




'''simple docstring'''
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


A							:				Optional[int]			     =	logging.get_logger(__name__)






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_   = 42
       snake_case_   = 42
       snake_case_   = None
       snake_case_   = None

       def    snake_case__      (   self					)   -> int:

           """simple docstring"""



           return json.dumps(dataclasses.asdict(self					) ,		indent=2					) + "\n"






@dataclass(frozen=_UpperCAmelCase					)
class 	SCREAMING_SNAKE_CASE:
       snake_case_   = 42
       snake_case_   = None
       snake_case_   = None
       snake_case_   = None

       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           return json.dumps(dataclasses.asdict(self					)					) + "\n"






class 	SCREAMING_SNAKE_CASE:

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           raise NotImplementedError()

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Optional[Any]:

           """simple docstring"""



           raise NotImplementedError()

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[Any]:

           """simple docstring"""



           raise NotImplementedError()

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[Any]:

           """simple docstring"""



           raise NotImplementedError()

       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           raise NotImplementedError()

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Dict:

           """simple docstring"""



           if len(self.get_labels()					) > 1:
               __lowercase          =						self.get_labels()[int(example.label					)]
           return example




       @classmethod
       def    snake_case__      (   cls ,		lowerCamelCase__ ,		lowerCamelCase__=None					)   -> Dict:

           """simple docstring"""



           with open(lowerCAmelCase__ ,		"""r""" ,		encoding="""utf-8-sig"""					) as f:
               return list(csv.reader(lowerCAmelCase__ ,		delimiter="""\t""" ,		quotechar=lowerCAmelCase__					)					)






class 	SCREAMING_SNAKE_CASE(    _UpperCAmelCase					):

       def __init__(   self ,		lowerCamelCase__=None ,		lowerCamelCase__=None ,		lowerCamelCase__="classification" ,		lowerCamelCase__=False					)   -> Dict:

           """simple docstring"""



           __lowercase          =						[] if labels is None else labels
           __lowercase          =						[] if examples is None else examples
           __lowercase          =						mode
           __lowercase          =						verbose

       def __len__(   self					)   -> Tuple:

           """simple docstring"""



           return len(self.examples					)

       def __getitem__(   self ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           if isinstance(lowerCAmelCase__ ,		lowerCAmelCase__					):
               return SingleSentenceClassificationProcessor(labels=self.labels ,		examples=self.examples[idx]					)
           return self.examples[idx]

       @classmethod
       def    snake_case__      (   cls ,		lowerCamelCase__ ,		lowerCamelCase__="" ,		lowerCamelCase__=0 ,		lowerCamelCase__=1 ,		lowerCamelCase__=None ,		lowerCamelCase__=False ,		**lowerCamelCase__					)   -> Any:

           """simple docstring"""



           __lowercase          =						cls(**lowerCAmelCase__					)
           processor.add_examples_from_csv(
               lowerCAmelCase__ ,		split_name=lowerCAmelCase__ ,		column_label=lowerCAmelCase__ ,		column_text=lowerCAmelCase__ ,		column_id=lowerCAmelCase__ ,		skip_first_row=lowerCAmelCase__ ,		overwrite_labels=lowerCAmelCase__ ,		overwrite_examples=lowerCAmelCase__ ,		)
           return processor

       @classmethod
       def    snake_case__      (   cls ,		lowerCamelCase__ ,		lowerCamelCase__=None ,		**lowerCamelCase__					)   -> List[Any]:

           """simple docstring"""



           __lowercase          =						cls(**lowerCAmelCase__					)
           processor.add_examples(lowerCAmelCase__ ,		labels=lowerCAmelCase__					)
           return processor

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__="" ,		lowerCamelCase__=0 ,		lowerCamelCase__=1 ,		lowerCamelCase__=None ,		lowerCamelCase__=False ,		lowerCamelCase__=False ,		lowerCamelCase__=False ,		)   -> List[str]:

           """simple docstring"""



