

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_       :				int		   =      None


UpperCAmelCase_       :				List[Any]		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				Tuple		   =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_       :				Optional[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''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      str    				=							VOCAB_FILES_NAMES
	_lowercase		:      List[str]    				=							PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:      Tuple    				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:      Union[str, Any]    				=							['''input_ids''', '''attention_mask''']
	_lowercase		:      int    				=							XLMRobertaTokenizer




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

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

		__A   : Optional[int]								= vocab_file
		__A   : Dict								= False if not self.vocab_file else True




	def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		__A   : str								= [self.cls_token_id]
		__A   : List[str]								= [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    List[int]				,			__A	:    Optional[List[int]] = None       ):
		__A   : Optional[int]								= [self.sep_token_id]
		__A   : List[Any]								= [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 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    str				,			__A	:    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(__A       ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
			return
		__A   : List[str]								= os.path.join(
		    __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

		return (out_vocab_file,)








# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

	# setable values
	_lowercase		:      jnp.ndarray
	_lowercase		:      jnp.ndarray
	_lowercase		:      Optional[int]    				=							None




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= CommonSchedulerState.create(self       )

		# At every step in ddim, we are looking into the previous alphas_cumprod
		# For the final step, there is no previous alphas_cumprod because we are already at 0
		# `set_alpha_to_one` decides whether we set this parameter simply to one or
		# whether we use the final alpha of the "non-previous" one.
		__A   : Tuple								= (
		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
		)

		# standard deviation of the initial noise distribution
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]

		return DDIMSchedulerState.create(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
		# creates integer timesteps by multiplying by ratio
		# rounding to avoid issues when num_inference_step is power of 3
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

		return state.replace(
		    num_inference_steps=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		if state.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		if self.config.prediction_type == "epsilon":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
			    """ `v_prediction`"""       )

		# 4. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps





