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 SchedulerMixin


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




@dataclass
class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		torch.FloatTensor







class       __lowerCAmelCase     (				a_,		a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       1

 @register_to_config
 def __init__(	self				:						Optional[Any]				,	lowerCamelCase				:						int = 4_0				,	lowerCamelCase				:						float = 0.0_0_2				,	lowerCamelCase				:						float = 8_0.0				,	lowerCamelCase				:						float = 0.5				,	lowerCamelCase				:						float = 1.0				,	lowerCamelCase				:						float = 7.0				,	lowerCamelCase				:						bool = True				,	)       ->							Tuple:
        # standard deviation of the initial noise distribution
        _UpperCamelCase   :				int       =     sigma_max

        _UpperCamelCase   :				int       =     np.linspace(0				,	1				,	lowerCamelCase     )
        _UpperCamelCase   :				Optional[Any]       =     self._convert_to_karras(lowerCamelCase     )
        _UpperCamelCase   :				int       =     self.sigma_to_t(lowerCamelCase     )

        # setable values
        _UpperCamelCase   :				Optional[int]       =     None
        _UpperCamelCase   :				Tuple       =     torch.from_numpy(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     torch.from_numpy(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     False
        _UpperCamelCase   :				Any       =     False

 def     __snake_case    (	self				:						List[Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						Any=None     )       ->							List[str]:
        if schedule_timesteps is None:
               _UpperCamelCase   :				List[Any]       =     self.timesteps

        _UpperCamelCase   :				Union[str, Any]       =     (schedule_timesteps == timestep).nonzero()
        return indices.item()

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						Union[float, torch.FloatTensor]     )       ->							torch.FloatTensor:
        # Get sigma corresponding to timestep
        if isinstance(lowerCamelCase				,	torch.Tensor     ):
               _UpperCamelCase   :				Dict       =     timestep.to(self.timesteps.device     )
        _UpperCamelCase   :				int       =     self.index_for_timestep(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     self.sigmas[step_idx]

        _UpperCamelCase   :				Any       =     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

        _UpperCamelCase   :				Dict       =     True
        return sample

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Union[float, np.ndarray]     )       ->							Optional[Any]:
        if not isinstance(lowerCamelCase				,	np.ndarray     ):
               _UpperCamelCase   :				Dict       =     np.array(lowerCamelCase				,	dtype=np.floataa     )

        _UpperCamelCase   :				str       =     1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44     )

        return timesteps

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						Optional[int] = None				,	lowerCamelCase				:						Union[str, torch.device] = None				,	lowerCamelCase				:						Optional[List[int]] = None				,	)       ->							List[Any]:
        if num_inference_steps is None and timesteps is None:
               raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'     )

        if num_inference_steps is not None and timesteps is not None:
               raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'     )

        # Follow DDPMScheduler custom timesteps logic
        if timesteps is not None:
               for i in range(1				,	len(lowerCamelCase     )     ):
                      if timesteps[i] >= timesteps[i - 1]:
                             raise ValueError('`timesteps` must be in descending order.'     )

               if timesteps[0] >= self.config.num_train_timesteps:
                      raise ValueError(
                          f"`timesteps` must start before `self.config.train_timesteps`:"
                          f" {self.config.num_train_timesteps}."     )

               _UpperCamelCase   :				Optional[int]       =     np.array(lowerCamelCase				,	dtype=np.intaa     )
               _UpperCamelCase   :				Any       =     True
        else:
               if num_inference_steps > self.config.num_train_timesteps:
                      raise ValueError(
                          f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                          f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                          f" maximal {self.config.num_train_timesteps} timesteps."     )

               _UpperCamelCase   :				Optional[int]       =     num_inference_steps

               _UpperCamelCase   :				Optional[int]       =     self.config.num_train_timesteps // self.num_inference_steps
               _UpperCamelCase   :				Union[str, Any]       =     (np.arange(0				,	lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
               _UpperCamelCase   :				Optional[Any]       =     False

        # Map timesteps to Karras sigmas directly for multistep sampling
        # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
        _UpperCamelCase   :				Optional[int]       =     self.config.num_train_timesteps
        _UpperCamelCase   :				Tuple       =     timesteps[::-1].copy()
        _UpperCamelCase   :				int       =     ramp / (num_train_timesteps - 1)
        _UpperCamelCase   :				List[Any]       =     self._convert_to_karras(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     self.sigma_to_t(lowerCamelCase     )

