


'''simple docstring'''


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

import numpy as np
import torch

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


_UpperCamelCase          =     logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							None


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE=0.999					,				SCREAMING_SNAKE_CASE="cosine"					,				):




     '''simple docstring'''





     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE       ):
               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(SCREAMING_SNAKE_CASE       ):
          __A  :					Dict						     =       i / num_diffusion_timesteps
          __A  :					List[Any]						     =       (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE       ) / alpha_bar_fn(SCREAMING_SNAKE_CASE       )					,				SCREAMING_SNAKE_CASE       )       )
     return torch.tensor(SCREAMING_SNAKE_CASE					,				dtype=torch.floataa       )







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							[e.name for e in KarrasDiffusionSchedulers]
     lowerCamelCase__						=							1


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


          '''simple docstring'''





          if trained_betas is not None:
               __A  :					Optional[int]						     =       torch.tensor(lowerCamelCase ,    dtype=torch.floataa		)
          elif beta_schedule == "linear":
               __A  :					Optional[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  :					Dict						     =       (
                   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  :					List[Any]						     =       1.0 - self.betas
          __A  :					List[str]						     =       torch.cumprod(self.alphas ,    dim=0		)

          __A  :					Tuple						     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
          __A  :					List[str]						     =       np.concatenate([sigmas[::-1], [0.0]]		).astype(np.floataa		)
          __A  :					int						     =       torch.from_numpy(lowerCamelCase		)

          # setable values
          __A  :					List[str]						     =       None
          __A  :					List[str]						     =       np.linspace(0 ,    num_train_timesteps - 1 ,    lowerCamelCase ,    dtype=lowerCamelCase		)[::-1].copy()
          __A  :					Tuple						     =       torch.from_numpy(lowerCamelCase		)
          __A  :					Any						     =       False
          __A  :					List[str]						     =       use_karras_sigmas


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          if isinstance(lowerCamelCase ,    torch.Tensor		):
               __A  :					List[str]						     =       timestep.to(self.timesteps.device		)
          __A  :					Optional[int]						     =       (self.timesteps == timestep).nonzero().item()
          __A  :					List[str]						     =       self.sigmas[step_index]

          __A  :					str						     =       sample / ((sigma**2 + 1) ** 0.5)

          __A  :					Tuple						     =       True
          return sample


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       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  :					Optional[Any]						     =       np.linspace(0 ,    self.config.num_train_timesteps - 1 ,    lowerCamelCase ,    dtype=lowerCamelCase		)[
                   ::-1
               ].copy()
          elif self.config.timestep_spacing == "leading":
               __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(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  :					str						     =       (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  :					Union[str, Any]						     =       np.log(lowerCamelCase		)

          if self.config.interpolation_type == "linear":
               __A  :					Dict						     =       np.interp(lowerCamelCase ,    np.arange(0 ,    len(lowerCamelCase		)		) ,    lowerCamelCase		)
          elif self.config.interpolation_type == "log_linear":
               __A  :					int						     =       torch.linspace(np.log(sigmas[-1]		) ,    np.log(sigmas[0]		) ,    num_inference_steps + 1		).exp()
          else:
               raise ValueError(
                   f"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
                   " 'linear' or 'log_linear'"		)

          if self.use_karras_sigmas:
               __A  :					List[Any]						     =       self._convert_to_karras(in_sigmas=lowerCamelCase ,    num_inference_steps=self.num_inference_steps		)
               __A  :					Tuple						     =       np.array([self._sigma_to_t(lowerCamelCase ,    lowerCamelCase		) for sigma in sigmas]		)

          __A  :					Optional[int]						     =       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
          __A  :					Optional[int]						     =       torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)
          if str(lowerCamelCase		).startswith("mps"		):
               # mps does not support float64
               __A  :					str						     =       torch.from_numpy(lowerCamelCase		).to(lowerCamelCase ,    dtype=torch.floataa		)
          else:
               __A  :					List[str]						     =       torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       np.log(lowerCamelCase		)

          # get distribution
          __A  :					Tuple						     =       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  :					Optional[int]						     =       log_sigmas[low_idx]
          __A  :					int						     =       log_sigmas[high_idx]

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

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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

