






"""simple docstring"""




import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

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


lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowerCamelCase__			=   {
    "src_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json"
    },
    "tgt_vocab_file": {
        "stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json"
    },
    "merges_file": {"stas/tiny-wmt19-en-de": "https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt"},
}

lowerCamelCase__			=   {"stas/tiny-wmt19-en-de": 1_024}
lowerCamelCase__			=   {
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1_024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]    ):
  A__											=							set()
  A__											=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    A__											=							char
  return pairs






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple    ):
  A__											=							text.replace("""，"""		,				""","""    )
  A__											=							re.sub(R"""。\s*"""		,				""". """		,				UpperCamelCase    )
  A__											=							text.replace("""、"""		,				""","""    )
  A__											=							text.replace("""”"""		,				"""\""""    )
  A__											=							text.replace("""“"""		,				"""\""""    )
  A__											=							text.replace("""∶"""		,				""":"""    )
  A__											=							text.replace("""："""		,				""":"""    )
  A__											=							text.replace("""？"""		,				"""?"""    )
  A__											=							text.replace("""《"""		,				"""\""""    )
  A__											=							text.replace("""》"""		,				"""\""""    )
  A__											=							text.replace("""）"""		,				""")"""    )
  A__											=							text.replace("""！"""		,				"""!"""    )
  A__											=							text.replace("""（"""		,				"""("""    )
  A__											=							text.replace("""；"""		,				""";"""    )
  A__											=							text.replace("""１"""		,				"""1"""    )
  A__											=							text.replace("""」"""		,				"""\""""    )
  A__											=							text.replace("""「"""		,				"""\""""    )
  A__											=							text.replace("""０"""		,				"""0"""    )
  A__											=							text.replace("""３"""		,				"""3"""    )
  A__											=							text.replace("""２"""		,				"""2"""    )
  A__											=							text.replace("""５"""		,				"""5"""    )
  A__											=							text.replace("""６"""		,				"""6"""    )
  A__											=							text.replace("""９"""		,				"""9"""    )
  A__											=							text.replace("""７"""		,				"""7"""    )
  A__											=							text.replace("""８"""		,				"""8"""    )
  A__											=							text.replace("""４"""		,				"""4"""    )
  A__											=							re.sub(R"""．\s*"""		,				""". """		,				UpperCamelCase    )
  A__											=							text.replace("""～"""		,				"""~"""    )
  A__											=							text.replace("""’"""		,				"""'"""    )
  A__											=							text.replace("""…"""		,				"""..."""    )
  A__											=							text.replace("""━"""		,				"""-"""    )
  A__											=							text.replace("""〈"""		,				"""<"""    )
  A__											=							text.replace("""〉"""		,				""">"""    )
  A__											=							text.replace("""【"""		,				"""["""    )
  A__											=							text.replace("""】"""		,				"""]"""    )
  A__											=							text.replace("""％"""		,				"""%"""    )
  return text






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Dict    ):
  A__											=							[]
  for char in text:
    A__											=							unicodedata.category(UpperCamelCase    )
    if cat.startswith("""C"""    ):
      continue
    output.append(UpperCamelCase    )
  return "".join(UpperCamelCase    )







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask"]






    def __init__(self						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=False						,      lowerCamelCase__="<unk>"						,      lowerCamelCase__="<s>"						,      lowerCamelCase__="</s>"						,      lowerCamelCase__="<pad>"						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__(
          langs=lowerCamelCase__						,      src_vocab_file=lowerCamelCase__						,      tgt_vocab_file=lowerCamelCase__						,      merges_file=lowerCamelCase__						,      do_lower_case=lowerCamelCase__						,      unk_token=lowerCamelCase__						,      bos_token=lowerCamelCase__						,      sep_token=lowerCamelCase__						,      pad_token=lowerCamelCase__						,      **lowerCamelCase__						,      )

      try:
        import sacremoses
      except ImportError:
        raise ImportError(
            """You need to install sacremoses to use XLMTokenizer. """
            """See https://pypi.org/project/sacremoses/ for installation."""   )

      A__											=							sacremoses

      A__											=							src_vocab_file
      A__											=							tgt_vocab_file
      A__											=							merges_file
      A__											=							do_lower_case

      # cache of sm.MosesPunctNormalizer instance
      A__											=							{}
      # cache of sm.MosesTokenizer instance
      A__											=							{}
      A__											=							{}

      if langs and len(lowerCamelCase__   ) == 2:
        A__						,A__											=							langs
      else:
        raise ValueError(
            F"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
            """Usually that means that tokenizer can't find a mapping for the given model path """
            """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""   )

      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as src_vocab_handle:
        A__											=							json.load(lowerCamelCase__   )
      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as tgt_vocab_handle:
        A__											=							json.load(lowerCamelCase__   )
        A__											=							{v: k for k, v in tgt_vocab.items()}
      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as merges_handle:
        A__											=							merges_handle.read().split("""\n"""   )[:-1]
      A__											=							[tuple(merge.split()[:2]   ) for merge in merges]
      A__											=							dict(zip(lowerCamelCase__						,      range(len(lowerCamelCase__   )   )   )   )
      A__											=							{}






    def  A    (self   ):


      """simple docstring"""
      return self.get_src_vocab()






    @property
    def  A    (self   ):