        _UpperCamelCase   :				Union[str, Any]       =     np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
        _UpperCamelCase   :				List[str]       =     torch.from_numpy(lowerCamelCase     ).to(device=lowerCamelCase     )

        if str(lowerCamelCase     ).startswith('mps'     ):
               # mps does not support float64
               _UpperCamelCase   :				List[str]       =     torch.from_numpy(lowerCamelCase     ).to(lowerCamelCase				,	dtype=torch.floataa     )
        else:
               _UpperCamelCase   :				Optional[Any]       =     torch.from_numpy(lowerCamelCase     ).to(device=lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						Union[str, Any]     )       ->							str:
        _UpperCamelCase   :				float       =     self.config.sigma_min
        _UpperCamelCase   :				float       =     self.config.sigma_max

        _UpperCamelCase   :				Optional[Any]       =     self.config.rho
        _UpperCamelCase   :				Optional[Any]       =     sigma_min ** (1 / rho)
        _UpperCamelCase   :				int       =     sigma_max ** (1 / rho)
        _UpperCamelCase   :				Optional[Any]       =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas

 def     __snake_case    (	self				:						Dict				,	lowerCamelCase				:						int     )       ->							Optional[Any]:
        _UpperCamelCase   :				Tuple       =     self.config.sigma_data

        _UpperCamelCase   :				int       =     sigma_data**2 / (sigma**2 + sigma_data**2)
        _UpperCamelCase   :				Optional[Any]       =     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						List[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				Optional[Any]       =     self.config.sigma_min
        _UpperCamelCase   :				str       =     self.config.sigma_data

        _UpperCamelCase   :				Any       =     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
        _UpperCamelCase   :				Optional[int]       =     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
        return c_skip, c_out

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						Union[float, torch.FloatTensor]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						Optional[torch.Generator] = None				,	lowerCamelCase				:						bool = True				,	)       ->							Union[CMStochasticIterativeSchedulerOutput, Tuple]:

        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'
                       f" `{self.__class__}.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     ):
               _UpperCamelCase   :				Optional[Any]       =     timestep.to(self.timesteps.device     )

        _UpperCamelCase   :				str       =     self.config.sigma_min
        _UpperCamelCase   :				str       =     self.config.sigma_max

        _UpperCamelCase   :				Union[str, Any]       =     self.index_for_timestep(lowerCamelCase     )

        # sigma_next corresponds to next_t in original implementation
        _UpperCamelCase   :				Union[str, Any]       =     self.sigmas[step_index]
        if step_index + 1 < self.config.num_train_timesteps:
               _UpperCamelCase   :				List[str]       =     self.sigmas[step_index + 1]
        else:
               # Set sigma_next to sigma_min
               _UpperCamelCase   :				List[str]       =     self.sigmas[-1]

        # Get scalings for boundary conditions
        _UpperCamelCase  ,					_UpperCamelCase   :				Union[str, Any]       =     self.get_scalings_for_boundary_condition(lowerCamelCase     )

        # 1. Denoise model output using boundary conditions
        _UpperCamelCase   :				Dict       =     c_out * model_output + c_skip * sample
        if self.config.clip_denoised:
               _UpperCamelCase   :				Any       =     denoised.clamp(-1				,	1     )

        # 2. Sample z ~ N(0, s_noise^2 * I)
        # Noise is not used for onestep sampling.
        if len(self.timesteps     ) > 1:
               _UpperCamelCase   :				Optional[Any]       =     randn_tensor(
                   model_output.shape				,	dtype=model_output.dtype				,	device=model_output.device				,	generator=lowerCamelCase     )
        else:
               _UpperCamelCase   :				Dict       =     torch.zeros_like(lowerCamelCase     )
        _UpperCamelCase   :				int       =     noise * self.config.s_noise

        _UpperCamelCase   :				Optional[Any]       =     sigma_next.clamp(min=lowerCamelCase				,	max=lowerCamelCase     )

