


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

lowerCamelCase						=		logging.get_logger(__name__)


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

lowerCamelCase						=		{
    "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"
    },
}

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







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




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= (
		    list(range(ord('''!'''	)     ,       ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)     ,       ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)     ,       ord('''ÿ'''	) + 1	)	)
		)
		lowerCAmelCase__       :      List[Any]   					= bs[:]
		lowerCAmelCase__       :      Optional[int]   					= 0
		for b in range(2**8	):
				if b not in bs:
						bs.append(_a	)
						cs.append(2**8 + n	)
						n += 1
		lowerCAmelCase__       :      int   					= [chr(_a	) for n in cs]
		return dict(zip(_a     ,       _a	)	)







def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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





class  _a							(    lowercase_):
	_a  :  Union[str, Any]				   =							VOCAB_FILES_NAMES
	_a  :  int				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  List[Any]				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Union[str, Any]				   =							["input_ids", "attention_mask"]




	def __init__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		List[str]="replace"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<s>"		, _SCREAMING_SNAKE_CASE						:		Tuple="</s>"		, _SCREAMING_SNAKE_CASE						:		int="</s>"		, _SCREAMING_SNAKE_CASE						:		str="<s>"		, _SCREAMING_SNAKE_CASE						:		str="<unk>"		, _SCREAMING_SNAKE_CASE						:		List[Any]="<pad>"		, _SCREAMING_SNAKE_CASE						:		int="<mask>"		, _SCREAMING_SNAKE_CASE						:		Dict=False		, **_SCREAMING_SNAKE_CASE						:		Any		, )->	Any:
			lowerCAmelCase__       :      List[str]   					= AddedToken(a__		, lstrip=a__		, rstrip=a__		) if isinstance(a__		, a__		) else bos_token
			lowerCAmelCase__       :      Union[str, Any]   					= AddedToken(a__		, lstrip=a__		, rstrip=a__		) if isinstance(a__		, a__		) else eos_token
			lowerCAmelCase__       :      Any   					= AddedToken(a__		, lstrip=a__		, rstrip=a__		) if isinstance(a__		, a__		) else sep_token
			lowerCAmelCase__       :      Optional[Any]   					= AddedToken(a__		, lstrip=a__		, rstrip=a__		) if isinstance(a__		, a__		) else cls_token
			lowerCAmelCase__       :      Optional[int]   					= AddedToken(a__		, lstrip=a__		, rstrip=a__		) if isinstance(a__		, a__		) else unk_token
			lowerCAmelCase__       :      int   					= 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
			lowerCAmelCase__       :      Optional[int]   					= 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:
					lowerCAmelCase__       :      str   					= json.load(a__		)
			lowerCAmelCase__       :      Tuple   					= {v: k for k, v in self.encoder.items()}
			lowerCAmelCase__       :      Optional[Any]   					= errors  # how to handle errors in decoding
			lowerCAmelCase__       :      Any   					= bytes_to_unicode()
			lowerCAmelCase__       :      Optional[int]   					= {v: k for k, v in self.byte_encoder.items()}
			with open(a__		, encoding='''utf-8'''		) as merges_handle:
					lowerCAmelCase__       :      Union[str, Any]   					= merges_handle.read().split('''\n'''		)[1:-1]
			lowerCAmelCase__       :      int   					= [tuple(merge.split()		) for merge in bpe_merges]
			lowerCAmelCase__       :      Tuple   					= dict(zip(a__		, range(len(a__		)		)		)		)
			lowerCAmelCase__       :      Any   					= {}
			lowerCAmelCase__       :      Optional[Any]   					= add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			lowerCAmelCase__       :      List[Any]   					= 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 							UpperCAmelCase__( self						:		Any		)->	Tuple:
			return len(self.encoder		)




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




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Any:




			if token in self.cache:
					return self.cache[token]
			lowerCAmelCase__       :      Dict   					= tuple(a__		)
			lowerCAmelCase__       :      List[Any]   					= get_pairs(a__		)

			if not pairs:
					return token

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




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Optional[int]:
			lowerCAmelCase__       :      Optional[int]   					= []
			for token in re.findall(self.pat		, a__		):
					lowerCAmelCase__       :      Union[str, 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(a__		).split(''' '''		)		)
			return bpe_tokens




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	Optional[Any]:
			return self.encoder.get(a__		, self.encoder.get(self.unk_token		)		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Union[str, Any]:
			return self.decoder.get(a__		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	List[str]:
			lowerCAmelCase__       :      Dict   					= ''''''.join(a__		)
			lowerCAmelCase__       :      Optional[int]   					= bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''		, errors=self.errors		)
			return text




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Tuple = None		)->	Tuple[str]:




			if not os.path.isdir(a__		):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
					return
			lowerCAmelCase__       :      List[str]   					= os.path.join(
			    a__		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
			lowerCAmelCase__       :      Union[str, Any]   					= 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'''		)

			lowerCAmelCase__       :      Tuple   					= 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 _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!'''		)
									lowerCAmelCase__       :      Any   					= token_index
							writer.write(''' '''.join(a__		) + '''\n'''		)
							index += 1

			return vocab_file, merge_file




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		str = None		, _SCREAMING_SNAKE_CASE						:		Dict = False		)->	List[int]:




			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 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		List[Any] = None		)->	List[int]:
			lowerCAmelCase__       :      Tuple   					= [self.sep_token_id]
			lowerCAmelCase__       :      Dict   					= [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 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		str=False		, **_SCREAMING_SNAKE_CASE						:		List[str]		)->	Optional[Any]:
			lowerCAmelCase__       :      Dict   					= 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()):
					lowerCAmelCase__       :      Optional[int]   					= ''' ''' + text
			return (text, kwargs)




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any] = None		)->	str:
			return token_ids_a + [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	List[int]:
			lowerCAmelCase__       :      Union[str, 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(a__		)

			lowerCAmelCase__       :      str   					= '''  '''.join(a__		)
			lowerCAmelCase__       :      Any   					= self.encode(a__		)
			if len(a__		) > self.model_max_length:
					lowerCAmelCase__       :      str   					= 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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




