
"""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

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


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

A__      :	Optional[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"""
    },
}

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



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






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						(
         list(range(ord("!"		)       ,    ord("~"		) + 1		)		) + list(range(ord("¡"		)       ,    ord("¬"		) + 1		)		) + list(range(ord("®"		)       ,    ord("ÿ"		) + 1		)		)
     )
     UpperCAmelCase__							: List[str] 							=						bs[:]
     UpperCAmelCase__							: Union[str, Any] 							=						0
     for b in range(2**8		):
          if b not in bs:
               bs.append(lowerCAmelCase		)
               cs.append(2**8 + n		)
               n += 1
     UpperCAmelCase__							: Optional[Any] 							=						[chr(lowerCAmelCase		) for n in cs]
     return dict(zip(lowerCAmelCase       ,    lowerCAmelCase		)		)



def 							a__ (				lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						set()
     UpperCAmelCase__							: Dict 							=						word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)		)
          UpperCAmelCase__							: List[Any] 							=						char
     return pairs







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							['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 ,			)->					Optional[Any]:
          UpperCAmelCase__							: Optional[int] 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else bos_token
          UpperCAmelCase__							: Optional[Any] 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else eos_token
          UpperCAmelCase__							: int 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else sep_token
          UpperCAmelCase__							: Optional[int] 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else cls_token
          UpperCAmelCase__							: Tuple 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else unk_token
          UpperCAmelCase__							: str 							=						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
          UpperCAmelCase__							: Union[str, Any] 							=						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:
               UpperCAmelCase__							: Dict 							=						json.load(__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						{v: k for k, v in self.encoder.items()}
          UpperCAmelCase__							: Optional[int] 							=						errors  # how to handle errors in decoding
          UpperCAmelCase__							: Dict 							=						bytes_to_unicode()
          UpperCAmelCase__							: Union[str, Any] 							=						{v: k for k, v in self.byte_encoder.items()}
          with open(__UpperCamelCase ,			encoding="utf-8"  ) as merges_handle:
               UpperCAmelCase__							: str 							=						merges_handle.read().split("\n"  )[1:-1]
          UpperCAmelCase__							: Optional[int] 							=						[tuple(merge.split()  ) for merge in bpe_merges]
          UpperCAmelCase__							: str 							=						dict(zip(__UpperCamelCase ,			range(len(__UpperCamelCase  )  )  )  )
          UpperCAmelCase__							: int 							=						{}
          UpperCAmelCase__							: str 							=						add_prefix_space

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



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if token in self.cache:
               return self.cache[token]
          UpperCAmelCase__							: Optional[Any] 							=						tuple(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						get_pairs(__UpperCamelCase  )

          if not pairs:
               return token

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: Any 							=						[]
          for token in re.findall(self.pat ,			__UpperCamelCase  ):
               UpperCAmelCase__							: Dict 							=						"".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  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          return self.encoder.get(__UpperCamelCase ,			self.encoder.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          return self.decoder.get(__UpperCamelCase  )



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          if not os.path.isdir(__UpperCamelCase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
               return
          UpperCAmelCase__							: Tuple 							=						os.path.join(
              __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
          UpperCAmelCase__							: List[str] 							=						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"  )

          UpperCAmelCase__							: Dict 							=						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!"  )
                         UpperCAmelCase__							: str 							=						token_index
                    writer.write(" ".join(__UpperCamelCase  ) + "\n"  )
                    index += 1

          return vocab_file, merge_file



     def  lowerCAmelCase__       (      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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:
          UpperCAmelCase__							: Optional[int] 							=						[self.sep_token_id]
          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			**__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: str 							=						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()):
               UpperCAmelCase__							: Optional[int] 							=						" " + text
          return (text, kwargs)



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[int]:
          UpperCAmelCase__							: 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(__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						"  ".join(__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						self.encode(__UpperCamelCase  )
          if len(__UpperCamelCase  ) > self.model_max_length:
               UpperCAmelCase__							: List[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





"""simple docstring"""




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors="pt" if labels is None else None ,			)

          if labels is None:
               return batch

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



