

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

import torch

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


@dataclass
class        a__  (							__a  ):
   _SCREAMING_SNAKE_CASE   :					torch.FloatTensor
   _SCREAMING_SNAKE_CASE   :					torch.FloatTensor




class        a__  (							__a  ,				__a  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							1


   @register_to_config
   def __init__(			self      ,    _UpperCamelCase = 2000      ,    _UpperCamelCase = 0.1_5      ,    _UpperCamelCase = 0.0_1      ,    _UpperCamelCase = 1348.0      ,    _UpperCamelCase = 1E-5      ,    _UpperCamelCase = 1      ,    ):





      """simple docstring"""





      _lowercase      :				Any      					=       sigma_max

      # setable values
      _lowercase      :				Any      					=       None

      self.set_sigmas(a__      ,    a__      ,    a__      ,    a__  )


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





      """simple docstring"""





      return sample


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





      """simple docstring"""





      _lowercase      :				Tuple      					=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

      _lowercase      :				Tuple      					=       torch.linspace(1      ,    a__      ,    a__      ,    device=a__  )


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





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       sigma_min if sigma_min is not None else self.config.sigma_min
      _lowercase      :				int      					=       sigma_max if sigma_max is not None else self.config.sigma_max
      _lowercase      :				List[Any]      					=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
         self.set_timesteps(a__      ,    a__  )

      _lowercase      :				Union[str, Any]      					=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      _lowercase      :				Tuple      					=       torch.exp(torch.linspace(math.log(a__  )      ,    math.log(a__  )      ,    a__  )  )
      _lowercase      :				Union[str, Any]      					=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      return torch.where(
          timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device  )  )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device  )      ,    )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





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

      _lowercase      :				List[str]      					=       timestep * torch.ones(
          sample.shape[0]      ,    device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
      _lowercase      :				int      					=       (timestep * (len(self.timesteps  ) - 1)).long()

      # mps requires indices to be in the same device, so we use cpu as is the default with cuda
      _lowercase      :				int      					=       timesteps.to(self.discrete_sigmas.device  )

      _lowercase      :				int      					=       self.discrete_sigmas[timesteps].to(sample.device  )
      _lowercase      :				List[str]      					=       self.get_adjacent_sigma(a__      ,    a__  ).to(sample.device  )
      _lowercase      :				Any      					=       torch.zeros_like(a__  )
      _lowercase      :				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
      _lowercase      :				List[str]      					=       diffusion.flatten()
      while len(diffusion.shape  ) < len(sample.shape  ):
         _lowercase      :				Dict      					=       diffusion.unsqueeze(-1  )
      _lowercase      :				str      					=       drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      _lowercase      :				Union[str, Any]      					=       randn_tensor(
          sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype  )
      _lowercase      :				Optional[Any]      					=       sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      _lowercase      :				Optional[int]      					=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
         return (prev_sample, prev_sample_mean)

      return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





      if self.timesteps is None:
         raise ValueError(
             "`self.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
      _lowercase      :				str      					=       randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__  ).to(sample.device  )

      # compute step size from the model_output, the noise, and the snr
      _lowercase      :				Tuple      					=       torch.norm(model_output.reshape(model_output.shape[0]      ,    -1  )      ,    dim=-1  ).mean()
      _lowercase      :				Optional[int]      					=       torch.norm(noise.reshape(noise.shape[0]      ,    -1  )      ,    dim=-1  ).mean()
      _lowercase      :				List[str]      					=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      _lowercase      :				List[str]      					=       step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
      # self.repeat_scalar(step_size, sample.shape[0])

      # compute corrected sample: model_output term and noise term
      _lowercase      :				Tuple      					=       step_size.flatten()
      while len(step_size.shape  ) < len(sample.shape  ):
         _lowercase      :				str      					=       step_size.unsqueeze(-1  )
      _lowercase      :				Optional[int]      					=       sample + step_size * model_output
      _lowercase      :				Optional[Any]      					=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=a__  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       timesteps.to(original_samples.device  )
      _lowercase      :				Tuple      					=       self.discrete_sigmas.to(original_samples.device  )[timesteps]
      _lowercase      :				Any      					=       (
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(a__  ) * sigmas[:, None, None, None]
      )
      _lowercase      :				int      					=       noise + original_samples
      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 Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case         =  '▁'

_snake_case         =  {'vocab_file': 'spiece.model'}

_snake_case         =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

_snake_case         =  {
    'google/pegasus-xsum': 512,
}


_snake_case         =  logging.get_logger(__name__)


