



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						'''▁'''

_lowercase     							=						{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase     							=						{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

_lowercase     							=						{
    '''facebook/m2m100_418M''': 10_24,
}

# fmt: off
_lowercase     							=						{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			VOCAB_FILES_NAMES
			UpperCamelCase					:Dict     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase					:Union[str, Any]     =			PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase					:Optional[int]     =			['''input_ids''', '''attention_mask''']

			UpperCamelCase					:List[int]     =			[]
			UpperCamelCase					:List[int]     =			[]





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__=None							,    __magic_name__=None							,    __magic_name__="<s>"							,    __magic_name__="</s>"							,    __magic_name__="</s>"							,    __magic_name__="<pad>"							,    __magic_name__="<unk>"							,    __magic_name__="m2m100"							,    __magic_name__ = None							,    __magic_name__=8							,    **__magic_name__							,    ):
						lowerCamelCase__ :      str								=		{} if sp_model_kwargs is None else sp_model_kwargs

						lowerCamelCase__ :      Any								=		language_codes
						lowerCamelCase__ :      int								=		FAIRSEQ_LANGUAGE_CODES[language_codes]
						lowerCamelCase__ :      Optional[Any]								=		{lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

						lowerCamelCase__ :      Union[str, Any]								=		kwargs.get("""additional_special_tokens"""							,    []				)
						kwargs["additional_special_tokens"] += [
						    self.get_lang_token(__magic_name__				)
						    for lang_code in fairseq_language_code
						    if self.get_lang_token(__magic_name__				) not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    src_lang=__magic_name__							,    tgt_lang=__magic_name__							,    bos_token=__magic_name__							,    eos_token=__magic_name__							,    sep_token=__magic_name__							,    unk_token=__magic_name__							,    pad_token=__magic_name__							,    language_codes=__magic_name__							,    sp_model_kwargs=self.sp_model_kwargs							,    num_madeup_words=__magic_name__							,    **__magic_name__							,    )

						lowerCamelCase__ :      Tuple								=		vocab_file
						lowerCamelCase__ :      List[Any]								=		load_json(__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		{v: k for k, v in self.encoder.items()}
						lowerCamelCase__ :      Union[str, Any]								=		spm_file
						lowerCamelCase__ :      List[str]								=		load_spm(__magic_name__							,    self.sp_model_kwargs				)

						lowerCamelCase__ :      Tuple								=		len(self.encoder				)

						lowerCamelCase__ :      List[str]								=		{
						    self.get_lang_token(__magic_name__				): self.encoder_size + i for i, lang_code in enumerate(__magic_name__				)
						}
						lowerCamelCase__ :      List[Any]								=		{lang_code: self.encoder_size + i for i, lang_code in enumerate(__magic_name__				)}
						lowerCamelCase__ :      List[Any]								=		{v: k for k, v in self.lang_token_to_id.items()}

						lowerCamelCase__ :      int								=		src_lang if src_lang is not None else """en"""
						lowerCamelCase__ :      int								=		tgt_lang
						lowerCamelCase__ :      str								=		self.get_lang_id(self._src_lang				)
						self.set_src_lang_special_tokens(self._src_lang				)

						lowerCamelCase__ :      List[str]								=		num_madeup_words





			@property
			def        _snake_case    (self				):
						return len(self.encoder				) + len(self.lang_token_to_id				)





			@property
			def        _snake_case    (self				):
						return self._src_lang





			@src_lang.setter
			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      int								=		new_src_lang
						self.set_src_lang_special_tokens(self._src_lang				)





			def        _snake_case    (self							,    __magic_name__				):
						return self.sp_model.encode(__magic_name__							,    out_type=__magic_name__				)





			def        _snake_case    (self							,    __magic_name__				):
						if token in self.lang_token_to_id:
									return self.lang_token_to_id[token]
						return self.encoder.get(__magic_name__							,    self.encoder[self.unk_token]				)





			def        _snake_case    (self							,    __magic_name__				):
						if index in self.id_to_lang_token:
									return self.id_to_lang_token[index]
						return self.decoder.get(__magic_name__							,    self.unk_token				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		[]
						lowerCamelCase__ :      List[str]								=		""""""
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												out_string += self.sp_model.decode(__magic_name__				) + token
												lowerCamelCase__ :      Union[str, Any]								=		[]
									else:
												current_sub_tokens.append(__magic_name__				)
						out_string += self.sp_model.decode(__magic_name__				)
						return out_string.strip()





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = False				):

