'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:Optional[jnp.ndarray]		       =						None
  lowerCamelCase_						:Optional[jnp.ndarray]		       =						None
  lowerCamelCase_						:Optional[jnp.ndarray]		       =						None


  @classmethod
  def _UpperCamelCase	(					cls		):




       '''simple docstring'''

       return cls()







@dataclass
class 	__SCREAMING_SNAKE_CASE		(			_a			):







  '''simple docstring'''






  lowerCamelCase_						:ScoreSdeVeSchedulerState
  lowerCamelCase_						:jnp.ndarray
  lowerCamelCase_						:Optional[jnp.ndarray]		       =						None







class 	__SCREAMING_SNAKE_CASE		(			_a		,      _a			):







  '''simple docstring'''






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return True


  @register_to_config
  def __init__(					self ,						snake_case_ = 2_0_0_0 ,						snake_case_ = 0.15 ,						snake_case_ = 0.01 ,						snake_case_ = 13_48.0 ,						snake_case_ = 1E-5 ,						snake_case_ = 1 ,						):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = ScoreSdeVeSchedulerState.create()
       return self.set_sigmas(
           __lowerCamelCase ,						self.config.num_train_timesteps ,						self.config.sigma_min ,						self.config.sigma_max ,						self.config.sampling_eps ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = () ,						snake_case_ = None		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCAmelCase_				:  Tuple  = jnp.linspace(1 ,						__lowerCamelCase ,						__lowerCamelCase		)
       return state.replace(timesteps=__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCAmelCase_				:  Any  = sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCAmelCase_				:  Dict  = sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
            UpperCAmelCase_				:  int  = self.set_timesteps(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase		)

       UpperCAmelCase_				:  int  = jnp.exp(jnp.linspace(jnp.log(__lowerCamelCase		) ,						jnp.log(__lowerCamelCase		) ,						__lowerCamelCase		)		)
       UpperCAmelCase_				:  List[Any]  = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

       return state.replace(discrete_sigmas=__lowerCamelCase ,						sigmas=__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       return jnp.where(timesteps == 0 ,						jnp.zeros_like(__lowerCamelCase		) ,						state.discrete_sigmas[timesteps - 1]		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ = True ,						):




       '''simple docstring'''

       if state.timesteps is None:
            raise ValueError(
                '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'		)

       UpperCAmelCase_				:  List[Any]  = timestep * jnp.ones(
           sample.shape[0] ,						)
       UpperCAmelCase_				:  Union[str, Any]  = (timestep * (len(state.timesteps		) - 1)).long()

       UpperCAmelCase_				:  str  = state.discrete_sigmas[timesteps]
       UpperCAmelCase_				:  int  = self.get_adjacent_sigma(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase		)
       UpperCAmelCase_				:  Any  = jnp.zeros_like(__lowerCamelCase		)
       UpperCAmelCase_				:  Union[str, Any]  = (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCAmelCase_				:  Dict  = diffusion.flatten()
       UpperCAmelCase_				:  str  = broadcast_to_shape_from_left(__lowerCamelCase ,						sample.shape		)
       UpperCAmelCase_				:  Tuple  = drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCAmelCase_				:  int  = random.split(__lowerCamelCase ,						num=1		)
       UpperCAmelCase_				:  Any  = random.normal(key=__lowerCamelCase ,						shape=sample.shape		)
       UpperCAmelCase_				:  Optional[int]  = sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCAmelCase_				:  int  = prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
            return (prev_sample, prev_sample_mean, state)

       return FlaxSdeVeOutput(prev_sample=__lowerCamelCase ,						prev_sample_mean=__lowerCamelCase ,						state=__lowerCamelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ = True ,						):




       '''simple docstring'''

       if state.timesteps is None:
            raise ValueError(
                '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'		)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCAmelCase_				:  Union[str, Any]  = random.split(__lowerCamelCase ,						num=1		)
       UpperCAmelCase_				:  str  = random.normal(key=__lowerCamelCase ,						shape=sample.shape		)

       # compute step size from the model_output, the noise, and the snr
       UpperCAmelCase_				:  Dict  = jnp.linalg.norm(__lowerCamelCase		)
       UpperCAmelCase_				:  Any  = jnp.linalg.norm(__lowerCamelCase		)
       UpperCAmelCase_				:  str  = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCAmelCase_				:  str  = step_size * jnp.ones(sample.shape[0]		)

       # compute corrected sample: model_output term and noise term
       UpperCAmelCase_				:  Optional[int]  = step_size.flatten()
       UpperCAmelCase_				:  Tuple  = broadcast_to_shape_from_left(__lowerCamelCase ,						sample.shape		)
       UpperCAmelCase_				:  str  = sample + step_size * model_output
       UpperCAmelCase_				:  Dict  = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
            return (prev_sample, state)

       return FlaxSdeVeOutput(prev_sample=__lowerCamelCase ,						state=__lowerCamelCase		)






  def __len__(					self		):




       '''simple docstring'''

       return self.config.num_train_timesteps














import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[int]="</s>"					,    __lowerCamelCase							:   int="</s>"					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[Any]="<unk>"					,    __lowerCamelCase							:   Union[str, Any]="<pad>"					,    __lowerCamelCase							:   Any="<mask>"					,    **__lowerCamelCase							:   List[str]					,    ):
		# Mask token behave like a normal word, i.e. include the space before it
		UpperCamelCase	:int		=      AddedToken(__lowerCamelCase					,    lstrip=__lowerCamelCase					,    rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else mask_token

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		UpperCamelCase	:int		=      [self.cls_token_id]
		UpperCamelCase	:Any		=      [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	:int		=      [self.cls_token_id]

		if token_ids_a is None:
			return len(cls + token_ids_a + sep      ) * [0]
		return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""      )

		if not os.path.isdir(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
			copyfile(self.vocab_file					,    __lowerCamelCase      )

		return (out_vocab_file,)
