


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


lowerCAmelCase__       :	Union[str, Any]     				=logging.getLogger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







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





										"""simple docstring"""




										if current_layer == 0:
													SCREAMING_SNAKE_CASE_						:			Dict       						= self.embedding_hidden_mapping_in(lowerCAmelCase__    )
										else:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= hidden_states[0]

										SCREAMING_SNAKE_CASE_						:			List[str]       						= int(self.config.num_hidden_layers / self.config.num_hidden_groups    )

										# Index of the hidden group
										SCREAMING_SNAKE_CASE_						:			Tuple       						= int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.albert_layer_groups[group_idx](
										    lowerCAmelCase__       ,		lowerCAmelCase__       ,		head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,		)
										SCREAMING_SNAKE_CASE_						:			List[str]       						= layer_group_output[0]

										return (hidden_states,)







@add_start_docstrings(
    """The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top."""     ,    __SCREAMING_SNAKE_CASE     ,    )
class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= AlbertTransformerWithPabee(lowerCAmelCase__    )

										self.init_weights()
										SCREAMING_SNAKE_CASE_						:			str       						= 0
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0

										SCREAMING_SNAKE_CASE_						:			Any       						= 0






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= threshold






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= patience






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= 0
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 0






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.inference_layers_num / self.inference_instances_num
										SCREAMING_SNAKE_CASE_						:			Tuple       						= (
										    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
										    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
										)
										print(lowerCAmelCase__    )






							@add_start_docstrings_to_model_forward(lowerCAmelCase__    )
							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=False       ,		):





										"""simple docstring"""




										if input_ids is not None and inputs_embeds is not None:
													raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'    )
										elif input_ids is not None:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= input_ids.size()
										elif inputs_embeds is not None:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= inputs_embeds.size()[:-1]
										else:
													raise ValueError('You have to specify either input_ids or inputs_embeds'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.ones(lowerCAmelCase__       ,		device=lowerCAmelCase__    )
										if token_type_ids is None:
													SCREAMING_SNAKE_CASE_						:			List[str]       						= torch.zeros(lowerCAmelCase__       ,		dtype=torch.long       ,		device=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= attention_mask.unsqueeze(1    ).unsqueeze(2    )
										SCREAMING_SNAKE_CASE_						:			int       						= extended_attention_mask.to(dtype=self.dtype    )  # fp16 compatibility
										SCREAMING_SNAKE_CASE_						:			Tuple       						= (1.0 - extended_attention_mask) * -10_000.0
										SCREAMING_SNAKE_CASE_						:			int       						= self.get_head_mask(lowerCAmelCase__       ,		self.config.num_hidden_layers    )

										SCREAMING_SNAKE_CASE_						:			Any       						= self.embeddings(
										    lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		token_type_ids=lowerCAmelCase__       ,		inputs_embeds=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= embedding_output

										if self.training:
													SCREAMING_SNAKE_CASE_						:			Dict       						= []
													for i in range(self.config.num_hidden_layers    ):
																SCREAMING_SNAKE_CASE_						:			Tuple       						= self.encoder.adaptive_forward(
																    lowerCAmelCase__       ,		current_layer=lowerCAmelCase__       ,		attention_mask=lowerCAmelCase__       ,		head_mask=lowerCAmelCase__       ,		)

																SCREAMING_SNAKE_CASE_						:			str       						= self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
																SCREAMING_SNAKE_CASE_						:			List[Any]       						= output_layers[i](output_dropout(lowerCAmelCase__    )    )
																res.append(lowerCAmelCase__    )
										elif self.patience == 0:  # Use all layers for inference
													SCREAMING_SNAKE_CASE_						:			Dict       						= self.encoder(lowerCAmelCase__       ,		lowerCAmelCase__       ,		head_mask=lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= [output_layers[self.config.num_hidden_layers - 1](lowerCAmelCase__    )]
										else:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 0
													SCREAMING_SNAKE_CASE_						:			int       						= None
													SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
													for i in range(self.config.num_hidden_layers    ):
																calculated_layer_num += 1
																SCREAMING_SNAKE_CASE_						:			int       						= self.encoder.adaptive_forward(
																    lowerCAmelCase__       ,		current_layer=lowerCAmelCase__       ,		attention_mask=lowerCAmelCase__       ,		head_mask=lowerCAmelCase__       ,		)

																SCREAMING_SNAKE_CASE_						:			Any       						= self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]    )    )
																SCREAMING_SNAKE_CASE_						:			List[str]       						= output_layers[i](lowerCAmelCase__    )
																if regression:
																			SCREAMING_SNAKE_CASE_						:			int       						= logits.detach()
																			if patient_result is not None:
																						SCREAMING_SNAKE_CASE_						:			Dict       						= patient_result.detach()
																			if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
																						patient_counter += 1
																			else:
																						SCREAMING_SNAKE_CASE_						:			str       						= 0
																else:
																			SCREAMING_SNAKE_CASE_						:			List[str]       						= logits.detach().argmax(dim=1    )
																			if patient_result is not None:
																						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= patient_result.detach().argmax(dim=1    )
																			if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase__    )    ):
																						patient_counter += 1
																			else:
																						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0

																SCREAMING_SNAKE_CASE_						:			List[str]       						= logits
																if patient_counter == self.patience:
																			break
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= [patient_result]
													self.inference_layers_num += calculated_layer_num
													self.inference_instances_num += 1

										return res







@add_start_docstrings(
    """Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """     ,    __SCREAMING_SNAKE_CASE     ,    )
class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= config.num_labels

										SCREAMING_SNAKE_CASE_						:			str       						= AlbertModelWithPabee(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= nn.Dropout(config.classifier_dropout_prob    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= nn.ModuleList(
										    [nn.Linear(config.hidden_size       ,		self.config.num_labels    ) for _ in range(config.num_hidden_layers    )]    )

										self.init_weights()






							@add_start_docstrings_to_model_forward(lowerCAmelCase__    )
							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.albert(
										    input_ids=lowerCAmelCase__       ,		attention_mask=lowerCAmelCase__       ,		token_type_ids=lowerCAmelCase__       ,		position_ids=lowerCAmelCase__       ,		head_mask=lowerCAmelCase__       ,		inputs_embeds=lowerCAmelCase__       ,		output_dropout=self.dropout       ,		output_layers=self.classifiers       ,		regression=self.num_labels == 1       ,		)

										SCREAMING_SNAKE_CASE_						:			Dict       						= (logits[-1],)

										if labels is not None:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= None
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0
													for ix, logits_item in enumerate(lowerCAmelCase__    ):
																if self.num_labels == 1:
																			#  We are doing regression
																			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= MSELoss()
																			SCREAMING_SNAKE_CASE_						:			Dict       						= loss_fct(logits_item.view(-1    )       ,		labels.view(-1    )    )
																else:
																			SCREAMING_SNAKE_CASE_						:			int       						= CrossEntropyLoss()
																			SCREAMING_SNAKE_CASE_						:			str       						= loss_fct(logits_item.view(-1       ,		self.num_labels    )       ,		labels.view(-1    )    )
																if total_loss is None:
																			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= loss
																else:
																			total_loss += loss * (ix + 1)
																total_weights += ix + 1
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= (total_loss / total_weights,) + outputs

										return outputs






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