          __A  :					List[Any]						     =       7.0  # 7.0 is the value used in the paper
          __A  :					str						     =       np.linspace(0 ,    1 ,    lowerCamelCase		)
          __A  :					int						     =       sigma_min ** (1 / rho)
          __A  :					Any						     =       sigma_max ** (1 / rho)
          __A  :					List[Any]						     =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.0 ,    lowerCamelCase = 0.0 ,    lowerCamelCase = float("inf"		) ,    lowerCamelCase = 1.0 ,    lowerCamelCase = None ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if (
              isinstance(lowerCamelCase ,    lowerCamelCase		)
              or isinstance(lowerCamelCase ,    torch.IntTensor		)
              or isinstance(lowerCamelCase ,    torch.LongTensor		)
          ):
               raise ValueError(
                   (
                       "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                       " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                       " one of the `scheduler.timesteps` as a timestep."
                   ) ,    )

          if not self.is_scale_input_called:
               logger.warning(
                   "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  :					str						     =       timestep.to(self.timesteps.device		)

          __A  :					Any						     =       (self.timesteps == timestep).nonzero().item()
          __A  :					List[Any]						     =       self.sigmas[step_index]

          __A  :					Union[str, Any]						     =       min(s_churn / (len(self.sigmas		) - 1) ,    2**0.5 - 1		) if s_tmin <= sigma <= s_tmax else 0.0

          __A  :					Optional[Any]						     =       randn_tensor(
              model_output.shape ,    dtype=model_output.dtype ,    device=model_output.device ,    generator=lowerCamelCase		)

          __A  :					Optional[int]						     =       noise * s_noise
          __A  :					Any						     =       sigma * (gamma + 1)

          if gamma > 0:
               __A  :					Optional[Any]						     =       sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          # NOTE: "original_sample" should not be an expected prediction_type but is left in for
          # backwards compatibility
          if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
               __A  :					Union[str, Any]						     =       model_output
          elif self.config.prediction_type == "epsilon":
               __A  :					Any						     =       sample - sigma_hat * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __A  :					List[Any]						     =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          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  :					int						     =       (sample - pred_original_sample) / sigma_hat

          __A  :					Any						     =       self.sigmas[step_index + 1] - sigma_hat

          __A  :					Dict						     =       sample + derivative * dt

          if not return_dict:
               return (prev_sample,)

          return EulerDiscreteSchedulerOutput(prev_sample=lowerCamelCase ,    pred_original_sample=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					Tuple						     =       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  :					int						     =       self.timesteps.to(original_samples.device ,    dtype=torch.floataa		)
               __A  :					Any						     =       timesteps.to(original_samples.device ,    dtype=torch.floataa		)
          else:
               __A  :					Optional[Any]						     =       self.timesteps.to(original_samples.device		)
               __A  :					Tuple						     =       timesteps.to(original_samples.device		)

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

          __A  :					Optional[int]						     =       sigmas[step_indices].flatten()
          while len(sigma.shape		) < len(original_samples.shape		):
               __A  :					Tuple						     =       sigma.unsqueeze(-1		)

          __A  :					str						     =       original_samples + noise * sigma
          return noisy_samples


     def __len__(      self		):


          '''simple docstring'''





          return self.config.num_train_timesteps





'''simple docstring'''


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase          =     {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

_UpperCamelCase          =     {
    """openai-gpt""": 512,
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       text.strip()
     if not text:
          return []
     __A  :					Any						     =       text.split()
     return tokens







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase=True ,    lowerCamelCase=None ,    lowerCamelCase=True ,    lowerCamelCase=None ,    lowerCamelCase=True ,    ):


          '''simple docstring'''





          if never_split is None:
               __A  :					List[Any]						     =       []
          __A  :					str						     =       do_lower_case
          __A  :					str						     =       set(lowerCamelCase		)
          __A  :					Tuple						     =       tokenize_chinese_chars
          __A  :					Any						     =       strip_accents
          __A  :					Dict						     =       do_split_on_punc


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          __A  :					str						     =       self.never_split.union(set(lowerCamelCase		)		) if never_split else self.never_split
          __A  :					Optional[Any]						     =       self._clean_text(lowerCamelCase		)

