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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

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


_lowercase							:      Dict      							={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase							:      Any      							={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_lowercase							:      Any      							={'''facebook/blenderbot-3B''': 1_2_8}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def A__  (			)				->		str:
       A						:     Optional[int]         =(
           list(range(ord('!'					),		ord('~'					) + 1					)					) + list(range(ord('¡'					),		ord('¬'					) + 1					)					) + list(range(ord('®'					),		ord('ÿ'					) + 1					)					)
       )
       A						:     Union[str, Any]         =bs[:]
       A						:     Optional[int]         =0
       for b in range(2**8					):
              if b not in bs:
                     bs.append(lowercase					)
                     cs.append(2**8 + n					)
                     n += 1
       A						:     Any         =[chr(lowercase					) for n in cs]
       return dict(zip(lowercase,		lowercase					)					)



def A__  (			lowercase:     Dict					)				->		Any:
       A						:     List[str]         =set()
       A						:     Tuple         =word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)					)
              A						:     List[str]         =char
       return pairs



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				VOCAB_FILES_NAMES
  lowercase		:     Optional[Any]            =				PRETRAINED_VOCAB_FILES_MAP
  lowercase		:     int            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase		:     Union[str, Any]            =				["input_ids", "attention_mask"]







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str="replace"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="<s>"      ,	SCREAMING_SNAKE_CASE__    :					str="</s>"      ,	SCREAMING_SNAKE_CASE__    :					str="</s>"      ,	SCREAMING_SNAKE_CASE__    :					Any="<s>"      ,	SCREAMING_SNAKE_CASE__    :					Tuple="<unk>"      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]="<pad>"      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]="<mask>"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=False      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				str:
         A						:     List[Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else bos_token
         A						:     List[Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else eos_token
         A						:     str         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else sep_token
         A						:     Tuple         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else cls_token
         A						:     List[Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else unk_token
         A						:     List[Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         A						:     List[Any]         =AddedToken(SCREAMING_SNAKE_CASE__      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ) else mask_token

         super().__init__(
             errors=SCREAMING_SNAKE_CASE__      ,	bos_token=SCREAMING_SNAKE_CASE__      ,	eos_token=SCREAMING_SNAKE_CASE__      ,	unk_token=SCREAMING_SNAKE_CASE__      ,	sep_token=SCREAMING_SNAKE_CASE__      ,	cls_token=SCREAMING_SNAKE_CASE__      ,	pad_token=SCREAMING_SNAKE_CASE__      ,	mask_token=SCREAMING_SNAKE_CASE__      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)

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

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







  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         return len(self.encoder     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[int]:
         return dict(self.encoder      ,	**self.added_tokens_encoder     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Optional[int]:
         if token in self.cache:
                return self.cache[token]
         A						:     Union[str, Any]         =tuple(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =get_pairs(SCREAMING_SNAKE_CASE__     )

         if not pairs:
                return token

         while True:
                A						:     Dict         =min(SCREAMING_SNAKE_CASE__      ,	key=lambda SCREAMING_SNAKE_CASE__     : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__      ,	float('inf'     )     )     )
                if bigram not in self.bpe_ranks:
                       break
                A  ,  A						:     str         =bigram
                A						:     Optional[int]         =[]
                A						:     List[str]         =0
                while i < len(SCREAMING_SNAKE_CASE__     ):
                       try:
                              A						:     str         =word.index(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                       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(SCREAMING_SNAKE_CASE__     ) - 1 and word[i + 1] == second:
                              new_word.append(first + second     )
                              i += 2
                       else:
                              new_word.append(word[i]     )
                              i += 1
                A						:     List[Any]         =tuple(SCREAMING_SNAKE_CASE__     )
                A						:     Optional[int]         =new_word
                if len(SCREAMING_SNAKE_CASE__     ) == 1:
                       break
                else:
                       A						:     Optional[int]         =get_pairs(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =' '.join(SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =word
         return word







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				List[Any]:
         A						:     Union[str, Any]         =[]
         for token in re.findall(self.pat      ,	SCREAMING_SNAKE_CASE__     ):
                A						:     Optional[Any]         =''.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(SCREAMING_SNAKE_CASE__     ).split(' '     )     )
         return bpe_tokens