        # 3. Return noisy sample
        # tau = sigma_hat, eps = sigma_min
        _UpperCamelCase   :				int       =     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

        if not return_dict:
               return (prev_sample,)

        return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase     )

 def     __snake_case    (	self				:						List[Any]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						torch.FloatTensor				,	)       ->							torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _UpperCamelCase   :				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
               _UpperCamelCase   :				Union[str, Any]       =     self.timesteps.to(original_samples.device				,	dtype=torch.floataa     )
               _UpperCamelCase   :				Optional[Any]       =     timesteps.to(original_samples.device				,	dtype=torch.floataa     )
        else:
               _UpperCamelCase   :				Any       =     self.timesteps.to(original_samples.device     )
               _UpperCamelCase   :				List[str]       =     timesteps.to(original_samples.device     )

        _UpperCamelCase   :				Optional[int]       =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        _UpperCamelCase   :				Dict       =     original_samples + noise * sigma
        return noisy_samples

 def __len__(	self				:						Dict     )       ->							Dict:
        return self.config.num_train_timesteps




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase							= logging.get_logger(__name__)

UpperCAmelCase							= {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

UpperCAmelCase							= {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

UpperCAmelCase							= {
    '''Salesforce/codegen-350M-mono''': 20_48,
}


@lru_cache()
def SCREAMING_SNAKE_CASE (   ):
       _UpperCamelCase   :				Any       =     (
           list(range(ord('!' )						,  ord('~' ) + 1 ) ) + list(range(ord('¡' )						,  ord('¬' ) + 1 ) ) + list(range(ord('®' )						,  ord('ÿ' ) + 1 ) )
       )
       _UpperCamelCase   :				int       =     bs[:]
       _UpperCamelCase   :				Any       =     0
       for b in range(2**8 ):
              if b not in bs:
                     bs.append(lowercase_ )
                     cs.append(2**8 + n )
                     n += 1
       _UpperCamelCase   :				str       =     [chr(lowercase_ ) for n in cs]
       return dict(zip(lowercase_						,  lowercase_ ) )


def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
       _UpperCamelCase   :				List[str]       =     set()
       _UpperCamelCase   :				Optional[int]       =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCamelCase   :				Optional[Any]       =     char
       return pairs





class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Union[str, Any] 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Optional[int] 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		List[str] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Optional[int] 			=       ['input_ids', 'attention_mask']

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[Any]="replace"				,	lowerCamelCase				:						int="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						List[Any]=False				,	lowerCamelCase				:						Optional[int]=False				,	**lowerCamelCase				:						Optional[Any]				,	)       ->							int:
        _UpperCamelCase   :				int       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else bos_token
        _UpperCamelCase   :				Any       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else eos_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else unk_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else pad_token
        super().__init__(
            errors=lowerCamelCase				,	unk_token=lowerCamelCase				,	bos_token=lowerCamelCase				,	eos_token=lowerCamelCase				,	pad_token=lowerCamelCase				,	add_prefix_space=lowerCamelCase				,	add_bos_token=lowerCamelCase				,	**lowerCamelCase				,	)
        _UpperCamelCase   :				List[str]       =     add_bos_token

        with open(lowerCamelCase				,	encoding='utf-8'     ) as vocab_handle:
               _UpperCamelCase   :				Union[str, Any]       =     json.load(lowerCamelCase     )
        _UpperCamelCase   :				int       =     {v: k for k, v in self.encoder.items()}
        _UpperCamelCase   :				Optional[int]       =     errors  # how to handle errors in decoding
        _UpperCamelCase   :				Dict       =     bytes_to_unicode()
        _UpperCamelCase   :				Optional[Any]       =     {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCamelCase				,	encoding='utf-8'     ) as merges_handle:
               _UpperCamelCase   :				Dict       =     merges_handle.read().split('\n'     )[1:-1]
        _UpperCamelCase   :				int       =     [tuple(merge.split()     ) for merge in bpe_merges]
        _UpperCamelCase   :				Optional[Any]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[Any]       =     {}
        _UpperCamelCase   :				Any       =     add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _UpperCamelCase   :				Union[str, Any]       =     re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'     )

