




'''simple docstring'''





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

import numpy as np
import torch
from scipy import integrate

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class     UpperCamelCase					(  snake_case__					):
  a    :torch.FloatTensor
  a    :Optional[torch.FloatTensor]      					=				None






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=0.999      ,  UpperCAmelCase_="cosine"      ,  ):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    _a							:       Union[str, Any]       					=			[]
    for i in range(UpperCAmelCase_	):
        _a							:       Optional[int]       					=			i / num_diffusion_timesteps
        _a							:       Optional[Any]       					=			(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_	) / alpha_bar_fn(UpperCAmelCase_	)      ,  UpperCAmelCase_	)	)
    return torch.tensor(UpperCAmelCase_      ,  dtype=torch.floataa	)





class     UpperCamelCase					(  snake_case__			,		snake_case__					):
  a    :Any      					=				[e.name for e in KarrasDiffusionSchedulers]
  a    :Tuple      					=				1



  @register_to_config
  def __init__( self		,    __lowerCAmelCase = 1000		,    __lowerCAmelCase = 0.0001		,    __lowerCAmelCase = 0.02		,    __lowerCAmelCase = "linear"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = False		,    __lowerCAmelCase = "epsilon"		,    __lowerCAmelCase = "linspace"		,    __lowerCAmelCase = 0		,    ):







      '''simple docstring'''


      if trained_betas is not None:
          _a							:       str       					=			torch.tensor(__lowerCAmelCase		,    dtype=torch.floataa   )
      elif beta_schedule == "linear":
          _a							:       int       					=			torch.linspace(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    dtype=torch.floataa   )
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          _a							:       Any       					=			(
              torch.linspace(beta_start**0.5		,    beta_end**0.5		,    __lowerCAmelCase		,    dtype=torch.floataa   ) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          _a							:       Optional[Any]       					=			betas_for_alpha_bar(__lowerCAmelCase   )
      else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

      _a							:       Dict       					=			1.0 - self.betas
      _a							:       Any       					=			torch.cumprod(self.alphas		,    dim=0   )

      _a							:       List[Any]       					=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
      _a							:       List[str]       					=			np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
      _a							:       Optional[Any]       					=			torch.from_numpy(__lowerCAmelCase   )

      # setable values
      _a							:       Tuple       					=			None
      _a							:       List[Any]       					=			use_karras_sigmas
      self.set_timesteps(__lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       Dict       					=			[]
      _a							:       int       					=			False



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,    torch.Tensor   ):
          _a							:       int       					=			timestep.to(self.timesteps.device   )
      _a							:       Optional[Any]       					=			(self.timesteps == timestep).nonzero().item()
      _a							:       int       					=			self.sigmas[step_index]
      _a							:       Optional[int]       					=			sample / ((sigma**2 + 1) ** 0.5)
      _a							:       str       					=			True
      return sample



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      def lms_derivative(__lowerCAmelCase   ):
          _a							:       Optional[Any]       					=			1.0
          for k in range(__lowerCAmelCase   ):
              if current_order == k:
                  continue
              prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
          return prod

      _a							:       Union[str, Any]       					=			integrate.quad(__lowerCAmelCase		,    self.sigmas[t]		,    self.sigmas[t + 1]		,    epsrel=1e-4   )[0]

      return integrated_coeff



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
          _a							:       str       					=			np.linspace(0		,    self.config.num_train_timesteps - 1		,    __lowerCAmelCase		,    dtype=__lowerCAmelCase   )[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          _a							:       str       					=			self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          _a							:       List[str]       					=			(np.arange(0		,    __lowerCAmelCase   ) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase   )
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          _a							:       Optional[int]       					=			self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          _a							:       List[Any]       					=			(np.arange(self.config.num_train_timesteps		,    0		,    -step_ratio   )).round().copy().astype(__lowerCAmelCase   )
          timesteps -= 1
      else:
          raise ValueError(
              f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

