



"""simple docstring"""




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase							:   Dict				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Union[str, Any]				=				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_lowerCAmelCase							:   Union[str, Any]				=				{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_lowerCAmelCase							:   Any				=				{
    "ctrl": 2_56,
}

_lowerCAmelCase							:   Union[str, Any]				=				{
    "Pregnancy": 16_86_29,
    "Christianity": 76_75,
    "Explain": 10_64_23,
    "Fitness": 6_34_40,
    "Saving": 6_31_63,
    "Ask": 2_71_71,
    "Ass": 9_59_85,
    "Joke": 16_35_09,
    "Questions": 4_56_22,
    "Thoughts": 4_96_05,
    "Retail": 5_23_42,
    "Feminism": 16_43_38,
    "Writing": 1_19_92,
    "Atheism": 19_22_63,
    "Netflix": 4_86_16,
    "Computing": 3_96_39,
    "Opinion": 4_32_13,
    "Alone": 4_49_67,
    "Funny": 5_89_17,
    "Gaming": 4_03_58,
    "Human": 40_88,
    "India": 13_31,
    "Joker": 7_71_38,
    "Diet": 3_62_06,
    "Legal": 1_18_59,
    "Norman": 49_39,
    "Tip": 7_26_89,
    "Weight": 5_23_43,
    "Movies": 4_62_73,
    "Running": 2_34_25,
    "Science": 20_90,
    "Horror": 3_77_93,
    "Confession": 6_05_72,
    "Finance": 1_22_50,
    "Politics": 1_63_60,
    "Scary": 19_19_85,
    "Support": 1_26_54,
    "Technologies": 3_25_16,
    "Teenage": 6_61_60,
    "Event": 3_27_69,
    "Learned": 6_74_60,
    "Notion": 18_27_70,
    "Wikipedia": 3_75_83,
    "Books": 66_65,
    "Extract": 7_60_50,
    "Confessions": 10_27_01,
    "Conspiracy": 7_59_32,
    "Links": 6_36_74,
    "Narcissus": 15_04_25,
    "Relationship": 5_47_66,
    "Relationships": 13_47_96,
    "Reviews": 4_16_71,
    "News": 42_56,
    "Translation": 2_68_20,
    "multilingual": 12_84_06,
}

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       set()
     _UpperCAmelCase     :      List[str]														=       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          _UpperCAmelCase     :      List[Any]														=       char

     _UpperCAmelCase     :      List[Any]														=       set(SCREAMING_SNAKE_CASE__							)
     return pairs






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Dict						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  Tuple						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Any						=					CONTROL_CODES



      def __init__(	self	:      List[str]	,      A	:      Any	,      A	:      Any	,      A	:      List[Any]="<unk>"	,      **A	:      str		):
           super().__init__(unk_token=A	,      **A		)

           with open(A	,      encoding="utf-8"		) as vocab_handle:
                _UpperCAmelCase     :      List[Any]														=       json.load(A		)
           _UpperCAmelCase     :      Dict														=       {v: k for k, v in self.encoder.items()}
           with open(A	,      encoding="utf-8"		) as merges_handle:
                _UpperCAmelCase     :      Dict														=       merges_handle.read().split("\n"		)[1:-1]
           _UpperCAmelCase     :      List[Any]														=       [tuple(merge.split()		) for merge in merges]
           _UpperCAmelCase     :      Optional[int]														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Union[str, Any]														=       {}



      @property
      def 		snake_case_   (	self	:      int		):
           return len(self.encoder		)



      def 		snake_case_   (	self	:      Any		):
           return dict(self.encoder	,      **self.added_tokens_encoder		)



      def 		snake_case_   (	self	:      str	,      A	:      List[str]		):
           if token in self.cache:
                return self.cache[token]
           _UpperCAmelCase     :      Tuple														=       tuple(A		)
           _UpperCAmelCase     :      Optional[int]														=       tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
           _UpperCAmelCase     :      Any														=       get_pairs(A		)

           if not pairs:
                return token

           while True:
                _UpperCAmelCase     :      str														=       min(A	,      key=lambda A		: self.bpe_ranks.get(A	,      float("inf"		)		)		)
                if bigram not in self.bpe_ranks:
                     break
                _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       bigram
                _UpperCAmelCase     :      Dict														=       []
                _UpperCAmelCase     :      int														=       0
                while i < len(A		):
                     try:
                          _UpperCAmelCase     :      Tuple														=       word.index(A	,      A		)
                     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(A		) - 1 and word[i + 1] == second:
                          new_word.append(first + second		)
                          i += 2
                     else:
                          new_word.append(word[i]		)
                          i += 1
                _UpperCAmelCase     :      Union[str, Any]														=       tuple(A		)
                _UpperCAmelCase     :      Union[str, Any]														=       new_word
                if len(A		) == 1:
                     break
                else:
                     _UpperCAmelCase     :      List[Any]														=       get_pairs(A		)
           _UpperCAmelCase     :      Any														=       "@@ ".join(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       word[:-4]
           _UpperCAmelCase     :      Optional[int]														=       word
           return word



      def 		snake_case_   (	self	:      int	,      A	:      str		):
           _UpperCAmelCase     :      Dict														=       []

           _UpperCAmelCase     :      Optional[int]														=       re.findall(R"\S+\n?"	,      A		)

           for token in words:
                split_tokens.extend(list(self.bpe(A		).split(" "		)		)		)
           return split_tokens



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Any		):
           return self.encoder.get(A	,      self.encoder.get(self.unk_token		)		)



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      int		):
           return self.decoder.get(A	,      self.unk_token		)



      def 		snake_case_   (	self	:      Any	,      A	:      Optional[int]		):
           _UpperCAmelCase     :      Dict														=       " ".join(A		).replace("@@ "	,      ""		).strip()
           return out_string



      def 		snake_case_   (	self	:      List[Any]	,      A	:      str	,      A	:      Optional[str] = None		):
           if not os.path.isdir(A		):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
                return
           _UpperCAmelCase     :      Optional[Any]														=       os.path.join(
               A	,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      Tuple														=       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"		)

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

           return vocab_file, merge_file

      # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
      #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
      #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
      #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
      #     return ''.join(tokens_generated_so_far)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase							:   Optional[int]				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
