



'''simple docstring'''





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_	 =						logging.get_logger(__name__)


lowercase_	 =						{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_	 =						{
    "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_	 =						{"facebook/blenderbot-3B": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase						():



					"""simple docstring"""



					_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(__A)
															cs.append(2**8 + n)
															n += 1
					_a											=						[chr(__A) for n in cs]
					return dict(zip(__A						,			__A))




def lowerCAmelCase						(__A):



					"""simple docstring"""



					_a											=						set()
					_a											=						word[0]
					for char in word[1:]:
										pairs.add((prev_char, char))
										_a											=						char
					return pairs






class __A    (						A		):



	'''simple docstring'''







	__lowerCamelCase					: Optional[int]    = VOCAB_FILES_NAMES
	__lowerCamelCase					: Tuple    = PRETRAINED_VOCAB_FILES_MAP
	__lowerCamelCase					: int    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__lowerCamelCase					: str    = ['input_ids', 'attention_mask']



	def __init__(self				,     A				,     A				,     A="replace"				,     A="<s>"				,     A="</s>"				,     A="</s>"				,     A="<s>"				,     A="<unk>"				,     A="<pad>"				,     A="<mask>"				,     A=False				,     **A				,     )					->  Optional[Any]:


						"""simple docstring"""



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

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

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

						with open(A				,     encoding='''utf-8''' ) as vocab_handle:
											_a											=						json.load(A )
						_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(A				,     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(A				,     range(len(A ) ) ) )
						_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
	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
	def       a__						(self )					->  str:


						"""simple docstring"""



						return len(self.encoder )



	def       a__						(self )					->  Dict:


						"""simple docstring"""



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



	def       a__						(self				,     A )					->  int:


						"""simple docstring"""



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

						if not pairs:
											return token

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

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



	def       a__						(self				,     A )					->  List[str]:


						"""simple docstring"""



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



	def       a__						(self				,     A )					->  List[Any]:


						"""simple docstring"""



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



	def       a__						(self				,     A )					->  Dict:


						"""simple docstring"""



						return self.decoder.get(A )



	def       a__						(self				,     A )					->  Union[str, Any]:


						"""simple docstring"""



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



	def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


						"""simple docstring"""



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

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

						_a											=						0
						with open(A				,     '''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 A : 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(A ) + '''\n''' )
																index += 1

						return vocab_file, merge_file



	def       a__						(self				,     A				,     A = None				,     A = False )					->  List[int]:


						"""simple docstring"""



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

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



	def       a__						(self				,     A				,     A = 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				,     A				,     A=False				,     **A )					->  Union[str, Any]:


						"""simple docstring"""



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



	def       a__						(self				,     A				,     A = None )					->  Optional[int]:


						"""simple docstring"""



						return token_ids_a + [self.eos_token_id]



	def       a__						(self				,     A )					->  List[int]:


						"""simple docstring"""



						_a											=						[]
						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(A )

						_a											=						'''  '''.join(A )
						_a											=						self.encode(A )
						if len(A ) > self.model_max_length:
											_a											=						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








'''simple docstring'''





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


lowercase_	 =						open  # noqa: we just need to have a builtin inside this module to test it properly