 @property
 def     __snake_case    (	self				:						Optional[Any]     )       ->							Union[str, Any]:
        return len(self.encoder     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							Optional[Any]:
        return dict(self.encoder				,	**self.added_tokens_encoder     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Dict     )       ->							Union[str, Any]:
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase   :				Dict       =     tuple(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     get_pairs(lowerCamelCase     )

        if not pairs:
               return token

        while True:
               _UpperCamelCase   :				List[Any]       =     min(lowerCamelCase				,	key=lambda lowerCamelCase     : self.bpe_ranks.get(lowerCamelCase				,	float('inf'     )     )     )
               if bigram not in self.bpe_ranks:
                      break
               _UpperCamelCase  ,					_UpperCamelCase   :				Optional[int]       =     bigram
               _UpperCamelCase   :				Optional[Any]       =     []
               _UpperCamelCase   :				int       =     0
               while i < len(lowerCamelCase     ):
                      try:
                             _UpperCamelCase   :				str       =     word.index(lowerCamelCase				,	lowerCamelCase     )
                      except ValueError:
                             new_word.extend(word[i:]     )
                             break
                      else:
                             new_word.extend(word[i:j]     )
                             _UpperCamelCase   :				List[Any]       =     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
               _UpperCamelCase   :				List[str]       =     tuple(lowerCamelCase     )
               _UpperCamelCase   :				Tuple       =     new_word
               if len(lowerCamelCase     ) == 1:
                      break
               else:
                      _UpperCamelCase   :				int       =     get_pairs(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     ' '.join(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     word
        return word

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Any=None     )       ->							Any:
        if self.add_bos_token:
               _UpperCamelCase   :				List[str]       =     [self.bos_token_id]
        else:
               _UpperCamelCase   :				List[str]       =     []

        _UpperCamelCase   :				Union[str, Any]       =     bos_token_ids + token_ids_a

        if token_ids_a is None:
               return output

        return output + bos_token_ids + token_ids_a

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						Union[str, Any]     )       ->							str:
        _UpperCamelCase   :				Tuple       =     []
        for token in re.findall(self.pat				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     ''.join(
                   self.byte_encoder[b] for b in token.encode('utf-8'     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase     ).split(' '     )     )
        return bpe_tokens

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Any     )       ->							List[str]:
        return self.encoder.get(lowerCamelCase				,	self.encoder.get(self.unk_token     )     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						List[Any]     )       ->							List[str]:
        return self.decoder.get(lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Optional[Any]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     ''.join(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'				,	errors=self.errors     )
        return text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[str] = None     )       ->							Tuple[str]:
        if not os.path.isdir(lowerCamelCase     ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
               return
        _UpperCamelCase   :				Any       =     os.path.join(
            lowerCamelCase				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
        _UpperCamelCase   :				Optional[int]       =     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'     )

        _UpperCamelCase   :				Any       =     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!'     )
                             _UpperCamelCase   :				Dict       =     token_index
                      writer.write(' '.join(lowerCamelCase     ) + '\n'     )
                      index += 1

        return vocab_file, merge_file

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						int=False				,	**lowerCamelCase				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     kwargs.pop('add_prefix_space'				,	self.add_prefix_space     )
        if is_split_into_words or add_prefix_space:
               _UpperCamelCase   :				Union[str, Any]       =     ' ' + text
        return (text, kwargs)

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = None				,	lowerCamelCase				:						Optional[List[str]] = None				,	**lowerCamelCase				:						Union[str, Any]				,	)       ->							str:
        _UpperCamelCase   :				List[str]       =     super()._decode(
            token_ids=lowerCamelCase				,	skip_special_tokens=lowerCamelCase				,	clean_up_tokenization_spaces=lowerCamelCase				,	**lowerCamelCase				,	)

        if truncate_before_pattern is not None and len(lowerCamelCase     ) > 0:
               _UpperCamelCase   :				Union[str, Any]       =     self.truncate(lowerCamelCase				,	lowerCamelCase     )

        return decoded_text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						Optional[int]     )       ->							Any:
        def find_re(lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						List[Any]     ):
               _UpperCamelCase   :				Dict       =     pattern.search(lowerCamelCase				,	lowerCamelCase     )
               return m.start() if m else -1

        _UpperCamelCase   :				int       =     [re.compile(lowerCamelCase				,	re.MULTILINE     ) for pattern in truncate_before_pattern]

        _UpperCamelCase   :				Union[str, Any]       =     list(re.finditer('^print'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				List[Any]       =     completion[: prints[1].start()]

        _UpperCamelCase   :				Dict       =     list(re.finditer('^def'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				Tuple       =     completion[: defs[1].start()]

        _UpperCamelCase   :				Optional[Any]       =     0

        _UpperCamelCase   :				int       =     [
            pos for pos in [find_re(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     ) for terminal in terminals] if pos != -1
        ]

        if len(lowerCamelCase     ) > 0:
               return completion[: min(lowerCamelCase     )]
        else:
               return completion