      _a							:       Optional[int]       					=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
      _a							:       Optional[int]       					=			np.log(__lowerCAmelCase   )
      _a							:       Any       					=			np.interp(__lowerCAmelCase		,    np.arange(0		,    len(__lowerCAmelCase   )   )		,    __lowerCAmelCase   )

      if self.use_karras_sigmas:
          _a							:       str       					=			self._convert_to_karras(in_sigmas=__lowerCAmelCase   )
          _a							:       str       					=			np.array([self._sigma_to_t(__lowerCAmelCase		,    __lowerCAmelCase   ) for sigma in sigmas]   )

      _a							:       List[Any]       					=			np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )

      _a							:       int       					=			torch.from_numpy(__lowerCAmelCase   ).to(device=__lowerCAmelCase   )
      if str(__lowerCAmelCase   ).startswith("""mps"""   ):
          # mps does not support float64
          _a							:       List[str]       					=			torch.from_numpy(__lowerCAmelCase   ).to(__lowerCAmelCase		,    dtype=torch.floataa   )
      else:
          _a							:       int       					=			torch.from_numpy(__lowerCAmelCase   ).to(device=__lowerCAmelCase   )

      _a							:       Any       					=			[]



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Any       					=			np.log(__lowerCAmelCase   )

      # get distribution
      _a							:       Optional[Any]       					=			log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      _a							:       str       					=			np.cumsum((dists >= 0)		,    axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
      _a							:       List[str]       					=			low_idx + 1

      _a							:       Union[str, Any]       					=			log_sigmas[low_idx]
      _a							:       str       					=			log_sigmas[high_idx]

      # interpolate sigmas
      _a							:       Union[str, Any]       					=			(low - log_sigma) / (low - high)
      _a							:       Tuple       					=			np.clip(__lowerCAmelCase		,    0		,    1   )

      # transform interpolation to time range
      _a							:       Any       					=			(1 - w) * low_idx + w * high_idx
      _a							:       List[str]       					=			t.reshape(sigma.shape   )
      return t



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       float       					=			in_sigmas[-1].item()
      _a							:       float       					=			in_sigmas[0].item()

      _a							:       Tuple       					=			7.0  # 7.0 is the value used in the paper
      _a							:       Dict       					=			np.linspace(0		,    1		,    self.num_inference_steps   )
      _a							:       Optional[int]       					=			sigma_min ** (1 / rho)
      _a							:       Tuple       					=			sigma_max ** (1 / rho)
      _a							:       Dict       					=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = 4		,    __lowerCAmelCase = True		,    ):







      '''simple docstring'''


      if not self.is_scale_input_called:
          warnings.warn(
              """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
              """See `StableDiffusionPipeline` for a usage example."""   )

      if isinstance(__lowerCAmelCase		,    torch.Tensor   ):
          _a							:       Tuple       					=			timestep.to(self.timesteps.device   )
      _a							:       List[str]       					=			(self.timesteps == timestep).nonzero().item()
      _a							:       str       					=			self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          _a							:       Any       					=			sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          _a							:       Any       					=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
          _a							:       List[Any]       					=			model_output
      else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

      # 2. Convert to an ODE derivative
      _a							:       Any       					=			(sample - pred_original_sample) / sigma
      self.derivatives.append(__lowerCAmelCase   )
      if len(self.derivatives   ) > order:
          self.derivatives.pop(0   )

      # 3. Compute linear multistep coefficients
      _a							:       Optional[Any]       					=			min(step_index + 1		,    __lowerCAmelCase   )
      _a							:       int       					=			[self.get_lms_coefficient(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ) for curr_order in range(__lowerCAmelCase   )]

