLike]   ,				lowerCamelCase__						:	bool = False   ,				**lowerCamelCase__						:	List[str]			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     return self._get_compatibles()

  @classmethod
  def    a					(    cls						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					list(set([cls.__name__] + cls._compatibles			)			)
     __UpperCamelCase	:			Union[str, Any]					=					importlib.import_module(__name__.split("""."""			)[0]			)
     __UpperCamelCase	:			str					=					[
         getattr(lowerCamelCase__   ,				lowerCamelCase__			) for c in compatible_classes_str if hasattr(lowerCamelCase__   ,				lowerCamelCase__			)
     ]
     return compatible_classes







def 				__lowerCamelCase     (							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					Tuple[int]							)     ->  jnp.ndarray:
   assert len(__lowerCAmelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCAmelCase							) - x.ndim)							)					,							__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[Any]=0.999					,							__lowerCAmelCase							:					Optional[int]=jnp.floataa							)     ->  jnp.ndarray:

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

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







@flax.struct.dataclass
class 		_A						:
  lowercase_						:			jnp.ndarray
  lowercase_						:			jnp.ndarray
  lowercase_						:			jnp.ndarray

  @classmethod
  def    a					(    cls						:	Optional[int]   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					scheduler.config

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

     __UpperCamelCase	:			Any					=					1.0 - betas

     __UpperCamelCase	:			Any					=					jnp.cumprod(lowerCamelCase__   ,				axis=0			)

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







def 				__lowerCamelCase     (							__lowerCAmelCase							:					CommonSchedulerState					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray							)     ->  int:
   __UpperCamelCase	:			Union[str, Any]					=					state.alphas_cumprod

   __UpperCamelCase	:			List[str]					=					alphas_cumprod[timesteps] ** 0.5
   __UpperCamelCase	:			Union[str, Any]					=					sqrt_alpha_prod.flatten()
   __UpperCamelCase	:			Any					=					broadcast_to_shape_from_left(__lowerCAmelCase					,							original_samples.shape							)

   __UpperCamelCase	:			str					=					(1 - alphas_cumprod[timesteps]) ** 0.5
   __UpperCamelCase	:			int					=					sqrt_one_minus_alpha_prod.flatten()
   __UpperCamelCase	:			Union[str, Any]					=					broadcast_to_shape_from_left(__lowerCAmelCase					,							original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def 				__lowerCamelCase     (							__lowerCAmelCase							:					CommonSchedulerState					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray							)     ->  int:
   __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					get_sqrt_alpha_prod(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples



def 				__lowerCamelCase     (							__lowerCAmelCase							:					CommonSchedulerState					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray					,							__lowerCAmelCase							:					jnp.ndarray							)     ->  Any:
   __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					get_sqrt_alpha_prod(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity











def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  bool:
   if not all(x.isalpha() for x in string							):
      raise ValueError("""String must only contain alphabetic characters."""							)

   __UpperCamelCase	:			List[str]					=					sorted(string.lower()							)
   return len(__lowerCAmelCase							) == len(set(__lowerCAmelCase							)							)


if __name__ == "__main__":
       UpperCamelCase    							=      input('Enter a string ').strip()

       UpperCamelCase    							=      is_isogram(input_str)
       print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")



