




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


__SCREAMING_SNAKE_CASE 						="""scheduler_config.json"""






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







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






@dataclass
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							jnp.ndarray






class  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					SCHEDULER_CONFIG_NAME
				SCREAMING_SNAKE_CASE__					:							str	      =					["dtype"]
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					[]
				SCREAMING_SNAKE_CASE__					:							Dict	      =					True


				@classmethod
				def     _A							(							cls:   Any		,					_lowerCamelCase:   Dict[str, Any] = None		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   List[str]=False		,					**_lowerCamelCase:   int		,					):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    cls.load_config(
											    pretrained_model_name_or_path=_lowerCamelCase		,					subfolder=_lowerCamelCase		,					return_unused_kwargs=_lowerCamelCase		,					**_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    cls.from_config(_lowerCamelCase		,					return_unused_kwargs=_lowerCamelCase		,					**_lowerCamelCase		)

											if hasattr(_lowerCamelCase		,					'''create_state'''		) and getattr(_lowerCamelCase		,					'''has_state'''		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    scheduler.create_state()

											if return_unused_kwargs:
																		return scheduler, state, unused_kwargs

											return scheduler, state


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Union[str, os.PathLike]		,					_lowerCamelCase:   bool = False		,					**_lowerCamelCase:   int		):
											self.save_config(save_directory=_lowerCamelCase		,					push_to_hub=_lowerCamelCase		,					**_lowerCamelCase		)


				@property
				def     _A							(							self:   Union[str, Any]		):
											return self._get_compatibles()





				@classmethod
				def     _A							(							cls:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    list(set([cls.__name__] + cls._compatibles		)		)
											SCREAMING_SNAKE_CASE_   	=    importlib.import_module(__name__.split('''.'''		)[0]		)
											SCREAMING_SNAKE_CASE_   	=    [
											    getattr(_lowerCamelCase		,					_lowerCamelCase		) for c in compatible_classes_str if hasattr(_lowerCamelCase		,					_lowerCamelCase		)
											]
											return compatible_classes






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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=0.999  ,    _lowerCAmelCase=jnp.floataa     ):

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

							SCREAMING_SNAKE_CASE_   	=    []
							for i in range(_lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_   	=    i / num_diffusion_timesteps
														SCREAMING_SNAKE_CASE_   	=    (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  __magic_name__						:
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							jnp.ndarray
				SCREAMING_SNAKE_CASE__					:							jnp.ndarray
				SCREAMING_SNAKE_CASE__					:							jnp.ndarray


				@classmethod
				def     _A							(							cls:   str		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    scheduler.config

											if config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE_   	=    jnp.asarray(config.trained_betas		,					dtype=scheduler.dtype		)
											elif config.beta_schedule == "linear":
																		SCREAMING_SNAKE_CASE_   	=    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.
																		SCREAMING_SNAKE_CASE_   	=    (
																		    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
																		SCREAMING_SNAKE_CASE_   	=    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__}"		)

											SCREAMING_SNAKE_CASE_   	=    1.0 - betas

											SCREAMING_SNAKE_CASE_   	=    jnp.cumprod(_lowerCamelCase		,					axis=0		)

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






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    state.alphas_cumprod

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

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

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_sqrt_alpha_prod(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_sqrt_alpha_prod(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


