

'''simple docstring'''




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =						{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

_lowerCAmelCase         =						{"allegro/herbert-base-cased": 514}
_lowerCAmelCase         =						{}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:int      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:Dict      	=					PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE					:Dict      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					HerbertTokenizer



 def __init__(   self		:   Optional[int]			,				a__		:   Dict=None			,				a__		:   Tuple=None			,				a__		:   List[str]=None			,				a__		:   Optional[Any]="<s>"			,				a__		:   str="<unk>"			,				a__		:   str="<pad>"			,				a__		:   str="<mask>"			,				a__		:   Union[str, Any]="</s>"			,				**a__		:   Optional[Any]			,				):
      super().__init__(
          a__			,				a__			,				tokenizer_file=a__			,				cls_token=a__			,				unk_token=a__			,				pad_token=a__			,				mask_token=a__			,				sep_token=a__			,				**a__			,				)



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.cls_token_id]
      __magic_name__						       =    [self.sep_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep

      return cls + token_ids_a + sep + token_ids_a + sep



 def 			snake_case__  (   self		:   str			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None			,				a__		:   bool = False      ):
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=a__			,				token_ids_a=a__			,				already_has_special_tokens=a__      )

      if token_ids_a is None:
           return [1] + ([0] * len(a__      )) + [1]
      return [1] + ([0] * len(a__      )) + [1] + ([0] * len(a__      )) + [1]



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.sep_token_id]
      __magic_name__						       =    [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      ) * [0] + len(token_ids_a + sep      ) * [1]



 def 			snake_case__  (   self		:   Tuple			,				a__		:   str			,				a__		:   Optional[str] = None      ):
      __magic_name__						       =    self._tokenizer.model.save(a__			,				name=a__      )
      return tuple(a__      )









'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