      """simple docstring"""
      return self.src_vocab_size






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      if lang not in self.cache_moses_punct_normalizer:
        A__											=							self.sm.MosesPunctNormalizer(lang=lowerCamelCase__   )
        A__											=							punct_normalizer
      return self.cache_moses_punct_normalizer[lang].normalize(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      if lang not in self.cache_moses_tokenizer:
        A__											=							self.sm.MosesTokenizer(lang=lowerCamelCase__   )
        A__											=							moses_tokenizer
      return self.cache_moses_tokenizer[lang].tokenize(
          lowerCamelCase__						,      aggressive_dash_splits=lowerCamelCase__						,      return_str=lowerCamelCase__						,      escape=lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      if lang not in self.cache_moses_detokenizer:
        A__											=							self.sm.MosesDetokenizer(lang=lowerCamelCase__   )
        A__											=							moses_detokenizer
      return self.cache_moses_detokenizer[lang].detokenize(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							replace_unicode_punct(lowerCamelCase__   )
      A__											=							self.moses_punct_norm(lowerCamelCase__						,      lowerCamelCase__   )
      A__											=							remove_non_printing_char(lowerCamelCase__   )
      return text






    @property
    def  A    (self   ):


      """simple docstring"""
      return len(self.encoder   )






    @property
    def  A    (self   ):


      """simple docstring"""
      return len(self.decoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.encoder						,      **self.added_tokens_encoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.decoder						,      **self.added_tokens_decoder   )






    def  A    (self						,      lowerCamelCase__   ):


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

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

      while True:
        A__											=							min(lowerCamelCase__						,      key=lambda lowerCamelCase__   : self.bpe_ranks.get(lowerCamelCase__						,      float("""inf"""   )   )   )
        if bigram not in self.bpe_ranks:
          break
        A__						,A__											=							bigram
        A__											=							[]
        A__											=							0
        while i < len(lowerCamelCase__   ):
          try:
            A__											=							word.index(lowerCamelCase__						,      lowerCamelCase__   )
          except ValueError:
            new_word.extend(word[i:]   )
            break
          else:
            new_word.extend(word[i:j]   )
            A__											=							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
        A__											=							tuple(lowerCamelCase__   )
        A__											=							new_word
        if len(lowerCamelCase__   ) == 1:
          break
        else:
          A__											=							get_pairs(lowerCamelCase__   )
      A__											=							""" """.join(lowerCamelCase__   )
      if word == "\n  </w>":
        A__											=							"""\n</w>"""
      A__											=							word
      return word






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__="en"						,      lowerCamelCase__=False   ):


      """simple docstring"""
      A__											=							self.src_lang

      if self.do_lower_case:
        A__											=							text.lower()

      if bypass_tokenizer:
        A__											=							text.split()
      else:
        A__											=							self.moses_pipeline(lowerCamelCase__						,      lang=lowerCamelCase__   )
        A__											=							self.moses_tokenize(lowerCamelCase__						,      lang=lowerCamelCase__   )

      A__											=							[]
      for token in text:
        if token:
          split_tokens.extend(list(self.bpe(lowerCamelCase__   ).split(""" """   )   )   )

      return split_tokens






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.encoder.get(lowerCamelCase__						,      self.encoder.get(self.unk_token   )   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.decoder.get(lowerCamelCase__						,      self.unk_token   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[t.replace(""" """						,      """"""   ).replace("""</w>"""						,      """ """   ) for t in tokens]
      A__											=							"""""".join(lowerCamelCase__   ).split()
      # detokenize
      A__											=							self.moses_detokenize(lowerCamelCase__						,      self.tgt_lang   )
      return text






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      A__											=							[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
        return token_ids_a + sep
      return token_ids_a + sep + token_ids_a + sep






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False   ):


      """simple docstring"""

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCamelCase__						,      token_ids_a=lowerCamelCase__						,      already_has_special_tokens=lowerCamelCase__   )
      # no bos used in fairseq
      if token_ids_a is not None:
        return ([0] * len(lowerCamelCase__   )) + [1] + ([0] * len(lowerCamelCase__   )) + [1]
      return ([0] * len(lowerCamelCase__   )) + [1]






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      A__											=							[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
        return len(token_ids_a + sep   ) * [0]
      return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if not os.path.isdir(lowerCamelCase__   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return

      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]   )
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]   )
      A__											=							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"""   )

      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as f:
        A__											=							{v: k for k, v in self.decoder.items()}
        f.write(json.dumps(lowerCamelCase__						,      indent=2						,      sort_keys=lowerCamelCase__						,      ensure_ascii=lowerCamelCase__   ) + """\n"""   )

      A__											=							0
      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as writer:
        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 {merges_file}: BPE merge indices are not consecutive."""
                """ Please check that the tokenizer is not corrupted!"""   )
            A__											=							token_index
          writer.write(""" """.join(lowerCamelCase__   ) + """\n"""   )
          index += 1

      return src_vocab_file, tgt_vocab_file, merges_file






    def __getstate__(self   ):


      """simple docstring"""
      A__											=							self.__dict__.copy()
      A__											=							None
      return state






    def __setstate__(self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							d

      try:
        import sacremoses
      except ImportError:
        raise ImportError(
            """You need to install sacremoses to use XLMTokenizer. """
            """See https://pypi.org/project/sacremoses/ for installation."""   )

      A__											=							sacremoses












"""simple docstring"""




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    ):
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							BertGenerationDecoder(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      UpperCamelCase		,				UpperCamelCase		,				model_class="""bert"""		,				is_encoder_named_decoder=UpperCamelCase		,				is_encoder=UpperCamelCase		,				)

  # Save pytorch-model
  print(F"""Save PyTorch model and config to {pytorch_dump_path}"""    )
  model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )




