




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
 a_ 		=  None


a_ 		=  logging.get_logger(__name__)

a_ 		=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_ 		=  {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

a_ 		=  {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       VOCAB_FILES_NAMES
      lowerCAmelCase__      :			List[Any]     =       PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__      :			Optional[Any]     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__      :			Optional[int]     =       ["input_ids", "attention_mask"]
      lowerCAmelCase__      :			Union[str, Any]     =       XLMRobertaTokenizer







      def __init__(  self       :			str			,							snake_case       :			Union[str, Any]=None			,							snake_case       :			Any=None			,							snake_case       :			Union[str, Any]="<s>"			,							snake_case       :			Tuple="</s>"			,							snake_case       :			List[Any]="</s>"			,							snake_case       :			Any="<s>"			,							snake_case       :			str="<unk>"			,							snake_case       :			int="<pad>"			,							snake_case       :			Any="<mask>"			,							**snake_case       :			str			,							):
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase			=    AddedToken(snake_case			,							lstrip=snake_case			,							rstrip=snake_case					) if isinstance(snake_case			,							snake_case					) else mask_token

          super().__init__(
              snake_case			,							tokenizer_file=snake_case			,							bos_token=snake_case			,							eos_token=snake_case			,							sep_token=snake_case			,							cls_token=snake_case			,							unk_token=snake_case			,							pad_token=snake_case			,							mask_token=snake_case			,							**snake_case			,							)

          __UpperCamelCase			=    vocab_file
          __UpperCamelCase			=    False if not self.vocab_file else True







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCamelCase			=    [self.cls_token_id]
          __UpperCamelCase			=    [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep







      def    snake_case						(  self       :			Any			,							snake_case       :			List[int]			,							snake_case       :			Optional[List[int]] = None					):
          __UpperCamelCase			=    [self.sep_token_id]
          __UpperCamelCase			=    [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    snake_case						(  self       :			Dict			,							snake_case       :			str			,							snake_case       :			Optional[str] = None					):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''					)

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

          if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case					):
              copyfile(self.vocab_file			,							snake_case					)

          return (out_vocab_file,)












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






