





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-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_lowerCamelCase   				=			{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 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']




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					VOCAB_FILES_NAMES
							lowerCamelCase_           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase_           =					PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_           =					["input_ids", "attention_mask"]

							lowerCamelCase_           =					[]
							lowerCamelCase_           =					[]


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      AddedToken(__A							,				lstrip=__A							,				rstrip=__A		) if isinstance(__A							,				__A		) else mask_token

									SCREAMING_SNAKE_CASE__			=      {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=__A							,				eos_token=__A							,				unk_token=__A							,				sep_token=__A							,				cls_token=__A							,				pad_token=__A							,				mask_token=__A							,				tokenizer_file=__A							,				src_lang=__A							,				tgt_lang=__A							,				additional_special_tokens=__A							,				sp_model_kwargs=self.sp_model_kwargs							,				**__A							,				)

									SCREAMING_SNAKE_CASE__			=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(str(__A		)		)
									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(__A		)
									}
									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__			=      list(self.lang_code_to_id.keys()		)

									if additional_special_tokens is not None:
											# Only add those special tokens if they are not already there.
											self._additional_special_tokens.extend(
											    [t for t in additional_special_tokens if t not in self._additional_special_tokens]		)

									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		)


							def __getstate__(							self    :Optional[Any]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.__dict__.copy()
									SCREAMING_SNAKE_CASE__			=      None
									SCREAMING_SNAKE_CASE__			=      self.sp_model.serialized_model_proto()
									return state


							def __setstate__(							self    :Tuple							,				__A    :List[str]		)   ->   Any:



									"""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.LoadFromSerializedProto(self.sp_model_proto		)


							@property
							def      _snake_case  (							self    :Optional[Any]		)   ->   List[str]:



									"""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      _snake_case  (							self    :Optional[Any]		)   ->   str:



									"""simple docstring"""



									return self._src_lang


							@src_lang.setter
							def      _snake_case  (							self    :Optional[int]							,				__A    :str		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      new_src_lang
									self.set_src_lang_special_tokens(self._src_lang		)


							def      _snake_case  (							self    :Dict							,				__A    :List[int]							,				__A    :Optional[List[int]] = None							,				__A    :bool = False		)   ->   List[int]:



									"""simple docstring"""



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

									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(__A		)) + suffix_ones
									return prefix_ones + ([0] * len(__A		)) + ([0] * len(__A		)) + suffix_ones


							def      _snake_case  (							self    :Any							,				__A    :List[int]							,				__A    :Optional[List[int]] = None		)   ->   List[int]:



									"""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      _snake_case  (							self    :List[Any]							,				__A    :List[int]							,				__A    :Optional[List[int]] = None		)   ->   List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      [self.sep_token_id]
									SCREAMING_SNAKE_CASE__			=      [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    :List[Any]							,				__A    :str							,				__A    :str							,				__A    :Optional[str]							,				__A    :Optional[str]							,				**__A    :List[str]		)   ->   Union[str, Any]:



									"""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(__A							,				add_special_tokens=__A							,				return_tensors=__A							,				**__A		)
									SCREAMING_SNAKE_CASE__			=      self.convert_tokens_to_ids(__A		)
									SCREAMING_SNAKE_CASE__			=      tgt_lang_id
									return inputs


							def      _snake_case  (							self    :List[str]		)   ->   List[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :List[str]							,				__A    :str		)   ->   List[str]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str							,				__A    :Tuple		)   ->   Optional[int]:



									"""simple docstring"""



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

									# 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      _snake_case  (							self    :Optional[int]							,				__A    :Dict		)   ->   Dict:



									"""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      _snake_case  (							self    :Optional[Any]							,				__A    :Dict		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """""".join(__A		).replace(__A							,				""" """		).strip()
									return out_string


							def      _snake_case  (							self    :int							,				__A    :str							,				__A    :Optional[str] = None		)   ->   Tuple[str]:



									"""simple docstring"""



									if not os.path.isdir(__A		):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
											return
									SCREAMING_SNAKE_CASE__			=      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		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file							,				__A		)
									elif not os.path.isfile(self.vocab_file		):
											with open(__A							,				"""wb"""		) as fi:
													SCREAMING_SNAKE_CASE__			=      self.sp_model.serialized_model_proto()
													fi.write(__A		)

									return (out_vocab_file,)


							def      _snake_case  (							self    :Optional[int]							,				__A    :List[str]							,				__A    :str = "en_XX"							,				__A    :Optional[List[str]] = None							,				__A    :str = "ro_RO"							,				**__A    :Optional[int]							,				)   ->   BatchEncoding:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      src_lang
									SCREAMING_SNAKE_CASE__			=      tgt_lang
									return super().prepare_seqaseq_batch(__A							,				__A							,				**__A		)


							def      _snake_case  (							self    :Dict		)   ->   Dict:



