

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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =1

    @register_to_config
    def __init__(			self :      int							,    snake_case__ :      int = 1_0_0_0							,    snake_case__ :      Optional[Union[np.ndarray, List[float]]] = None    ):

     """simple docstring"""






     self.set_timesteps(snake_case__    )

     # standard deviation of the initial noise distribution
     SCREAMING_SNAKE_CASE								=			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.
     SCREAMING_SNAKE_CASE								=			4

     # running values
     SCREAMING_SNAKE_CASE								=			[]

    def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      Union[str, torch.device] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			num_inference_steps
     SCREAMING_SNAKE_CASE								=			torch.linspace(1							,    0							,    num_inference_steps + 1    )[:-1]
     SCREAMING_SNAKE_CASE								=			torch.cat([steps, torch.tensor([0.0]    )]    )

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

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

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

     SCREAMING_SNAKE_CASE								=			[]

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      int							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      bool = True							,    ):

     """simple docstring"""






     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'    )

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

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

     if len(self.ets    ) == 1:
      SCREAMING_SNAKE_CASE								=			self.ets[-1]
     elif len(self.ets    ) == 2:
      SCREAMING_SNAKE_CASE								=			(3 * self.ets[-1] - self.ets[-2]) / 2
     elif len(self.ets    ) == 3:
      SCREAMING_SNAKE_CASE								=			(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
     else:
      SCREAMING_SNAKE_CASE								=			(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

     SCREAMING_SNAKE_CASE								=			self._get_prev_sample(snake_case__							,    snake_case__							,    snake_case__							,    snake_case__    )

     if not return_dict:
      return (prev_sample,)

     return SchedulerOutput(prev_sample=snake_case__    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      torch.FloatTensor							,    *snake_case__ :      List[Any]							,    **snake_case__ :      int    ):

     """simple docstring"""






     return sample

    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.alphas[timestep_index]
     SCREAMING_SNAKE_CASE								=			self.betas[timestep_index]

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

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

     return prev_sample




    def __len__(			self :      Any    ):

     """simple docstring"""






     return self.config.num_train_timesteps




from typing import List, Optional, Tuple

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


a_    :						Optional[Any]    		=      logging.get_logger(__name__)

a_    :						Optional[Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "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"
    },
}

a_    :						Union[str, Any]    		=      {"allegro/herbert-base-cased": 514}
a_    :						List[Any]    		=      {}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =HerbertTokenizer

    def __init__(			self :      Tuple							,    snake_case__ :      Optional[Any]=None							,    snake_case__ :      int=None							,    snake_case__ :      Optional[int]=None							,    snake_case__ :      str="<s>"							,    snake_case__ :      Tuple="<unk>"							,    snake_case__ :      List[str]="<pad>"							,    snake_case__ :      Tuple="<mask>"							,    snake_case__ :      Dict="</s>"							,    **snake_case__ :      List[str]							,    ):

     """simple docstring"""






     super().__init__(
         snake_case__							,    snake_case__							,    tokenizer_file=snake_case__							,    cls_token=snake_case__							,    unk_token=snake_case__							,    pad_token=snake_case__							,    mask_token=snake_case__							,    sep_token=snake_case__							,    **snake_case__							,    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.cls_token_id]
     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=snake_case__							,    token_ids_a=snake_case__							,    already_has_special_tokens=snake_case__    )

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

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

