






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 							__a							(  __A						):





		'''simple docstring'''



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






@dataclass
class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				jnp.ndarray






class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						SCHEDULER_CONFIG_NAME
		UpperCAmelCase__      :				List[Any]				    =						["""dtype"""]
		UpperCAmelCase__      :				Optional[Any]				    =						[]
		UpperCAmelCase__      :				int				    =						True


		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__=False ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   cls.load_config(
						    pretrained_model_name_or_path=UpperCamelCase__ ,    subfolder=UpperCamelCase__ ,    return_unused_kwargs=UpperCamelCase__ ,    **UpperCamelCase__ ,    )
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   cls.from_config(UpperCamelCase__ ,    return_unused_kwargs=UpperCamelCase__ ,    **UpperCamelCase__       )

						if hasattr(UpperCamelCase__ ,    'create_state'       ) and getattr(UpperCamelCase__ ,    'has_state' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scheduler.create_state()

						if return_unused_kwargs:
										return scheduler, state, unused_kwargs

						return scheduler, state


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = False ,    **UpperCamelCase__       ):
						self.save_config(save_directory=UpperCamelCase__ ,    push_to_hub=UpperCamelCase__ ,    **UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self._get_compatibles()






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






def 					_lowerCamelCase(			lowerCAmelCase__				:		jnp.ndarray    ,    lowerCAmelCase__				:		Tuple[int]      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		Any=0.999    ,    lowerCAmelCase__				:		Union[str, Any]=jnp.floataa      ):





				'''simple docstring'''







				def alpha_bar(lowerCAmelCase__				:		Optional[Any]      ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2      ) ** 2

				SCREAMING_SNAKE_CASE_   :	str													=   []
				for i in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (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							:





		'''simple docstring'''



		UpperCAmelCase__      :				jnp.ndarray
		UpperCAmelCase__      :				jnp.ndarray
		UpperCAmelCase__      :				jnp.ndarray


		@classmethod
		def   __snake_case			(						cls ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   scheduler.config

						if config.trained_betas is not None:
										SCREAMING_SNAKE_CASE_   :	Dict													=   jnp.asarray(config.trained_betas ,    dtype=scheduler.dtype       )
						elif config.beta_schedule == "linear":
										SCREAMING_SNAKE_CASE_   :	str													=   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_   :	Optional[int]													=   (
										    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_   :	str													=   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_   :	Dict													=   1.0 - betas

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   jnp.cumprod(UpperCamelCase__ ,    axis=0       )

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






def 					_lowerCamelCase(			lowerCAmelCase__				:		CommonSchedulerState    ,    lowerCAmelCase__				:		jnp.ndarray    ,    lowerCAmelCase__				:		jnp.ndarray    ,    lowerCAmelCase__				:		jnp.ndarray      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   state.alphas_cumprod

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

				SCREAMING_SNAKE_CASE_   :	str													=   (1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   sqrt_one_minus_alpha_prod.flatten()
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   get_sqrt_alpha_prod(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   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      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   get_sqrt_alpha_prod(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""Translation""" ,	init=__A ,	repr=__A						)


		def __call__(						self       ):
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






		def   __snake_case			(						self       ):
						from .features import Value

						return {k: Value('string'       ) for k in sorted(self.languages       )}






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[List]				    =						None
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""TranslationVariableLanguages""" ,	init=__A ,	repr=__A						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(self.languages       ) if self.languages else None


		def __call__(						self       ):
						return pa.struct({'language': pa.list_(pa.string()       ), 'translation': pa.list_(pa.string()       )}       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for lang, text in translation_dict.items():
										if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														translation_tuples.append((lang, text)       )
										else:
														translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

						return {"language": languages, "translation": translations}






		def   __snake_case			(						self       ):
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'       )       ),
						    "translation": Sequence(Value('string'       )       ),
						}