

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


a__				=		'''scheduler_config.json'''

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    1
							UpperCAmelCase__       :						Dict	    =    2
							UpperCAmelCase__       :						Optional[int]	    =    3
							UpperCAmelCase__       :						Dict	    =    4
							UpperCAmelCase__       :						List[str]	    =    5



@dataclass
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    42



class 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Dict	    =    SCHEDULER_CONFIG_NAME
							UpperCAmelCase__       :						Tuple	    =    ["dtype"]
							UpperCAmelCase__       :						Optional[int]	    =    []
							UpperCAmelCase__       :						Optional[Any]	    =    True


							@classmethod
							def 				__lowercase				(			cls       ,							_a = None       ,							_a = None       ,							_a=False       ,							**_a       ,							)    ->    Any:
												_a	:    List[Any]         =		cls.load_config(
												    pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE       ,							subfolder=_SCREAMING_SNAKE_CASE       ,							return_unused_kwargs=_SCREAMING_SNAKE_CASE       ,							**_SCREAMING_SNAKE_CASE       ,							)
												_a	:    str         =		cls.from_config(_SCREAMING_SNAKE_CASE       ,							return_unused_kwargs=_SCREAMING_SNAKE_CASE       ,							**_SCREAMING_SNAKE_CASE       )

												if hasattr(_SCREAMING_SNAKE_CASE       ,							'''create_state'''       ) and getattr(_SCREAMING_SNAKE_CASE       ,							'''has_state'''       ,							_SCREAMING_SNAKE_CASE       ):
																	_a	:    str         =		scheduler.create_state()

												if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

												return scheduler, state


							def 				__lowercase				(			self       ,							_a       ,							_a = False       ,							**_a       )    ->    List[str]:
												self.save_config(save_directory=_SCREAMING_SNAKE_CASE       ,							push_to_hub=_SCREAMING_SNAKE_CASE       ,							**_SCREAMING_SNAKE_CASE       )


							@property
							def 				__lowercase				(			self       )    ->    Any:
												return self._get_compatibles()




							@classmethod
							def 				__lowercase				(			cls       )    ->    Tuple:
												_a	:    Tuple         =		list(set([cls.__name__] + cls._compatibles       )       )
												_a	:    Union[str, Any]         =		importlib.import_module(__name__.split('''.'''       )[0]       )
												_a	:    Any         =		[
												    getattr(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       )
												]
												return compatible_classes



def    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			Optional[int]	)			->     List[str]:

					"""simple docstring"""

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



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			Tuple=0.9_99		,__a      :			Optional[Any]=jnp.floataa	)			->     Any:

					"""simple docstring"""

					def alpha_bar(__a      :			List[Any]	):
										return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

					_a	:    Optional[Any]         =		[]
					for i in range(__a	):
										_a	:    Optional[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 				UpperCAmelCase_		:
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    42
							UpperCAmelCase__       :						List[Any]	    =    42
							UpperCAmelCase__       :						Any	    =    42


							@classmethod
							def 				__lowercase				(			cls       ,							_a       )    ->    Tuple:
												_a	:    Tuple         =		scheduler.config

												if config.trained_betas is not None:
																	_a	:    List[Any]         =		jnp.asarray(config.trained_betas       ,							dtype=scheduler.dtype       )
												elif config.beta_schedule == "linear":
																	_a	:    List[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.
																	_a	:    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
																	_a	:    Tuple         =		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	:    int         =		1.0 - betas

												_a	:    List[Any]         =		jnp.cumprod(_SCREAMING_SNAKE_CASE       ,							axis=0       )

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



def    __UpperCAmelCase (	__a      :			List[Any]		,__a      :			int		,__a      :			Tuple		,__a      :			List[str]	)			->     Union[str, Any]:

					"""simple docstring"""

					_a	:    List[Any]         =		state.alphas_cumprod

					_a	:    Tuple         =		alphas_cumprod[timesteps] ** 0.5
					_a	:    Optional[int]         =		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	:    Dict         =		sqrt_one_minus_alpha_prod.flatten()
					_a	:    List[str]         =		broadcast_to_shape_from_left(__a		,original_samples.shape	)

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def    __UpperCAmelCase (	__a      :			Tuple		,__a      :			Dict		,__a      :			Union[str, Any]		,__a      :			Tuple	)			->     Union[str, Any]:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		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    __UpperCAmelCase (	__a      :			Dict		,__a      :			str		,__a      :			Union[str, Any]		,__a      :			int	)			->     Tuple:

					"""simple docstring"""

					_a	:    Dict         =		get_sqrt_alpha_prod(__a		,__a		,__a		,__a	)
					_a	:    Union[str, Any]         =		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase								=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""RobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RobertaForCausalLM""",
         """RobertaForMaskedLM""",
         """RobertaForMultipleChoice""",
         """RobertaForQuestionAnswering""",
         """RobertaForSequenceClassification""",
         """RobertaForTokenClassification""",
         """RobertaModel""",
         """RobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRobertaForCausalLM""",
         """TFRobertaForMaskedLM""",
         """TFRobertaForMultipleChoice""",
         """TFRobertaForQuestionAnswering""",
         """TFRobertaForSequenceClassification""",
         """TFRobertaForTokenClassification""",
         """TFRobertaMainLayer""",
         """TFRobertaModel""",
         """TFRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """FlaxRobertaForCausalLM""",
         """FlaxRobertaForMaskedLM""",
         """FlaxRobertaForMultipleChoice""",
         """FlaxRobertaForQuestionAnswering""",
         """FlaxRobertaForSequenceClassification""",
         """FlaxRobertaForTokenClassification""",
         """FlaxRobertaModel""",
         """FlaxRobertaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roberta_fast import RobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roberta import (
              ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              RobertaForCausalLM,
              RobertaForMaskedLM,
              RobertaForMultipleChoice,
              RobertaForQuestionAnswering,
              RobertaForSequenceClassification,
              RobertaForTokenClassification,
              RobertaModel,
              RobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roberta import (
              TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRobertaForCausalLM,
              TFRobertaForMaskedLM,
              TFRobertaForMultipleChoice,
              TFRobertaForQuestionAnswering,
              TFRobertaForSequenceClassification,
              TFRobertaForTokenClassification,
              TFRobertaMainLayer,
              TFRobertaModel,
              TFRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roberta import (
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaModel,
              FlaxRobertaPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




