





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

from tokenizers import processors

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


if is_sentencepiece_available():
       from .tokenization_mbartaa import MBartaaTokenizer
else:
       UpperCamelCase__          =None


UpperCamelCase__          =logging.get_logger(__name__)

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

UpperCamelCase__          ={
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__          ={
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
UpperCamelCase__          =['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			VOCAB_FILES_NAMES
       __snake_case       	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case       	=			PRETRAINED_VOCAB_FILES_MAP
       __snake_case       	=			['input_ids', 'attention_mask']
       __snake_case       	=			MBartaaTokenizer

       __snake_case       	=			[]
       __snake_case       	=			[]




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

            _SCREAMING_SNAKE_CASE				:		Tuple  			=							kwargs.get("additional_special_tokens"			,  []							)
            kwargs["additional_special_tokens"] += [
                code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
            ]

            super().__init__(
                __lowerCamelCase			,  src_lang=__lowerCamelCase			,  tgt_lang=__lowerCamelCase			,  tokenizer_file=__lowerCamelCase			,  eos_token=__lowerCamelCase			,  sep_token=__lowerCamelCase			,  cls_token=__lowerCamelCase			,  unk_token=__lowerCamelCase			,  pad_token=__lowerCamelCase			,  mask_token=__lowerCamelCase			,  **__lowerCamelCase			,  )

            _SCREAMING_SNAKE_CASE				:		str  			=							vocab_file
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							False if not self.vocab_file else True

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							{
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase							) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _SCREAMING_SNAKE_CASE				:		int  			=							src_lang if src_lang is not None else "en_XX"
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							tgt_lang
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.lang_code_to_id[self._src_lang]
            self.set_src_lang_special_tokens(self._src_lang							)




       @property
       def 			UpperCamelCase_	(					self							)	->   str:
            return self._src_lang




       @src_lang.setter
       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   None:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							new_src_lang
            self.set_src_lang_special_tokens(self._src_lang							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   List[int]:
            if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = "en_XX"			,  __lowerCamelCase = None			,  __lowerCamelCase = "ro_RO"			,  **__lowerCamelCase			,  )	->   BatchEncoding:
            _SCREAMING_SNAKE_CASE				:		str  			=							src_lang
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase			,  __lowerCamelCase			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            return self.set_src_lang_special_tokens(self.src_lang							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   None:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.convert_tokens_to_ids(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[self.cur_lang_code_id]
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[self.eos_token_id]

            _SCREAMING_SNAKE_CASE				:		Any  			=							self.convert_ids_to_tokens(self.prefix_tokens							)
            _SCREAMING_SNAKE_CASE				:		int  			=							self.convert_ids_to_tokens(self.suffix_tokens							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str			,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str			,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,  self.prefix_tokens + self.suffix_tokens							)							)			,  )




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   None:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.convert_tokens_to_ids(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							[self.cur_lang_code_id]
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[self.eos_token_id]

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.convert_ids_to_tokens(self.prefix_tokens							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.convert_ids_to_tokens(self.suffix_tokens							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str			,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str			,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,  self.prefix_tokens + self.suffix_tokens							)							)			,  )




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  **__lowerCamelCase							)	->   Optional[int]:
            if src_lang is None or tgt_lang is None:
                 raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"							)
            _SCREAMING_SNAKE_CASE				:		int  			=							src_lang
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self(__lowerCamelCase			,  add_special_tokens=__lowerCamelCase			,  return_tensors=__lowerCamelCase			,  **__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							self.convert_tokens_to_ids(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tgt_lang_id
            return inputs







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase = None							)	->   Tuple[str]:
            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(__lowerCamelCase							):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
                 return
            _SCREAMING_SNAKE_CASE				:		Dict  			=							os.path.join(
                __lowerCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

            return (out_vocab_file,)






"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

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


if __name__ == "__main__":
    _lowerCamelCase       =	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 T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