           __lowercase          =						self._read_tsv(lowerCAmelCase__					)
           if skip_first_row:
               __lowercase          =						lines[1:]
           __lowercase          =						[]
           __lowercase          =						[]
           __lowercase          =						[]
           for i, line in enumerate(lowerCAmelCase__					):
               texts.append(line[column_text]					)
               labels.append(line[column_label]					)
               if column_id is not None:
                   ids.append(line[column_id]					)
               else:
                   __lowercase          =						F'{split_name}-{i}' if split_name else str(lowerCAmelCase__					)
                   ids.append(lowerCAmelCase__					)

           return self.add_examples(
               lowerCAmelCase__ ,		lowerCAmelCase__ ,		lowerCAmelCase__ ,		overwrite_labels=lowerCAmelCase__ ,		overwrite_examples=lowerCAmelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=None ,		lowerCamelCase__=None ,		lowerCamelCase__=False ,		lowerCamelCase__=False					)   -> Any:

           """simple docstring"""



           if labels is not None and len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
               raise ValueError(
                   F'Text and labels have mismatched lengths {len(lowerCAmelCase__					)} and {len(lowerCAmelCase__					)}'					)
           if ids is not None and len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
               raise ValueError(F'Text and ids have mismatched lengths {len(lowerCAmelCase__					)} and {len(lowerCAmelCase__					)}'					)
           if ids is None:
               __lowercase          =						[None] * len(lowerCAmelCase__					)
           if labels is None:
               __lowercase          =						[None] * len(lowerCAmelCase__					)
           __lowercase          =						[]
           __lowercase          =						set()
           for text_or_text_and_label, label, guid in zip(lowerCAmelCase__ ,		lowerCAmelCase__ ,		lowerCAmelCase__					):
               if isinstance(lowerCAmelCase__ ,		(tuple, list)					) and label is None:
                   __lowercase    ,__lowercase          =						text_or_text_and_label
               else:
                   __lowercase          =						text_or_text_and_label
               added_labels.add(lowerCAmelCase__					)
               examples.append(InputExample(guid=lowerCAmelCase__ ,		text_a=lowerCAmelCase__ ,		text_b=lowerCAmelCase__ ,		label=lowerCAmelCase__					)					)

           # Update examples
           if overwrite_examples:
               __lowercase          =						examples
           else:
               self.examples.extend(lowerCAmelCase__					)

           # Update labels
           if overwrite_labels:
               __lowercase          =						list(lowerCAmelCase__					)
           else:
               __lowercase          =						list(set(self.labels					).union(lowerCAmelCase__					)					)

           return self.examples




       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=None ,		lowerCamelCase__=False ,		lowerCamelCase__=0 ,		lowerCamelCase__=True ,		lowerCamelCase__=None ,		)   -> str:

           """simple docstring"""



           if max_length is None:
               __lowercase          =						tokenizer.max_len

           __lowercase          =						{label: i for i, label in enumerate(self.labels					)}

           __lowercase          =						[]
           for ex_index, example in enumerate(self.examples					):
               if ex_index % 1_0000 == 0:
                   logger.info(F'Tokenizing example {ex_index}'					)

               __lowercase          =						tokenizer.encode(
                   example.text_a ,		add_special_tokens=lowerCAmelCase__ ,		max_length=min(lowerCAmelCase__ ,		tokenizer.max_len					) ,		)
               all_input_ids.append(lowerCAmelCase__					)

           __lowercase          =						max(len(lowerCAmelCase__					) for input_ids in all_input_ids					)

           __lowercase          =						[]
           for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__ ,		self.examples					)					):
               if ex_index % 1_0000 == 0:
                   logger.info(F'Writing example {ex_index}/{len(self.examples					)}'					)
               # The mask has 1 for real tokens and 0 for padding tokens. Only real
               # tokens are attended to.
               __lowercase          =						[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__					)

