


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:
    __snake_case             =	None


__snake_case             =	logging.get_logger(__name__)

__snake_case             =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case             =	{
    '''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'''
        ),
    },
}

__snake_case             =	{
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}



class   __lowerCamelCase (_a	):
       _lowercase       							=    VOCAB_FILES_NAMES
       _lowercase       							=    PRETRAINED_VOCAB_FILES_MAP
       _lowercase       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase       							=    ["""input_ids""", """attention_mask"""]
       _lowercase       							=    XLMRobertaTokenizer







       def __init__( self:   int,A_:   List[Any]=None,A_:   List[Any]=None,A_:   Any="<s>",A_:   Union[str, Any]="</s>",A_:   Any="</s>",A_:   str="<s>",A_:   Optional[Any]="<unk>",A_:   str="<pad>",A_:   Tuple="<mask>",**A_:   str,):






            '''simple docstring'''




            __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else mask_token

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

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







       def 		snake_case_  ( self:   int,A_:   List[int],A_:   Optional[List[int]] = None				):






            '''simple docstring'''




            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:   int,A_:   List[int],A_:   Optional[List[int]] = None				):






            '''simple docstring'''




            __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,A_:   str,A_:   Optional[str] = None				):






            '''simple docstring'''




            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(A_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
                 return
            __UpperCamelCase   = os.path.join(
                A_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

            return (out_vocab_file,)






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
     __UpperCamelCase   = GPTNeoConfig(
         hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
     print(f'''Building PyTorch model from configuration: {config}'''   )
     __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )

     # Save pytorch-model
     print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
     model.save_pretrained(_lowercase   )


if __name__ == "__main__":
    __snake_case             =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case             =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


