



"""simple docstring"""







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

import regex as re

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


A    =			logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
A    =			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A    =			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}

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

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Optional[Any]:
							A         =					set()
							A         =					word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)			)
														A         =					char
							return pairs






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Dict 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	str 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	Optional[Any] 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    List[Any]      ,							snake_case  :    List[Any]="replace"      ,							snake_case  :    List[Any]="<s>"      ,							snake_case  :    str="</s>"      ,							snake_case  :    Any="</s>"      ,							snake_case  :    int="<s>"      ,							snake_case  :    Tuple="<unk>"      ,							snake_case  :    Optional[int]="<pad>"      ,							snake_case  :    str="<mask>"      ,							snake_case  :    str=False      ,							**snake_case  :    List[str]      ,							)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else bos_token
									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else eos_token
									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else sep_token
									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else cls_token
									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else unk_token
									A         =					AddedToken(snake_case      ,							lstrip=snake_case      ,							rstrip=snake_case				) if isinstance(snake_case      ,							snake_case				) else pad_token

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

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

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

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



		@property
		def 					A_		( self  :    int				)			->						Any:






									'''simple docstring'''

									return len(self.encoder				)



		def 					A_		( self  :    Union[str, Any]				)			->						Tuple:






									'''simple docstring'''

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



		def 					A_		( self  :    Dict      ,							snake_case  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

									if token in self.cache:
																return self.cache[token]
									A         =					tuple(snake_case				)
									A         =					get_pairs(snake_case				)

									if not pairs:
																return token

									while True:
																A         =					min(snake_case      ,							key=lambda snake_case				: self.bpe_ranks.get(snake_case      ,							float('inf'				)				)				)
																if bigram not in self.bpe_ranks:
																							break
																A      ,  A         =					bigram
																A         =					[]
																A         =					0
																while i < len(snake_case				):
																							try:
																														A         =					word.index(snake_case      ,							snake_case				)
																							except ValueError:
																														new_word.extend(word[i:]				)
																														break
																							else:
																														new_word.extend(word[i:j]				)
																														A         =					j

																							if word[i] == first and i < len(snake_case				) - 1 and word[i + 1] == second:
																														new_word.append(first + second				)
																														i += 2
																							else:
																														new_word.append(word[i]				)
																														i += 1
																A         =					tuple(snake_case				)
																A         =					new_word
																if len(snake_case				) == 1:
																							break
																else:
																							A         =					get_pairs(snake_case				)
									A         =					' '.join(snake_case				)
									A         =					word
									return word



		def 					A_		( self  :    str      ,							snake_case  :    Dict				)			->						str:






									'''simple docstring'''

									A         =					[]
									for token in re.findall(self.pat      ,							snake_case				):
																A         =					''.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(snake_case				).split(' '				)				)
									return bpe_tokens



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    List[str]				)			->						int:






									'''simple docstring'''

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



		def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						List[str]:






									'''simple docstring'''

									return self.decoder.get(snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Any				)			->						List[Any]:






									'''simple docstring'''

									A         =					''.join(snake_case				)
									A         =					bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8'      ,							errors=self.errors				)
									return text



		def 					A_		( self  :    List[str]      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

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

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

									A         =					0
									with open(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 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         =					token_index
																							writer.write(' '.join(snake_case				) + '\n'				)
																							index += 1

									return vocab_file, merge_file



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    List[int]      ,							snake_case  :    Optional[List[int]] = None				)			->						List[int]:






									'''simple docstring'''

									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									A         =					[self.cls_token_id]
									A         =					[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



		def 					A_		( self  :    Dict      ,							snake_case  :    List[int]      ,							snake_case  :    Optional[List[int]] = None      ,							snake_case  :    bool = False				)			->						List[int]:






									'''simple docstring'''

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

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



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    List[int]      ,							snake_case  :    Optional[List[int]] = None				)			->						List[int]:






									'''simple docstring'''

									A         =					[self.sep_token_id]
									A         =					[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 					A_		( self  :    str      ,							snake_case  :    int      ,							snake_case  :    Dict=False      ,							**snake_case  :    Optional[int]				)			->						List[Any]:






									'''simple docstring'''

									A         =					kwargs.pop('add_prefix_space'      ,							self.add_prefix_space				)
									if (is_split_into_words or add_prefix_space) and (len(snake_case				) > 0 and not text[0].isspace()):
																A         =					' ' + text
									return (text, kwargs)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