               # Zero-pad up to the sequence length.
               __lowercase          =						batch_length - len(lowerCAmelCase__					)
               if pad_on_left:
                   __lowercase          =						([pad_token] * padding_length) + input_ids
                   __lowercase          =						([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
               else:
                   __lowercase          =						input_ids + ([pad_token] * padding_length)
                   __lowercase          =						attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

               if len(lowerCAmelCase__					) != batch_length:
                   raise ValueError(F'Error with input length {len(lowerCAmelCase__					)} vs {batch_length}'					)
               if len(lowerCAmelCase__					) != batch_length:
                   raise ValueError(F'Error with input length {len(lowerCAmelCase__					)} vs {batch_length}'					)

               if self.mode == "classification":
                   __lowercase          =						label_map[example.label]
               elif self.mode == "regression":
                   __lowercase          =						float(example.label					)
               else:
                   raise ValueError(self.mode					)

               if ex_index < 5 and self.verbose:
                   logger.info("""*** Example ***"""					)
                   logger.info(F'guid: {example.guid}'					)
                   logger.info(F'input_ids: {" ".join([str(lowerCAmelCase__					) for x in input_ids]					)}'					)
                   logger.info(F'attention_mask: {" ".join([str(lowerCAmelCase__					) for x in attention_mask]					)}'					)
                   logger.info(F'label: {example.label} (id = {label})'					)

               features.append(InputFeatures(input_ids=lowerCAmelCase__ ,		attention_mask=lowerCAmelCase__ ,		label=lowerCAmelCase__					)					)

           if return_tensors is None:
               return features
           elif return_tensors == "tf":
               if not is_tf_available():
                   raise RuntimeError("""return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported"""					)
               import tensorflow as tf

               def gen():
                   for ex in features:
                       yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

               __lowercase          =						tf.data.Dataset.from_generator(
                   lowerCAmelCase__ ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) ,		({"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)}, tf.TensorShape([]					)) ,		)
               return dataset
           elif return_tensors == "pt":
               if not is_torch_available():
                   raise RuntimeError("""return_tensors set to \'pt\' but PyTorch can\'t be imported"""					)
               import torch
               from torch.utils.data import TensorDataset

               __lowercase          =						torch.tensor([f.input_ids for f in features] ,		dtype=torch.long					)
               __lowercase          =						torch.tensor([f.attention_mask for f in features] ,		dtype=torch.long					)
               if self.mode == "classification":
                   __lowercase          =						torch.tensor([f.label for f in features] ,		dtype=torch.long					)
               elif self.mode == "regression":
                   __lowercase          =						torch.tensor([f.label for f in features] ,		dtype=torch.float					)

               __lowercase          =						TensorDataset(lowerCAmelCase__ ,		lowerCAmelCase__ ,		lowerCAmelCase__					)
               return dataset
           else:
               raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""					)










'''simple docstring'''
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


A							:				int			     =	logging.get_logger(__name__)

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

A							:				Tuple			     =	{
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

A							:				Any			     =	{
    """openai-gpt""": 512,
}
def     snake_case_  (       a__		:     List[str]      ):



    """simple docstring"""




    __lowercase          =						text.strip()
    if not text:
        return []
    __lowercase          =						text.split()
    return tokens






class 	SCREAMING_SNAKE_CASE(    __A					):

       def __init__(   self ,		lowerCamelCase__=True ,		lowerCamelCase__=None ,		lowerCamelCase__=True ,		lowerCamelCase__=None ,		lowerCamelCase__=True ,		)   -> Optional[Any]:

           """simple docstring"""



           if never_split is None:
               __lowercase          =						[]
           __lowercase          =						do_lower_case
           __lowercase          =						set(lowerCamelCase__					)
           __lowercase          =						tokenize_chinese_chars
           __lowercase          =						strip_accents
           __lowercase          =						do_split_on_punc

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=None					)   -> Optional[Any]:

           """simple docstring"""



           __lowercase          =						self.never_split.union(set(lowerCamelCase__					)					) if never_split else self.never_split
           __lowercase          =						self._clean_text(lowerCamelCase__					)