          # This was added on November 1st, 2018 for the multilingual and Chinese
          # models. This is also applied to the English models now, but it doesn't
          # matter since the English models were not trained on any Chinese data
          # and generally don't have any Chinese data in them (there are Chinese
          # characters in the vocabulary because Wikipedia does have some Chinese
          # words in the English Wikipedia.).
          if self.tokenize_chinese_chars:
               __A  :					Tuple						     =       self._tokenize_chinese_chars(lowerCamelCase		)
          # prevents treating the same character with different unicode codepoints as different characters
          __A  :					str						     =       unicodedata.normalize("NFC" ,    lowerCamelCase		)
          __A  :					Tuple						     =       whitespace_tokenize(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       []
          for token in orig_tokens:
               if token not in never_split:
                    if self.do_lower_case:
                         __A  :					str						     =       token.lower()
                         if self.strip_accents is not False:
                              __A  :					str						     =       self._run_strip_accents(lowerCamelCase		)
                    elif self.strip_accents:
                         __A  :					Tuple						     =       self._run_strip_accents(lowerCamelCase		)
               split_tokens.extend(self._run_split_on_punc(lowerCamelCase ,    lowerCamelCase		)		)

          __A  :					Tuple						     =       whitespace_tokenize(" ".join(lowerCamelCase		)		)
          return output_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Tuple						     =       unicodedata.normalize("NFD" ,    lowerCamelCase		)
          __A  :					Tuple						     =       []
          for char in text:
               __A  :					Dict						     =       unicodedata.category(lowerCamelCase		)
               if cat == "Mn":
                    continue
               output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
          __A  :					int						     =       list(lowerCamelCase		)
          __A  :					int						     =       0
          __A  :					int						     =       True
          __A  :					Tuple						     =       []
          while i < len(lowerCamelCase		):
               __A  :					str						     =       chars[i]
               if _is_punctuation(lowerCamelCase		):
                    output.append([char]		)
                    __A  :					Optional[int]						     =       True
               else:
                    if start_new_word:
                         output.append([]		)
                    __A  :					List[str]						     =       False
                    output[-1].append(lowerCamelCase		)
               i += 1

          return ["".join(lowerCamelCase		) for x in output]


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					int						     =       []
          for char in text:
               __A  :					Union[str, Any]						     =       ord(lowerCamelCase		)
               if self._is_chinese_char(lowerCamelCase		):
                    output.append(" "		)
                    output.append(lowerCamelCase		)
                    output.append(" "		)
               else:
                    output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if (
              (cp >= 0x4E00 and cp <= 0x9FFF)
              or (cp >= 0x3400 and cp <= 0x4DBF)  #
              or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
              or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
              or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
              or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
              or (cp >= 0xF900 and cp <= 0xFAFF)
              or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
          ):  #
               return True

          return False


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       []
          for char in text:
               __A  :					Dict						     =       ord(lowerCamelCase		)
               if cp == 0 or cp == 0xFFFD or _is_control(lowerCamelCase		):
                    continue
               if _is_whitespace(lowerCamelCase		):
                    output.append(" "		)
               else:
                    output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       set()
     __A  :					Tuple						     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)       )
          __A  :					str						     =       char
     return pairs


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       text.replace("—"					,				"-"       )
     __A  :					Optional[int]						     =       text.replace("–"					,				"-"       )
     __A  :					Dict						     =       text.replace("―"					,				"-"       )
     __A  :					Optional[int]						     =       text.replace("…"					,				"..."       )
     __A  :					int						     =       text.replace("´"					,				"'"       )
     __A  :					List[Any]						     =       re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"					,				r" \1 "					,				SCREAMING_SNAKE_CASE       )
     __A  :					Union[str, Any]						     =       re.sub(r"\s*\n\s*"					,				" \n "					,				SCREAMING_SNAKE_CASE       )
     __A  :					List[Any]						     =       re.sub(r"[^\S\n]+"					,				" "					,				SCREAMING_SNAKE_CASE       )
     return text.strip()







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							VOCAB_FILES_NAMES
     lowerCamelCase__						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__						=							['input_ids', 'attention_mask']


