
'''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


lowerCAmelCase					:    int           = 'scheduler_config.json'


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							1
				lowerCAmelCase_     			=							2
				lowerCAmelCase_     			=							3
				lowerCAmelCase_     			=							4
				lowerCAmelCase_     			=							5


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							42


class 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							SCHEDULER_CONFIG_NAME
				lowerCAmelCase_     			=							["""dtype"""]
				lowerCAmelCase_     			=							[]
				lowerCAmelCase_     			=							True







				@classmethod
				def 						UpperCAmelCase_    (       cls						,				A_ = None						,				A_ = None						,				A_=False						,				**A_						,				)->		Tuple:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     cls.load_config(
											    pretrained_model_name_or_path=A_						,				subfolder=A_						,				return_unused_kwargs=A_						,				**A_						,				)
											UpperCamelCase			,	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 						UpperCAmelCase_    (       self						,				A_						,				A_ = False						,				**A_     )->		Any:







											'''simple docstring'''






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







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											return self._get_compatibles()







				@classmethod
				def 						UpperCAmelCase_    (       cls     )->		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_(     A						:		jnp.ndarray		,     A						:		Tuple[int]):
							assert len(A) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A) - x.ndim))		,     A)







def 		A_(     A						:		int		,     A						:		int=0.999		,     A						:		List[Any]=jnp.floataa):

							def alpha_bar(A						:		Union[str, Any]):
														return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2

							UpperCamelCase   			=     []
							for i in range(A):
														UpperCamelCase   			=     i / num_diffusion_timesteps
														UpperCamelCase   			=     (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 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42







				@classmethod
				def 						UpperCAmelCase_    (       cls						,				A_     )->		str:







											'''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_(     A						:		CommonSchedulerState		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray):
							UpperCamelCase   			=     state.alphas_cumprod

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

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

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 		A_(     A						:		CommonSchedulerState		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray):
							UpperCamelCase			,	UpperCamelCase   			=     get_sqrt_alpha_prod(A		,     A		,     A		,     A)
							UpperCamelCase   			=     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def 		A_(     A						:		CommonSchedulerState		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray		,     A						:		jnp.ndarray):
							UpperCamelCase			,	UpperCamelCase   			=     get_sqrt_alpha_prod(A		,     A		,     A		,     A)
							UpperCamelCase   			=     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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase					:    Tuple           = TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[int]           = [
						    'MT5EncoderModel',
						    'MT5ForConditionalGeneration',
						    'MT5ForQuestionAnswering',
						    'MT5Model',
						    'MT5PreTrainedModel',
						    'MT5Stack',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Dict           = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