           # This was added on November 1st, 2018 for the multilingual and Chinese
           # models. This is also applied to the English models now, but it doesn't
           # matter since the English models were not trained on any Chinese data
           # and generally don't have any Chinese data in them (there are Chinese
           # characters in the vocabulary because Wikipedia does have some Chinese
           # words in the English Wikipedia.).
           if self.tokenize_chinese_chars:
               __lowercase          =						self._tokenize_chinese_chars(lowerCamelCase__					)
           # prevents treating the same character with different unicode codepoints as different characters
           __lowercase          =						unicodedata.normalize("""NFC""" ,		lowerCamelCase__					)
           __lowercase          =						whitespace_tokenize(lowerCamelCase__					)
           __lowercase          =						[]
           for token in orig_tokens:
               if token not in never_split:
                   if self.do_lower_case:
                       __lowercase          =						token.lower()
                       if self.strip_accents is not False:
                           __lowercase          =						self._run_strip_accents(lowerCamelCase__					)
                   elif self.strip_accents:
                       __lowercase          =						self._run_strip_accents(lowerCamelCase__					)
               split_tokens.extend(self._run_split_on_punc(lowerCamelCase__ ,		lowerCamelCase__					)					)

           __lowercase          =						whitespace_tokenize(""" """.join(lowerCamelCase__					)					)
           return output_tokens

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Any:

           """simple docstring"""



           __lowercase          =						unicodedata.normalize("""NFD""" ,		lowerCamelCase__					)
           __lowercase          =						[]
           for char in text:
               __lowercase          =						unicodedata.category(lowerCamelCase__					)
               if cat == "Mn":
                   continue
               output.append(lowerCamelCase__					)
           return "".join(lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=None					)   -> Optional[int]:

           """simple docstring"""



           if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
           __lowercase          =						list(lowerCamelCase__					)
           __lowercase          =						0
           __lowercase          =						True
           __lowercase          =						[]
           while i < len(lowerCamelCase__					):
               __lowercase          =						chars[i]
               if _is_punctuation(lowerCamelCase__					):
                   output.append([char]					)
                   __lowercase          =						True
               else:
                   if start_new_word:
                       output.append([]					)
                   __lowercase          =						False
                   output[-1].append(lowerCamelCase__					)
               i += 1

           return ["".join(lowerCamelCase__					) for x in output]

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Any:

           """simple docstring"""



           __lowercase          =						[]
           for char in text:
               __lowercase          =						ord(lowerCamelCase__					)
               if self._is_chinese_char(lowerCamelCase__					):
                   output.append(""" """					)
                   output.append(lowerCamelCase__					)
                   output.append(""" """					)
               else:
                   output.append(lowerCamelCase__					)
           return "".join(lowerCamelCase__					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



           if (
               (cp >= 0X4_e00 and cp <= 0X9_fff)
               or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
               or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
               or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
               or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
               or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
               or (cp >= 0Xf_900 and cp <= 0Xf_aff)
               or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
           ):  #
               return True

           return False




       def    snake_case__      (   self ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           __lowercase          =						[]
           for char in text:
               __lowercase          =						ord(lowerCamelCase__					)
               if cp == 0 or cp == 0Xf_ffd or _is_control(lowerCamelCase__					):
                   continue
               if _is_whitespace(lowerCamelCase__					):
                   output.append(""" """					)
               else:
                   output.append(lowerCamelCase__					)
           return "".join(lowerCamelCase__					)

def     snake_case_  (       a__		:     List[Any]      ):



    """simple docstring"""




    __lowercase          =						set()
    __lowercase          =						word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)      )
        __lowercase          =						char
    return pairs
def     snake_case_  (       a__		:     Union[str, Any]      ):



    """simple docstring"""




