






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


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

lowerCamelCase			:					List[str]         =	{
    "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			:					Any         =	{"facebook/blenderbot-3B": 128}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __lowerCAmelCase      (						):
     __lowerCAmelCase								=					(
         list(range(ord("!"       )					, ord("~"       ) + 1       )       ) + list(range(ord("¡"       )					, ord("¬"       ) + 1       )       ) + list(range(ord("®"       )					, ord("ÿ"       ) + 1       )       )
     )
     __lowerCAmelCase								=					bs[:]
     __lowerCAmelCase								=					0
     for b in range(2**8       ):
          if b not in bs:
               bs.append(__A       )
               cs.append(2**8 + n       )
               n += 1
     __lowerCAmelCase								=					[chr(__A       ) for n in cs]
     return dict(zip(__A					, __A       )       )





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



class 							_UpperCamelCase			(__lowerCAmelCase			):
      snake_case_  =							VOCAB_FILES_NAMES
      snake_case_  =							PRETRAINED_VOCAB_FILES_MAP
      snake_case_  =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case_  =							["""input_ids""", """attention_mask"""]


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase="replace"					,						__UpperCamelCase="<s>"					,						__UpperCamelCase="</s>"					,						__UpperCamelCase="</s>"					,						__UpperCamelCase="<s>"					,						__UpperCamelCase="<unk>"					,						__UpperCamelCase="<pad>"					,						__UpperCamelCase="<mask>"					,						__UpperCamelCase=False					,						**__UpperCamelCase					,						)->	List[Any]:
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else bos_token
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else eos_token
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else sep_token
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else cls_token
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else unk_token
           __lowerCAmelCase								=					AddedToken(_UpperCamelCase					,						lstrip=_UpperCamelCase					,						rstrip=_UpperCamelCase	) if isinstance(_UpperCamelCase					,						_UpperCamelCase	) else pad_token

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

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

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

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           __lowerCAmelCase								=					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	)->	str:
           return len(self.encoder	)


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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Optional[Any]:


           if token in self.cache:
                return self.cache[token]
           __lowerCAmelCase								=					tuple(_UpperCamelCase	)
           __lowerCAmelCase								=					get_pairs(_UpperCamelCase	)

           if not pairs:
                return token

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

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for token in re.findall(self.pat					,						_UpperCamelCase	):
                __lowerCAmelCase								=					"""""".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(_UpperCamelCase	).split(" "	)	)
           return bpe_tokens


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Optional[int]:
           return self.encoder.get(_UpperCamelCase					,						self.encoder.get(self.unk_token	)	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           return self.decoder.get(_UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	List[Any]:
           __lowerCAmelCase								=					"""""".join(_UpperCamelCase	)
           __lowerCAmelCase								=					bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"					,						errors=self.errors	)
           return text


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None	)->	Tuple[str]:


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

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

           __lowerCAmelCase								=					0
           with open(_UpperCamelCase					,						"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 __UpperCamelCase	: 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								=					token_index
                     writer.write(" ".join(_UpperCamelCase	) + "\n"	)
                     index += 1

           return vocab_file, merge_file


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = False	)->	List[int]:


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

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None	)->	List[int]:
           __lowerCAmelCase								=					[self.sep_token_id]
           __lowerCAmelCase								=					[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					,						__UpperCamelCase					,						__UpperCamelCase=False					,						**__UpperCamelCase	)->	Optional[Any]:
           __lowerCAmelCase								=					kwargs.pop("add_prefix_space"					,						self.add_prefix_space	)
           if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase	) > 0 and not text[0].isspace()):
                __lowerCAmelCase								=					""" """ + text
           return (text, kwargs)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None	)->	Any:
           return token_ids_a + [self.eos_token_id]


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	List[int]:
           __lowerCAmelCase								=					[]
           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(_UpperCamelCase	)

           __lowerCAmelCase								=					"""  """.join(_UpperCamelCase	)
           __lowerCAmelCase								=					self.encode(_UpperCamelCase	)
           if len(_UpperCamelCase	) > self.model_max_length:
                __lowerCAmelCase								=					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