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Union[str, Any]:
         return self.encoder.get(SCREAMING_SNAKE_CASE__      ,	self.encoder.get(self.unk_token     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Dict:
         return self.decoder.get(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				str:
         A						:     Optional[int]         =''.join(SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'      ,	errors=self.errors     )
         return text







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[str] = None     )    ->				Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
         A						:     Union[str, Any]         =os.path.join(
             SCREAMING_SNAKE_CASE__      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
         A						:     Optional[Any]         =os.path.join(
             SCREAMING_SNAKE_CASE__      ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

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

         A						:     Dict         =0
         with open(SCREAMING_SNAKE_CASE__      ,	'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 SCREAMING_SNAKE_CASE__     : 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						:     str         =token_index
                       writer.write(' '.join(SCREAMING_SNAKE_CASE__     ) + '\n'     )
                       index += 1

         return vocab_file, merge_file







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False     )    ->				List[int]:
         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=SCREAMING_SNAKE_CASE__      ,	token_ids_a=SCREAMING_SNAKE_CASE__      ,	already_has_special_tokens=SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				List[int]:
         A						:     Any         =[self.sep_token_id]
         A						:     Optional[Any]         =[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 + sep + token_ids_a + sep     ) * [0]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=False      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				str:
         A						:     List[str]         =kwargs.pop('add_prefix_space'      ,	self.add_prefix_space     )
         if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__     ) > 0 and not text[0].isspace()):
                A						:     Optional[Any]         =' ' + text
         return (text, kwargs)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[List[int]] = None     )    ->				Any:
         return token_ids_a + [self.eos_token_id]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					"Conversation"     )    ->				List[int]:
         A						:     List[Any]         =[]
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(' ' + text     )
                else:
                       # Generated responses should contain them already.
                       inputs.append(SCREAMING_SNAKE_CASE__     )

         A						:     str         ='  '.join(SCREAMING_SNAKE_CASE__     )
         A						:     str         =self.encode(SCREAMING_SNAKE_CASE__     )
         if len(SCREAMING_SNAKE_CASE__     ) > self.model_max_length:
                A						:     Optional[Any]         =input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
         return input_ids







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase							:      List[Any]      							=logging.get_logger(__name__)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					float      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				int:
         A						:     Tuple         =feature_size
         A						:     int         =sampling_rate
         A						:     List[str]         =padding_value

         A						:     Tuple         =kwargs.pop('padding_side'      ,	'right'     )
         A						:     str         =kwargs.pop('return_attention_mask'      ,	SCREAMING_SNAKE_CASE__     )

         super().__init__(**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]      ,	SCREAMING_SNAKE_CASE__    :					Union[bool, str, PaddingStrategy] = True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, TensorType]] = None      ,	)    ->				BatchFeature:
         # If we have a list of dicts, let's convert it in a dict of lists
         # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
         if isinstance(SCREAMING_SNAKE_CASE__      ,	(list, tuple)     ) and isinstance(processed_features[0]      ,	(dict, BatchFeature)     ):
                A						:     Tuple         ={
                    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
                raise ValueError(
                    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                    f' to this method that includes {self.model_input_names[0]}, but you provided'
                    f' {list(processed_features.keys()     )}'     )

         A						:     Dict         =processed_features[self.model_input_names[0]]
         A						:     int         =(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(SCREAMING_SNAKE_CASE__     ) == 0:
                if return_attention_mask:
                       A						:     List[Any]         =[]
                return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         A						:     List[str]         =required_input[0]
         if isinstance(SCREAMING_SNAKE_CASE__      ,	(list, tuple)     ):
                # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                A						:     Any         =0
                while len(required_input[index]     ) == 0:
                       index += 1
                if index < len(SCREAMING_SNAKE_CASE__     ):
                       A						:     Dict         =required_input[index][0]

         if return_tensors is None:
                if is_tf_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     List[Any]         ='tf'
                elif is_torch_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         ='pt'
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	(int, float, list, tuple, np.ndarray)     ):
                       A						:     Union[str, Any]         ='np'
                else:
                       raise ValueError(
                           f'type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE__     )}. '
                           'Should be one of a python, numpy, pytorch or tensorflow object.'     )

         for key, value in processed_features.items():
                if isinstance(value[0]      ,	(int, float)     ):
                       A						:     int         =to_numpy(SCREAMING_SNAKE_CASE__     )
                else:
                       A						:     List[Any]         =[to_numpy(SCREAMING_SNAKE_CASE__     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         A						:     List[Any]         =self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__      ,	max_length=SCREAMING_SNAKE_CASE__     )

