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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase							=       {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
}

UpperCAmelCase							=       {
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}






@lru_cache()
def 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			List[Any]       =       (
          list(range(ord("!"     ) ,	ord("~"     ) + 1     )     ) + list(range(ord("¡"     ) ,	ord("¬"     ) + 1     )     ) + list(range(ord("®"     ) ,	ord("ÿ"     ) + 1     )     )
      )
      snake_case_     :			Dict       =       bs[:]
      snake_case_     :			Optional[Any]       =       0
      for b in range(2**8     ):
            if b not in bs:
                  bs.append(lowerCAmelCase_     )
                  cs.append(2**8 + n     )
                  n += 1
      snake_case_     :			Tuple       =       [chr(lowerCAmelCase_     ) for n in cs]
      return dict(zip(lowerCAmelCase_ ,	lowerCAmelCase_     )     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Union[str, Any]     ):
      snake_case_     :			int       =       set()
      snake_case_     :			Dict       =       word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)     )
            snake_case_     :			Optional[Any]       =       char
      return pairs





class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE	: str						    =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE	: List[Any]						    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE	: Any						    =      ["input_ids", "attention_mask"]



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




         '''simple docstring'''




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

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



   @property
   def 					UpperCAmelCase__       (				self   :   Any   )							->     Any:




         '''simple docstring'''




         return len(self.encoder   )



   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Optional[int]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   Dict   )							->     Dict:




         '''simple docstring'''




         if token in self.cache:
               return self.cache[token]
         snake_case_     :			List[Any]       =       tuple(A__   )
         snake_case_     :			List[str]       =       get_pairs(A__   )

         if not pairs:
               return token

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



   def 					UpperCAmelCase__       (				self   :   Any			,	A__   :   Tuple   )							->     List[Any]:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       []
         for token in re.findall(self.pat			,	A__   ):
               snake_case_     :			str       =       "".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   :   Dict			,	A__   :   Union[str, Any]   )							->     Optional[int]:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   List[Any]   )							->     Union[str, Any]:




         '''simple docstring'''




         return self.decoder.get(A__   )



   def 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   Tuple   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			str       =       "".join(A__   )
         snake_case_     :			Tuple       =       bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"			,	errors=self.errors   )
         return text



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   str			,	A__   :   Optional[str] = None   )							->     Tuple[str]:




         '''simple docstring'''




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

         snake_case_     :			Optional[int]       =       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!"   )
                           snake_case_     :			int       =       token_index
                     writer.write(" ".join(A__   ) + "\n"   )
                     index += 1

         return vocab_file, merge_file



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[int]			,	A__   :   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]
         snake_case_     :			Any       =       [self.cls_token_id]
         snake_case_     :			Dict       =       [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   List[int]			,	A__   :   Optional[List[int]] = None			,	A__   :   bool = 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 					UpperCAmelCase__       (				self   :   str			,	A__   :   List[int]			,	A__   :   Optional[List[int]] = None   )							->     List[int]:




         '''simple docstring'''




         snake_case_     :			List[str]       =       [self.sep_token_id]
         snake_case_     :			List[Any]       =       [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   :   Tuple			,	A__   :   Union[str, Any]			,	A__   :   Tuple=False			,	**A__   :   List[Any]   )							->     Any:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       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()):
               snake_case_     :			Tuple       =       " " + text
         return (text, kwargs)




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



   @property
   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