						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__magic_name__							,    token_ids_a=__magic_name__							,    already_has_special_tokens=__magic_name__				)

						lowerCamelCase__ :      int								=		[1] * len(self.prefix_tokens				)
						lowerCamelCase__ :      Dict								=		[1] * len(self.suffix_tokens				)
						if token_ids_a is None:
									return prefix_ones + ([0] * len(__magic_name__				)) + suffix_ones
						return prefix_ones + ([0] * len(__magic_name__				)) + ([0] * len(__magic_name__				)) + suffix_ones





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None				):
						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        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		{self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
						vocab.update(self.added_tokens_encoder				)
						return vocab





			def __getstate__(self				):
						lowerCamelCase__ :      Any								=		self.__dict__.copy()
						lowerCamelCase__ :      Any								=		None
						return state





			def __setstate__(self							,    __magic_name__				):
						lowerCamelCase__ :      Union[str, Any]								=		d

						# for backward compatibility
						if not hasattr(self							,    """sp_model_kwargs"""				):
									lowerCamelCase__ :      int								=		{}

						lowerCamelCase__ :      List[str]								=		load_spm(self.spm_file							,    self.sp_model_kwargs				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None				):
						lowerCamelCase__ :      Union[str, Any]								=		Path(__magic_name__				)
						if not save_dir.is_dir():
									raise OSError(f"{save_directory} should be a directory"				)
						lowerCamelCase__ :      Optional[Any]								=		save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
						)
						lowerCamelCase__ :      Dict								=		save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
						)

						save_json(self.encoder							,    __magic_name__				)

						if os.path.abspath(self.spm_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.spm_file				):
									copyfile(self.spm_file							,    __magic_name__				)
						elif not os.path.isfile(self.spm_file				):
									with open(__magic_name__							,    """wb"""				) as fi:
												lowerCamelCase__ :      List[Any]								=		self.sp_model.serialized_model_proto()
												fi.write(__magic_name__				)

						return (str(__magic_name__				), str(__magic_name__				))





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = "en"							,    __magic_name__ = None							,    __magic_name__ = "ro"							,    **__magic_name__							,    ):
						lowerCamelCase__ :      Any								=		src_lang
						lowerCamelCase__ :      List[str]								=		tgt_lang
						self.set_src_lang_special_tokens(self.src_lang				)
						return super().prepare_seqaseq_batch(__magic_name__							,    __magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    **__magic_name__				):
						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
						lowerCamelCase__ :      List[str]								=		src_lang
						lowerCamelCase__ :      Optional[Any]								=		self(__magic_name__							,    add_special_tokens=__magic_name__							,    **__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		self.get_lang_id(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		tgt_lang_id
						return inputs





			def        _snake_case    (self				):
						self.set_src_lang_special_tokens(self.src_lang				)





			def        _snake_case    (self				):
						self.set_tgt_lang_special_tokens(self.tgt_lang				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      int								=		self.get_lang_token(__magic_name__				)
						lowerCamelCase__ :      str								=		self.lang_token_to_id[lang_token]
						lowerCamelCase__ :      List[Any]								=		[self.cur_lang_id]
						lowerCamelCase__ :      Tuple								=		[self.eos_token_id]





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		self.get_lang_token(__magic_name__				)
						lowerCamelCase__ :      List[Any]								=		self.lang_token_to_id[lang_token]
						lowerCamelCase__ :      Tuple								=		[self.cur_lang_id]
						lowerCamelCase__ :      List[str]								=		[self.eos_token_id]





			def        _snake_case    (self							,    __magic_name__				):
						return self.lang_code_to_token[lang]





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Optional[int]								=		self.get_lang_token(__magic_name__				)
						return self.lang_token_to_id[lang_token]







def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Dict[str, Any] )  ->sentencepiece.SentencePieceProcessor:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		sentencepiece.SentencePieceProcessor(**UpperCamelCase )
			spm.Load(str(UpperCamelCase ) )
			return spm






def      _A				(UpperCamelCase		:      str )  ->Union[Dict, List]:



			'''simple docstring'''







			with open(UpperCamelCase      ,			"""r""" ) as f:
						return json.load(UpperCamelCase )






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			with open(UpperCamelCase      ,			"""w""" ) as f:
						json.dump(UpperCamelCase      ,			UpperCamelCase      ,			indent=2 )








import argparse

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


logging.set_verbosity_info()






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any]      ,			UpperCamelCase		:      Optional[Any] )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		TaConfig.from_json_file(UpperCamelCase )
			print(f"Building PyTorch model from configuration: {config}" )
			lowerCamelCase__ :      Optional[int]								=		TaForConditionalGeneration(UpperCamelCase )

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

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


if __name__ == "__main__":
				_lowercase     							=						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.'''
				)
				_lowercase     							=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