									"""simple docstring"""



									return self.set_src_lang_special_tokens(self.src_lang		)


							def      _snake_case  (							self    :List[Any]		)   ->   List[str]:



									"""simple docstring"""



									return self.set_tgt_lang_special_tokens(self.tgt_lang		)


							def      _snake_case  (							self    :str							,				__A    :Union[str, Any]		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.lang_code_to_id[src_lang]
									SCREAMING_SNAKE_CASE__			=      []
									SCREAMING_SNAKE_CASE__			=      [self.eos_token_id, self.cur_lang_code]


							def      _snake_case  (							self    :int							,				__A    :str		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.lang_code_to_id[lang]
									SCREAMING_SNAKE_CASE__			=      []
									SCREAMING_SNAKE_CASE__			=      [self.eos_token_id, self.cur_lang_code]






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							str    ):
		def get_masked_lm_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_array(UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		def get_encoder_attention_layer_array(UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Any    ):
				SCREAMING_SNAKE_CASE__			=      f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
				SCREAMING_SNAKE_CASE__			=      tf.train.load_variable(UpperCamelCase__					,   UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      array.reshape(UpperCamelCase__    )

				if "kernel" in name:
						SCREAMING_SNAKE_CASE__			=      array.transpose()

				return torch.from_numpy(UpperCamelCase__    )

		print(f'''Loading model based on config from {config_path}...'''    )
		SCREAMING_SNAKE_CASE__			=      BertConfig.from_json_file(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM(UpperCamelCase__    )

		# Layers
		for layer_index in range(0					,   config.num_hidden_layers    ):
				SCREAMING_SNAKE_CASE__			=      model.bert.encoder.layer[layer_index]

				# Self-attention
				SCREAMING_SNAKE_CASE__			=      layer.attention.self

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/kernel"""					,   self_attn.query.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_query_dense/bias"""					,   self_attn.query.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/kernel"""					,   self_attn.key.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_key_dense/bias"""					,   self_attn.key.bias.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/kernel"""					,   self_attn.value.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_value_dense/bias"""					,   self_attn.value.bias.data.shape    )

				# Self-attention Output
				SCREAMING_SNAKE_CASE__			=      layer.attention.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/kernel"""					,   self_output.dense.weight.data.shape    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_attention_layer_array(
				    UpperCamelCase__					,   """_output_dense/bias"""					,   self_output.dense.bias.data.shape    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_attention_layer_norm/beta"""    )

				# Intermediate
				SCREAMING_SNAKE_CASE__			=      layer.intermediate

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_intermediate_dense/bias"""    )

				# Output
				SCREAMING_SNAKE_CASE__			=      layer.output

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/kernel"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_dense/bias"""    )

				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/gamma"""    )
				SCREAMING_SNAKE_CASE__			=      get_encoder_layer_array(UpperCamelCase__					,   """_output_layer_norm/beta"""    )

		# Embeddings
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_position_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_type_embedding_layer/embeddings"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_embedding_norm_layer/beta"""    )

		# LM Head
		SCREAMING_SNAKE_CASE__			=      model.cls.predictions.transform

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""dense/bias"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/gamma"""    )
		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""layer_norm/beta"""    )

		SCREAMING_SNAKE_CASE__			=      get_masked_lm_array("""embedding_table"""    )

		# Pooling
		SCREAMING_SNAKE_CASE__			=      BertPooler(config=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/kernel"""    )
		SCREAMING_SNAKE_CASE__			=      get_encoder_array("""_pooler_layer/bias"""    )

		# Export final model
		model.save_pretrained(UpperCamelCase__    )

		# Integration test - should load without any errors ;)
		SCREAMING_SNAKE_CASE__			=      BertForMaskedLM.from_pretrained(UpperCamelCase__    )
		print(new_model.eval()    )

		print("""Model conversion was done sucessfully!"""    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()
	parser.add_argument(
	    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
	)
	parser.add_argument(
	    '--bert_config_file',
	    type=str,
	    required=True,
	    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path',
	    type=str,
	    required=True,
	    help='Path to the output PyTorch model.',
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)