     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase="<unk>" ,    **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(unk_token=lowerCamelCase ,    **lowerCamelCase		)

          try:
               import ftfy
               from spacy.lang.en import English

               __A  :					Union[str, Any]						     =       English()
               __A  :					Tuple						     =       _nlp.tokenizer
               __A  :					Any						     =       ftfy.fix_text
          except ImportError:
               logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."		)
               __A  :					List[Any]						     =       BasicTokenizer(do_lower_case=lowerCamelCase		)
               __A  :					Any						     =       None

          with open(lowerCamelCase ,    encoding="utf-8"		) as vocab_handle:
               __A  :					int						     =       json.load(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       {v: k for k, v in self.encoder.items()}
          with open(lowerCamelCase ,    encoding="utf-8"		) as merges_handle:
               __A  :					Tuple						     =       merges_handle.read().split("\n"		)[1:-1]
          __A  :					List[Any]						     =       [tuple(merge.split()		) for merge in merges]
          __A  :					Optional[int]						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)
          __A  :					Union[str, Any]						     =       {}


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.encoder		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return dict(self.encoder ,    **self.added_tokens_encoder		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       tuple(token[:-1]		) + (token[-1] + "</w>",)
          if token in self.cache:
               return self.cache[token]
          __A  :					Optional[Any]						     =       get_pairs(lowerCamelCase		)

          if not pairs:
               return token + "</w>"

          while True:
               __A  :					List[Any]						     =       min(lowerCamelCase ,    key=lambda lowerCamelCase		: self.bpe_ranks.get(lowerCamelCase ,    float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                    break
               __A     ,__A  :					Tuple						     =       bigram
               __A  :					List[Any]						     =       []
               __A  :					Optional[int]						     =       0
               while i < len(lowerCamelCase		):
                    try:
                         __A  :					Dict						     =       word.index(lowerCamelCase ,    lowerCamelCase		)
                    except ValueError:
                         new_word.extend(word[i:]		)
                         break
                    else:
                         new_word.extend(word[i:j]		)
                         __A  :					int						     =       j

                    if word[i] == first and i < len(lowerCamelCase		) - 1 and word[i + 1] == second:
                         new_word.append(first + second		)
                         i += 2
                    else:
                         new_word.append(word[i]		)
                         i += 1
               __A  :					List[str]						     =       tuple(lowerCamelCase		)
               __A  :					int						     =       new_word
               if len(lowerCamelCase		) == 1:
                    break
               else:
                    __A  :					List[str]						     =       get_pairs(lowerCamelCase		)
          __A  :					Tuple						     =       " ".join(lowerCamelCase		)
          if word == "\n  </w>":
               __A  :					List[str]						     =       "\n</w>"
          __A  :					List[Any]						     =       word
          return word


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Dict						     =       []
          if self.fix_text is None:
               # Using BERT's BasicTokenizer
               __A  :					Tuple						     =       self.nlp.tokenize(lowerCamelCase		)
               for token in text:
                    split_tokens.extend(list(self.bpe(lowerCamelCase		).split(" "		)		)		)
          else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               __A  :					List[Any]						     =       self.nlp(text_standardize(self.fix_text(lowerCamelCase		)		)		)
               for token in text:
                    split_tokens.extend(list(self.bpe(token.text.lower()		).split(" "		)		)		)
          return split_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.encoder.get(lowerCamelCase ,    self.encoder.get(self.unk_token		)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.decoder.get(lowerCamelCase ,    self.unk_token		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       "".join(lowerCamelCase		).replace("</w>" ,    " "		).strip()
          return out_string


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Optional[Any]						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          __A  :					str						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

          with open(lowerCamelCase ,    "w" ,    encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder ,    indent=2 ,    sort_keys=lowerCamelCase ,    ensure_ascii=lowerCamelCase		) + "\n"		)

          __A  :					Tuple						     =       0
          with open(lowerCamelCase ,    "w" ,    encoding="utf-8"		) as writer:
               writer.write("#version: 0.2\n"		)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,    key=lambda lowerCamelCase		: kv[1]		):
                    if index != token_index:
                         logger.warning(
                             f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                             " Please check that the tokenizer is not corrupted!"		)
                         __A  :					Tuple						     =       token_index
                    writer.write(" ".join(lowerCamelCase		) + "\n"		)
                    index += 1

          return vocab_file, merge_file

