






# 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        _SCREAMING_SNAKE_CASE			:
	snake_case__				:	CommonSchedulerState
	snake_case__				:	jnp.ndarray

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





	@classmethod
	def 		_A					(						cls							:   List[str]					,    __lowerCamelCase							:   CommonSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    ):
		return cls(
		    common=__lowerCamelCase					,    final_alpha_cumprod=__lowerCamelCase					,    init_noise_sigma=__lowerCamelCase					,    timesteps=__lowerCamelCase					,    )

@dataclass
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	DDIMSchedulerState

class        _SCREAMING_SNAKE_CASE			(     _a						,  _a ):
	snake_case__				:	Any	      =    [e.name for e in FlaxKarrasDiffusionSchedulers]

	snake_case__				:	jnp.dtype





	@property
	def 		_A					(						self							:   Optional[int]      ):
		return True





	@register_to_config
	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   int = 1_000					,    __lowerCamelCase							:   float = 0.0001					,    __lowerCamelCase							:   float = 0.02					,    __lowerCamelCase							:   str = "linear"					,    __lowerCamelCase							:   Optional[jnp.ndarray] = None					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   int = 0					,    __lowerCamelCase							:   str = "epsilon"					,    __lowerCamelCase							:   jnp.dtype = jnp.floataa					,    ):
		UpperCamelCase	:str		=      dtype





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   Optional[CommonSchedulerState] = None      ):
		if common is None:
			UpperCamelCase	:Union[str, 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.
		UpperCamelCase	:Optional[Any]		=      (
		    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
		UpperCamelCase	:Dict		=      jnp.array(1.0					,    dtype=self.dtype      )

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

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





	def 		_A					(						self							:   int					,    __lowerCamelCase							:   DDIMSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   Optional[int] = None      ):
		return sample





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   DDIMSchedulerState					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Tuple = ()      ):
		UpperCamelCase	:Optional[int]		=      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
		UpperCamelCase	:Optional[int]		=      (jnp.arange(0					,    __lowerCamelCase      ) * step_ratio).round()[::-1] + self.config.steps_offset

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





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   DDIMSchedulerState					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[int]      ):
		UpperCamelCase	:str		=      state.common.alphas_cumprod[timestep]
		UpperCamelCase	:Union[str, Any]		=      jnp.where(
		    prev_timestep >= 0					,    state.common.alphas_cumprod[prev_timestep]					,    state.final_alpha_cumprod      )
		UpperCamelCase	:Dict		=      1 - alpha_prod_t
		UpperCamelCase	:List[str]		=      1 - alpha_prod_t_prev

		UpperCamelCase	:Any		=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   DDIMSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   float = 0.0					,    __lowerCamelCase							:   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)
		UpperCamelCase	:Any		=      timestep - self.config.num_train_timesteps // state.num_inference_steps

		UpperCamelCase	:str		=      state.common.alphas_cumprod
		UpperCamelCase	:List[str]		=      state.final_alpha_cumprod

		# 2. compute alphas, betas
		UpperCamelCase	:Tuple		=      alphas_cumprod[timestep]
		UpperCamelCase	:List[str]		=      jnp.where(prev_timestep >= 0					,    alphas_cumprod[prev_timestep]					,    __lowerCamelCase      )

		UpperCamelCase	:List[str]		=      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":
			UpperCamelCase	:int		=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			UpperCamelCase	:int		=      model_output
		elif self.config.prediction_type == "sample":
			UpperCamelCase	:Optional[int]		=      model_output
			UpperCamelCase	:Union[str, Any]		=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			UpperCamelCase	:Optional[int]		=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			UpperCamelCase	:Any		=      (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)
		UpperCamelCase	:Optional[Any]		=      self._get_variance(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )
		UpperCamelCase	:Optional[Any]		=      eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		UpperCamelCase	:Tuple		=      (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
		UpperCamelCase	:Optional[Any]		=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__lowerCamelCase					,    state=__lowerCamelCase      )





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





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   DDIMSchedulerState					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    __lowerCamelCase							:   jnp.ndarray					,    ):
		return get_velocity_common(state.common					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )





	def __len__(						self							:   Union[str, Any]      ):
		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,)
