




'''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


lowercase      :   Dict      =       """scheduler_config.json"""







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     int          =					1
     lowerCAmelCase_  :     Dict          =					2
     lowerCAmelCase_  :     Dict          =					3
     lowerCAmelCase_  :     Any          =					4
     lowerCAmelCase_  :     Tuple          =					5







@dataclass
class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     jnp.ndarray







class 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					SCHEDULER_CONFIG_NAME
     lowerCAmelCase_  :     Optional[Any]          =					["""dtype"""]
     lowerCAmelCase_  :     Union[str, Any]          =					[]
     lowerCAmelCase_  :     str          =					True







     @classmethod
     def 			snake_case_      (				cls							,__a = None							,__a = None							,__a=False							,**__a							,)      ->				List[Any]:
          snake_case ,		snake_case			:      Dict        =			cls.load_config(
              pretrained_model_name_or_path=__a							,subfolder=__a							,return_unused_kwargs=__a							,**__a							,)
          snake_case ,		snake_case			:      str        =			cls.from_config(__a							,return_unused_kwargs=__a							,**__a							)

          if hasattr(__a							,"""create_state"""							) and getattr(__a							,"""has_state"""							,__a							):
               snake_case			:      List[str]        =			scheduler.create_state()

          if return_unused_kwargs:
               return scheduler, state, unused_kwargs

          return scheduler, state







     def 			snake_case_      (				self							,__a							,__a = False							,**__a							)      ->				Optional[Any]:
          self.save_config(save_directory=__a							,push_to_hub=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				str:
          return self._get_compatibles()







     @classmethod
     def 			snake_case_      (				cls							)      ->				List[str]:
          snake_case			:      Optional[Any]        =			list(set([cls.__name__] + cls._compatibles							)							)
          snake_case			:      Union[str, Any]        =			importlib.import_module(__name__.split("""."""							)[0]							)
          snake_case			:      int        =			[
              getattr(__a							,__a							) for c in compatible_classes_str if hasattr(__a							,__a							)
          ]
          return compatible_classes



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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase=0.999     ,   __lowercase=jnp.floataa				):

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

     snake_case			:      Tuple        =			[]
     for i in range(__lowercase				):
          snake_case			:      List[str]        =			i / num_diffusion_timesteps
          snake_case			:      Any        =			(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 					_a :





     '''simple docstring'''



     lowerCAmelCase_  :     jnp.ndarray
     lowerCAmelCase_  :     jnp.ndarray
     lowerCAmelCase_  :     jnp.ndarray







     @classmethod
     def 			snake_case_      (				cls							,__a							)      ->				Tuple:
          snake_case			:      Any        =			scheduler.config

          if config.trained_betas is not None:
               snake_case			:      Any        =			jnp.asarray(config.trained_betas							,dtype=scheduler.dtype							)
          elif config.beta_schedule == "linear":
               snake_case			:      Optional[int]        =			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.
               snake_case			:      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
               snake_case			:      List[Any]        =			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__}'''							)

          snake_case			:      Optional[Any]        =			1.0 - betas

          snake_case			:      Union[str, Any]        =			jnp.cumprod(__a							,axis=0							)

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[int]        =			state.alphas_cumprod

     snake_case			:      Union[str, Any]        =			alphas_cumprod[timesteps] ** 0.5
     snake_case			:      Optional[Any]        =			sqrt_alpha_prod.flatten()
     snake_case			:      Any        =			broadcast_to_shape_from_left(__lowercase     ,   original_samples.shape				)

     snake_case			:      Tuple        =			(1 - alphas_cumprod[timesteps]) ** 0.5
     snake_case			:      List[str]        =			sqrt_one_minus_alpha_prod.flatten()
     snake_case			:      Any        =			broadcast_to_shape_from_left(__lowercase     ,   original_samples.shape				)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case ,		snake_case			:      Optional[int]        =			get_sqrt_alpha_prod(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)
     snake_case			:      Optional[Any]        =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples







def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case ,		snake_case			:      List[str]        =			get_sqrt_alpha_prod(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)
     snake_case			:      List[str]        =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Optional[int]      =       {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   int      =       [
          """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PegasusXForConditionalGeneration""",
          """PegasusXModel""",
          """PegasusXPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_pegasus_x import (
                PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                PegasusXForConditionalGeneration,
                PegasusXModel,
                PegasusXPreTrainedModel,
            )


else:
      import sys

      lowercase      :   Optional[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



