



'''simple docstring'''


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(A__    ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(A__    ):
         return math.exp(t * -12.0    )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

   UpperCamelCase					=  []
   for i in range(A__    ):
      UpperCamelCase					=  i / num_diffusion_timesteps
      UpperCamelCase					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(A__    ) / alpha_bar_fn(A__    )    ,						A__    )    )
   return torch.tensor(A__    ,						dtype=torch.floataa    )

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

       UpperCamelCase					=  1.0 - self.betas
       UpperCamelCase					=  torch.cumprod(self.alphas  ,						dim=0 )
       UpperCamelCase					=  torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  torch.from_numpy(np.arange(0  ,						UpperCamelCase__ )[::-1].copy() )

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
          UpperCamelCase					=  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

          UpperCamelCase					=  (predicted_variance + 1) / 2
          UpperCamelCase					=  frac * max_log + (1 - frac) * min_log

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						sample.shape[1]  ,						dim=1 )
       else:
          UpperCamelCase					=  None

       # 1. compute alphas, betas
       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  model_output
       else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
              ' for the UnCLIPScheduler.' )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
          UpperCamelCase					=  torch.clamp(
              UpperCamelCase__  ,						-self.config.clip_sample_range  ,						self.config.clip_sample_range )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase					=  0
       if t > 0:
          UpperCamelCase					=  randn_tensor(
              model_output.shape  ,						dtype=model_output.dtype  ,						generator=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (0.5 * variance).exp()
          else:
             raise ValueError(
                 f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                 ' for the UnCLIPScheduler.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype )
       UpperCamelCase					=  timesteps.to(original_samples.device )

       UpperCamelCase					=  alphas_cumprod[timesteps] ** 0.5
       UpperCamelCase					=  sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase					=  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples






'''simple docstring'''





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

from tokenizers import processors

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


if is_sentencepiece_available():
   from .tokenization_mbart import MBartTokenizer
else:
   _UpperCamelCase											=						None


_UpperCamelCase											=						logging.get_logger(__name__)


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

_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
_UpperCamelCase											=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=["""input_ids""", """attention_mask"""]
  a_												=MBartTokenizer

  a_												=[]
  a_												=[]




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

    super().__init__(
        vocab_file=_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			,	src_lang=_a			,	tgt_lang=_a			,	additional_special_tokens=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = vocab_file
    __lowerCamelCase       :       List[str]           = False if not self.vocab_file else True

    __lowerCamelCase       :       str           = FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

    self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
    __lowerCamelCase       :       Optional[Any]           = {
        lang_code: self.convert_tokens_to_ids(_a   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    __lowerCamelCase       :       Optional[Any]           = src_lang if src_lang is not None else 'en_XX'
    __lowerCamelCase       :       int           = self.convert_tokens_to_ids(self._src_lang   )
    __lowerCamelCase       :       List[str]           = tgt_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    return self._src_lang




  @src_lang.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = new_src_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




  def 						_lowercase     ( self      :	int			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Optional[int]           = [self.sep_token_id]
    __lowerCamelCase       :       Union[str, 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 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[Any]			,	_a      :	str			,	_a      :	Optional[str]			,	_a      :	Optional[str]			,	**_a      :	Optional[int]   )					->					Any:
    if src_lang is None or tgt_lang is None:
      raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
    __lowerCamelCase       :       Optional[Any]           = src_lang
    __lowerCamelCase       :       Dict           = self(_a			,	add_special_tokens=_a			,	return_tensors=_a			,	**_a   )
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = tgt_lang_id
    return inputs




  def 						_lowercase     ( self      :	Any			,	_a      :	List[str]			,	_a      :	str = "en_XX"			,	_a      :	Optional[List[str]] = None			,	_a      :	str = "ro_RO"			,	**_a      :	Tuple			,	)					->					BatchEncoding:
    __lowerCamelCase       :       List[Any]           = src_lang
    __lowerCamelCase       :       str           = tgt_lang
    return super().prepare_seqaseq_batch(_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    return self.set_src_lang_special_tokens(self.src_lang   )




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	List[str]   )					->					None:
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       Dict           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Optional[Any]           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Union[str, Any]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       int           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       int           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Any           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Optional[int]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Any			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    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
    __lowerCamelCase       :       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,)






