





'''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  :   Optional[Any]						      ='scheduler_config.json'

class      A       (	__lowercase						):
      _snake_case							     =1
      _snake_case							     =2
      _snake_case							     =3
      _snake_case							     =4
      _snake_case							     =5

@dataclass
class      A       (	__lowercase						):
      _snake_case							     =42

class      A       :
      _snake_case							     =SCHEDULER_CONFIG_NAME
      _snake_case							     =['''dtype''']
      _snake_case							     =[]
      _snake_case							     =True



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Union[str, Any]		,  _lowerCAmelCase:						Dict[str, Any] = None		,  _lowerCAmelCase:						Optional[str] = None		,  _lowerCAmelCase:						Dict=False		,  **_lowerCAmelCase:						Any		,  )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=cls.load_config(
             pretrained_model_name_or_path=__a		,  subfolder=__a		,  return_unused_kwargs=__a		,  **__a		,  )
         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 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						Union[str, os.PathLike]		,  _lowerCAmelCase:						bool = False		,  **_lowerCAmelCase:						int       )						->    Optional[Any]:
         '''simple docstring'''

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



      @property
      def 	lowerCAmelCase__    (	self:						str       )						->    int:
         '''simple docstring'''

         return self._get_compatibles()






      @classmethod
      def 	lowerCAmelCase__    (	cls:						int       )						->    Union[str, Any]:
         '''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__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__=0.999  ,    lowercase__=jnp.floataa  ):






   '''simple docstring'''






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

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

@flax.struct.dataclass
class      A       :
      _snake_case							     =42
      _snake_case							     =42
      _snake_case							     =42



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[Any]		,  _lowerCAmelCase:						List[str]       )						->    Optional[Any]:
         '''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__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state.alphas_cumprod

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

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

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=get_sqrt_alpha_prod(UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  )
   UpperCAmelCase_											=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=get_sqrt_alpha_prod(UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  ,    UpperCamelCase__  )
   UpperCAmelCase_											=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase  :   Optional[Any]						      ={
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[Any]						      =[
         """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GroupViTModel""",
         """GroupViTPreTrainedModel""",
         """GroupViTTextModel""",
         """GroupViTVisionModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[str]						      =[
         """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGroupViTModel""",
         """TFGroupViTPreTrainedModel""",
         """TFGroupViTTextModel""",
         """TFGroupViTVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     __lowercase  :   str						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