         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )
         if not all(len(SCREAMING_SNAKE_CASE__     ) == batch_size for v in processed_features.values()     ):
                raise ValueError('Some items in the output dictionary have a different batch size than others.'     )

         A						:     Tuple         =[]
         for i in range(SCREAMING_SNAKE_CASE__     ):
                A						:     int         ={k: v[i] for k, v in processed_features.items()}
                # truncation
                A						:     List[Any]         =self._truncate(
                    SCREAMING_SNAKE_CASE__      ,	max_length=SCREAMING_SNAKE_CASE__      ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE__      ,	truncation=SCREAMING_SNAKE_CASE__      ,	)
                truncated_inputs.append(SCREAMING_SNAKE_CASE__     )

         if padding_strategy == PaddingStrategy.LONGEST:
                # make sure that `max_length` cannot be longer than the longest truncated length
                A						:     Any         =max(len(input_slice[self.model_input_names[0]]     ) for input_slice in truncated_inputs     )
                A						:     Optional[Any]         =PaddingStrategy.MAX_LENGTH

         A						:     List[Any]         ={}
         for i in range(SCREAMING_SNAKE_CASE__     ):
                # padding
                A						:     Optional[Any]         =self._pad(
                    truncated_inputs[i]      ,	max_length=SCREAMING_SNAKE_CASE__      ,	padding_strategy=SCREAMING_SNAKE_CASE__      ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE__      ,	return_attention_mask=SCREAMING_SNAKE_CASE__      ,	)

                for key, value in outputs.items():
                       if key not in batch_outputs:
                              A						:     Dict         =[]
                       if value.dtype is np.dtype(np.floataa     ):
                              A						:     Tuple         =value.astype(np.floataa     )
                       batch_outputs[key].append(SCREAMING_SNAKE_CASE__     )

         return BatchFeature(SCREAMING_SNAKE_CASE__      ,	tensor_type=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				dict:
         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
                A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                A						:     Tuple         =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         A						:     int         =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__     ) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
                A						:     str         =np.ones(len(SCREAMING_SNAKE_CASE__     )      ,	dtype=np.intaa     )

         if needs_to_be_padded:
                A						:     Union[str, Any]         =max_length - len(SCREAMING_SNAKE_CASE__     )
                if self.padding_side == "right":
                       if return_attention_mask:
                              A						:     Dict         =np.pad(
                                  processed_features['attention_mask']      ,	(0, difference)     )
                       A						:     str         =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                       A						:     Tuple         =np.pad(
                           SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'constant'      ,	constant_values=self.padding_value     )
                elif self.padding_side == "left":
                       if return_attention_mask:
                              A						:     List[Any]         =np.pad(
                                  processed_features['attention_mask']      ,	(difference, 0)     )
                       A						:     Union[str, Any]         =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                       A						:     Tuple         =np.pad(
                           SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'constant'      ,	constant_values=self.padding_value     )
                else:
                       raise ValueError('Invalid padding strategy:' + str(self.padding_side     )     )

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				Optional[Any]:
         if not truncation:
                return processed_features
         elif truncation and max_length is None:
                raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'     )

         A						:     Tuple         =processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                A						:     Any         =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     ) > max_length

         if needs_to_be_truncated:
                A						:     Union[str, Any]         =processed_features[self.model_input_names[0]][:max_length]
                if "attention_mask" in processed_features:
                       A						:     Dict         =processed_features['attention_mask'][:max_length]

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					Dict=None     )    ->				Union[str, Any]:

         # Get padding strategy
         if padding is not False:
                if padding is True:
                       A						:     List[Any]         =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                elif not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Tuple         =PaddingStrategy(SCREAMING_SNAKE_CASE__     )
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         =padding
         else:
                A						:     List[str]         =PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
                if padding_strategy == PaddingStrategy.MAX_LENGTH:
                       raise ValueError(
                           f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'     )

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                raise ValueError(
                    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'     )

         return padding_strategy






