

"""simple docstring"""




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

import sentencepiece as spm

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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				'▁'

lowerCAmelCase__							=				{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase__							=				{
    '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'
        ),
    }
}

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

# fmt: off
lowerCAmelCase__							=				['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  snake_case     (							UpperCamelCase_   ):
							UpperCAmelCase__						     =      VOCAB_FILES_NAMES
							UpperCAmelCase__						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__						     =      PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__						     =      ['''input_ids''', '''attention_mask''']

							UpperCAmelCase__						     =      []
							UpperCAmelCase__						     =      []

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = AddedToken(SCREAMING_SNAKE_CASE_       ,       lstrip=SCREAMING_SNAKE_CASE_       ,       rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else mask_token

									SCREAMING_SNAKE_CASE_						 = {} if sp_model_kwargs is None else sp_model_kwargs

									SCREAMING_SNAKE_CASE_						 = 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__(
									    src_lang=SCREAMING_SNAKE_CASE_       ,       tgt_lang=SCREAMING_SNAKE_CASE_       ,       eos_token=SCREAMING_SNAKE_CASE_       ,       unk_token=SCREAMING_SNAKE_CASE_       ,       sep_token=SCREAMING_SNAKE_CASE_       ,       cls_token=SCREAMING_SNAKE_CASE_       ,       pad_token=SCREAMING_SNAKE_CASE_       ,       mask_token=SCREAMING_SNAKE_CASE_       ,       sp_model_kwargs=self.sp_model_kwargs       ,       **SCREAMING_SNAKE_CASE_       ,       )

									SCREAMING_SNAKE_CASE_						 = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									SCREAMING_SNAKE_CASE_						 = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									SCREAMING_SNAKE_CASE_						 = 1

									SCREAMING_SNAKE_CASE_						 = len(self.sp_model    )
									SCREAMING_SNAKE_CASE_						 = {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE_    )
									}
									SCREAMING_SNAKE_CASE_						 = {v: k for k, v in self.lang_code_to_id.items()}
									SCREAMING_SNAKE_CASE_						 = len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
									SCREAMING_SNAKE_CASE_						 = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

									SCREAMING_SNAKE_CASE_						 = src_lang if src_lang is not None else '''en_XX'''
									SCREAMING_SNAKE_CASE_						 = self.lang_code_to_id[self._src_lang]
									SCREAMING_SNAKE_CASE_						 = tgt_lang
									self.set_src_lang_special_tokens(self._src_lang    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self._src_lang

							@src_lang.setter
							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = new_src_lang
									self.set_src_lang_special_tokens(self._src_lang    )

							def __getstate__(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.__dict__.copy()
									SCREAMING_SNAKE_CASE_						 = None
									return state

							def __setstate__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = d

									# for backward compatibility
									if not hasattr(self       ,       '''sp_model_kwargs'''    ):
											SCREAMING_SNAKE_CASE_						 = {}

									SCREAMING_SNAKE_CASE_						 = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.Load(self.vocab_file    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    ): i for i in range(self.vocab_size    )}
									vocab.update(self.added_tokens_encoder    )
									return vocab

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.sp_model.encode(SCREAMING_SNAKE_CASE_       ,       out_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									SCREAMING_SNAKE_CASE_						 = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_    )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = ''''''
									SCREAMING_SNAKE_CASE_						 = False
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													if not prev_is_special:
															out_string += " "
													out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_    ) + token
													SCREAMING_SNAKE_CASE_						 = True
													SCREAMING_SNAKE_CASE_						 = []
											else:
													current_sub_tokens.append(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = False
									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_    )
									return out_string.strip()

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None    ):

									"""simple docstring"""







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

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE_    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file       ,       SCREAMING_SNAKE_CASE_    )
									elif not os.path.isfile(self.vocab_file    ):
											with open(SCREAMING_SNAKE_CASE_       ,       '''wb'''    ) as fi:
													SCREAMING_SNAKE_CASE_						 = self.sp_model.serialized_model_proto()
													fi.write(SCREAMING_SNAKE_CASE_    )

									return (out_vocab_file,)

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = False    ):

									"""simple docstring"""







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

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None    ):

									"""simple docstring"""







									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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									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_						 = src_lang
									SCREAMING_SNAKE_CASE_						 = self(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       return_tensors=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = tgt_lang_id
									return inputs

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = "en_XX"       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "ro_RO"       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = src_lang
									SCREAMING_SNAKE_CASE_						 = tgt_lang
									return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return self.set_src_lang_special_tokens(self.src_lang    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return self.set_tgt_lang_special_tokens(self.tgt_lang    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.lang_code_to_id[src_lang]
									SCREAMING_SNAKE_CASE_						 = [self.cur_lang_code_id]
									SCREAMING_SNAKE_CASE_						 = [self.eos_token_id]







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.lang_code_to_id[tgt_lang]
									SCREAMING_SNAKE_CASE_						 = [self.cur_lang_code_id]
									SCREAMING_SNAKE_CASE_						 = [self.eos_token_id]

"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_						 = BigBirdConfig.from_json_file(__a    )
		print(F'Building PyTorch model from configuration: {config}'    )

		if is_trivia_qa:
				SCREAMING_SNAKE_CASE_						 = BigBirdForQuestionAnswering(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = BigBirdForPreTraining(__a    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__a,							__a,							is_trivia_qa=__a    )

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


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(
						    '--big_bird_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT 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.'
						)
						parser.add_argument(
						    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
						)
						lowerCAmelCase__							=				parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
						)