      # 4. Compute previous sample based on the derivatives path
      _a							:       Optional[Any]       					=			sample + sum(
          coeff * derivative for coeff, derivative in zip(__lowerCAmelCase		,    reversed(self.derivatives   )   )   )

      if not return_dict:
          return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=__lowerCAmelCase		,    pred_original_sample=__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			self.sigmas.to(device=original_samples.device		,    dtype=original_samples.dtype   )
      if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase   ):
          # mps does not support float64
          _a							:       Any       					=			self.timesteps.to(original_samples.device		,    dtype=torch.floataa   )
          _a							:       Optional[Any]       					=			timesteps.to(original_samples.device		,    dtype=torch.floataa   )
      else:
          _a							:       int       					=			self.timesteps.to(original_samples.device   )
          _a							:       Optional[Any]       					=			timesteps.to(original_samples.device   )

      _a							:       Any       					=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      _a							:       List[str]       					=			sigmas[step_indices].flatten()
      while len(sigma.shape   ) < len(original_samples.shape   ):
          _a							:       int       					=			sigma.unsqueeze(-1   )

      _a							:       List[str]       					=			original_samples + noise * sigma
      return noisy_samples



  def __len__( self   ):







      '''simple docstring'''


      return self.config.num_train_timesteps








'''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_mbartaa import MBartaaTokenizer
else:
  a		:  str   			=			None


a		:  List[str]   			=			logging.get_logger(__name__)

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

a		:  Optional[Any]   			=			{
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

a		:  Any   			=			{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
a		:  Dict   			=			['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class     UpperCamelCase					(  snake_case__					):
  a    :str      					=				VOCAB_FILES_NAMES
  a    :int      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a    :Dict      					=				PRETRAINED_VOCAB_FILES_MAP
  a    :List[str]      					=				['input_ids', 'attention_mask']
  a    :Optional[Any]      					=				MBartaaTokenizer

  a    :List[int]      					=				[]
  a    :List[int]      					=				[]



  def __init__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="<s>"		,    __lowerCAmelCase="<unk>"		,    __lowerCAmelCase="<pad>"		,    __lowerCAmelCase="<mask>"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[str]       					=			AddedToken(__lowerCAmelCase		,    lstrip=__lowerCAmelCase		,    rstrip=__lowerCAmelCase   ) if isinstance(__lowerCAmelCase		,    __lowerCAmelCase   ) else mask_token

      _a							:       List[str]       					=			kwargs.get("""additional_special_tokens"""		,    []   )
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          __lowerCAmelCase		,    src_lang=__lowerCAmelCase		,    tgt_lang=__lowerCAmelCase		,    tokenizer_file=__lowerCAmelCase		,    eos_token=__lowerCAmelCase		,    sep_token=__lowerCAmelCase		,    cls_token=__lowerCAmelCase		,    unk_token=__lowerCAmelCase		,    pad_token=__lowerCAmelCase		,    mask_token=__lowerCAmelCase		,    **__lowerCAmelCase		,    )

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

      _a							:       str       					=			{
          lang_code: self.convert_tokens_to_ids(__lowerCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a							:       Dict       					=			src_lang if src_lang is not None else """en_XX"""
      _a							:       Tuple       					=			tgt_lang
      _a							:       Tuple       					=			self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return self._src_lang



  @src_lang.setter
  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       int       					=			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      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 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = "en_XX"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = "ro_RO"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			src_lang
      _a							:       Any       					=			tgt_lang
      return super().prepare_seqaseq_batch(__lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_src_lang_special_tokens(self.src_lang   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_tgt_lang_special_tokens(self.tgt_lang   )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Union[str, Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			[self.cur_lang_code_id]
      _a							:       int       					=			[self.eos_token_id]

      _a							:       Any       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       Dict       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       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 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       List[str]       					=			[self.cur_lang_code_id]
      _a							:       Tuple       					=			[self.eos_token_id]

      _a							:       List[Any]       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       int       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       str       					=			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 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      if src_lang is None or tgt_lang is None:
          raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""   )
      _a							:       List[Any]       					=			src_lang
      _a							:       List[str]       					=			self(__lowerCAmelCase		,    add_special_tokens=__lowerCAmelCase		,    return_tensors=__lowerCAmelCase		,    **__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Tuple       					=			tgt_lang_id
      return inputs



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      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
      _a							:       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,)