    __lowercase          =						text.replace("""—"""   ,"""-"""      )
    __lowercase          =						text.replace("""–"""   ,"""-"""      )
    __lowercase          =						text.replace("""―"""   ,"""-"""      )
    __lowercase          =						text.replace("""…"""   ,"""..."""      )
    __lowercase          =						text.replace("""´"""   ,"""'"""      )
    __lowercase          =						re.sub(r"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""   ,r""" \1 """   ,a__      )
    __lowercase          =						re.sub(r"""\s*\n\s*"""   ,""" \n """   ,a__      )
    __lowercase          =						re.sub(r"""[^\S\n]+"""   ,""" """   ,a__      )
    return text.strip()






class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					Dict   = VOCAB_FILES_NAMES
       snake_case_			:					Tuple   = PRETRAINED_VOCAB_FILES_MAP
       snake_case_			:					List[Any]   = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case_			:					Union[str, Any]   = ["""input_ids""", """attention_mask"""]

       def __init__(   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__="<unk>" ,		**lowerCamelCase__					)   -> List[Any]:

           """simple docstring"""



           super().__init__(unk_token=lowerCamelCase__ ,		**lowerCamelCase__					)

           try:
               import ftfy
               from spacy.lang.en import English

               __lowercase          =						English()
               __lowercase          =						_nlp.tokenizer
               __lowercase          =						ftfy.fix_text
           except ImportError:
               logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""					)
               __lowercase          =						BasicTokenizer(do_lower_case=lowerCamelCase__					)
               __lowercase          =						None

           with open(lowerCamelCase__ ,		encoding="""utf-8"""					) as vocab_handle:
               __lowercase          =						json.load(lowerCamelCase__					)
           __lowercase          =						{v: k for k, v in self.encoder.items()}
           with open(lowerCamelCase__ ,		encoding="""utf-8"""					) as merges_handle:
               __lowercase          =						merges_handle.read().split("""\n"""					)[1:-1]
           __lowercase          =						[tuple(merge.split()					) for merge in merges]
           __lowercase          =						dict(zip(lowerCamelCase__ ,		range(len(lowerCamelCase__					)					)					)					)
           __lowercase          =						{}

       @property
       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           return True

       @property
       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           return len(self.encoder					)

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



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

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> int:

           """simple docstring"""



           __lowercase          =						tuple(token[:-1]					) + (token[-1] + """</w>""",)
           if token in self.cache:
               return self.cache[token]
           __lowercase          =						get_pairs(lowerCamelCase__					)

           if not pairs:
               return token + "</w>"

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

                   if word[i] == first and i < len(lowerCamelCase__					) - 1 and word[i + 1] == second:
                       new_word.append(first + second					)
                       i += 2
                   else:
                       new_word.append(word[i]					)
                       i += 1
               __lowercase          =						tuple(lowerCamelCase__					)
               __lowercase          =						new_word
               if len(lowerCamelCase__					) == 1:
                   break
               else:
                   __lowercase          =						get_pairs(lowerCamelCase__					)
           __lowercase          =						""" """.join(lowerCamelCase__					)
           if word == "\n  </w>":
               __lowercase          =						"""\n</w>"""
           __lowercase          =						word
           return word

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



           __lowercase          =						[]
           if self.fix_text is None:
               # Using BERT's BasicTokenizer
               __lowercase          =						self.nlp.tokenize(lowerCamelCase__					)
               for token in text:
                   split_tokens.extend(list(self.bpe(lowerCamelCase__					).split(""" """					)					)					)
           else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               __lowercase          =						self.nlp(text_standardize(self.fix_text(lowerCamelCase__					)					)					)
               for token in text:
                   split_tokens.extend(list(self.bpe(token.text.lower()					).split(""" """					)					)					)
           return split_tokens

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



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

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           return self.decoder.get(lowerCamelCase__ ,		self.unk_token					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Any:

           """simple docstring"""



           __lowercase          =						"""""".join(lowerCamelCase__					).replace("""</w>""" ,		""" """					).strip()
           return out_string




       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = None					)   -> Tuple[str]:

           """simple docstring"""



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

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

           __lowercase          =						0
           with open(lowerCamelCase__ ,		"""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 lowerCamelCase__					: 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!"""					)
                       __lowercase          =						token_index
                   writer.write(""" """.join(lowerCamelCase__					) + """\n"""					)
                   index += 1

           return vocab_file, merge_file