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							VOCAB_FILES_NAMES

   _SCREAMING_SNAKE_CASE   :					Tuple     	=							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE   :					Any     	=							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['input_ids', 'attention_mask']


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase="<pad>"      ,    _UpperCamelCase="</s>"      ,    _UpperCamelCase="<unk>"      ,    _UpperCamelCase="<mask_2>"      ,    _UpperCamelCase="<mask_1>"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=103      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       offset
      if additional_special_tokens is not None:
         if not isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
            raise TypeError(
                f'''additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is'''
                f''' {type(_UpperCamelCase  )}'''  )

         _lowercase      :				Dict      					=       (
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f'''<unk_{i}>''' for i in range(len(_UpperCamelCase  )      ,    self.offset - 1  )
         ]

         if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
            raise ValueError(
                "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
         _lowercase      :				List[str]      					=       additional_special_tokens_extended
      else:
         _lowercase      :				Dict      					=       [mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f'''<unk_{i}>''' for i in range(2      ,    self.offset  )]

      _lowercase      :				int      					=       {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=_UpperCamelCase      ,    unk_token=_UpperCamelCase      ,    mask_token=_UpperCamelCase      ,    pad_token=_UpperCamelCase      ,    mask_token_sent=_UpperCamelCase      ,    offset=_UpperCamelCase      ,    additional_special_tokens=_UpperCamelCase      ,    sp_model_kwargs=self.sp_model_kwargs      ,    **_UpperCamelCase      ,    )
      _lowercase      :				Union[str, Any]      					=       mask_token_sent
      _lowercase      :				str      					=       vocab_file
      _lowercase      :				Dict      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(_UpperCamelCase  )

      # add special tokens to encoder dict
      _lowercase      :				Dict[int, str]      					=       {
          0: self.pad_token,
          1: self.eos_token,
      }

      if self.mask_token_sent is not None:
         self.encoder.update(
             {
                 2: self.mask_token_sent,
                 3: self.mask_token,
             }  )

      if self.offset > 0:
         # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
         # mask_token_sent is already added to list -> so start at 1
         self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,    self.offset - 1  )}  )

      _lowercase      :				Dict[str, int]      					=       {v: k for k, v in self.encoder.items()}


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return len(self.sp_model  ) + self.offset


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       {self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab


   def __getstate__(			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.__dict__.copy()
      _lowercase      :				Union[str, Any]      					=       None
      return state


   def __setstate__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       d

      # for backward compatibility
      if not hasattr(self      ,    "sp_model_kwargs"  ):
         _lowercase      :				List[Any]      					=       {}

      _lowercase      :				Any      					=       spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.sp_model.encode(_UpperCamelCase      ,    out_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if token in self.decoder:
         return self.decoder[token]
      elif token in self.added_tokens_decoder:
         return self.added_tokens_decoder[token]
      _lowercase      :				int      					=       self.sp_model.piece_to_id(_UpperCamelCase  )
      return sp_id + self.offset


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if index in self.encoder:
         return self.encoder[index]
      elif index in self.added_tokens_encoder:
         return self.added_tokens_encoder[index]
      else:
         _lowercase      :				Any      					=       self.sp_model.IdToPiece(index - self.offset  )
      return token


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       []
      _lowercase      :				Optional[Any]      					=       ""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(_UpperCamelCase  ) + token
            _lowercase      :				Tuple      					=       []
         else:
            current_sub_tokens.append(_UpperCamelCase  )
      out_string += self.sp_model.decode(_UpperCamelCase  )
      return out_string.strip()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=False  ):





      """simple docstring"""





      return 1


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       set(self.all_special_ids  )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

      return [1 if x in all_special_ids else 0 for x in seq]


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False  ):





      """simple docstring"""





      if already_has_special_tokens:
         return self._special_token_mask(_UpperCamelCase  )
      elif token_ids_a is None:
         return self._special_token_mask(_UpperCamelCase  ) + [1]
      else:
         return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]


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





      """simple docstring"""





      if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]






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





      """simple docstring"""





      if not os.path.isdir(_UpperCamelCase  ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
         return
      _lowercase      :				List[Any]      					=       os.path.join(
          _UpperCamelCase      ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
         copyfile(self.vocab_file      ,    _UpperCamelCase  )
      elif not os.path.isfile(self.vocab_file  ):
         with open(_UpperCamelCase      ,    "wb"  ) as fi:
            _lowercase      :				Optional[int]      					=       self.sp_model.serialized_model_proto()
            fi.write(_UpperCamelCase  )

      return (out_vocab_file,)






