


'''simple docstring'''



import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

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


if version.parse(nlp.__version__) != version.parse('0.8.3'):
		raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
		raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__A		=logging.get_logger(__name__)

__A		='The Nymphenburg Palace is a beautiful palace in Munich!'







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       {
			    """attention_cell""": """multi_head""",
			    """num_layers""": 4,
			    """units""": 1_0_2_4,
			    """hidden_size""": 7_6_8,
			    """max_length""": 5_1_2,
			    """num_heads""": 8,
			    """scaled""": True,
			    """dropout""": 0.1,
			    """use_residual""": True,
			    """embed_size""": 1_0_2_4,
			    """embed_dropout""": 0.1,
			    """word_embed""": None,
			    """layer_norm_eps""": 1e-5,
			    """token_type_vocab_size""": 2,
			}

			UpperCAmelCase__					:   Any       				=       bort_4_8_768_1024_hparams

			# Let's construct the original Bort model here
			# Taken from official BERT implementation, see:
			# https://github.com/alexa/bort/blob/master/bort/bort.py
			UpperCAmelCase__					:   Optional[Any]       				=       BERTEncoder(
			    attention_cell=predefined_args["""attention_cell"""]	, num_layers=predefined_args["""num_layers"""]	, units=predefined_args["""units"""]	, hidden_size=predefined_args["""hidden_size"""]	, max_length=predefined_args["""max_length"""]	, num_heads=predefined_args["""num_heads"""]	, scaled=predefined_args["""scaled"""]	, dropout=predefined_args["""dropout"""]	, output_attention=UpperCamelCase__	, output_all_encodings=UpperCamelCase__	, use_residual=predefined_args["""use_residual"""]	, activation=predefined_args.get("""activation"""	, """gelu"""   )	, layer_norm_eps=predefined_args.get("""layer_norm_eps"""	, UpperCamelCase__   )	, )

			# Vocab information needs to be fetched first
			# It's the same as RoBERTa, so RobertaTokenizer can be used later
			UpperCAmelCase__					:   Union[str, Any]       				=       """openwebtext_ccnews_stories_books_cased"""

			# Specify download folder to Gluonnlp's vocab
			UpperCAmelCase__					:   Dict       				=       os.path.join(get_home_dir()	, """models"""   )
			UpperCAmelCase__					:   List[str]       				=       _load_vocab(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, cls=UpperCamelCase__   )

			UpperCAmelCase__					:   int       				=       nlp.model.BERTModel(
			    UpperCamelCase__	, len(UpperCamelCase__   )	, units=predefined_args["""units"""]	, embed_size=predefined_args["""embed_size"""]	, embed_dropout=predefined_args["""embed_dropout"""]	, word_embed=predefined_args["""word_embed"""]	, use_pooler=UpperCamelCase__	, use_token_type_embed=UpperCamelCase__	, token_type_vocab_size=predefined_args["""token_type_vocab_size"""]	, use_classifier=UpperCamelCase__	, use_decoder=UpperCamelCase__	, )

			original_bort.load_parameters(UpperCamelCase__	, cast_dtype=UpperCamelCase__	, ignore_extra=UpperCamelCase__   )
			UpperCAmelCase__					:   Tuple       				=       original_bort._collect_params_with_prefix()

			# Build our config 🤗
			UpperCAmelCase__					:   Optional[int]       				=       {
			    """architectures""": ["""BertForMaskedLM"""],
			    """attention_probs_dropout_prob""": predefined_args["""dropout"""],
			    """hidden_act""": """gelu""",
			    """hidden_dropout_prob""": predefined_args["""dropout"""],
			    """hidden_size""": predefined_args["""embed_size"""],
			    """initializer_range""": 0.02,
			    """intermediate_size""": predefined_args["""hidden_size"""],
			    """layer_norm_eps""": predefined_args["""layer_norm_eps"""],
			    """max_position_embeddings""": predefined_args["""max_length"""],
			    """model_type""": """bort""",
			    """num_attention_heads""": predefined_args["""num_heads"""],
			    """num_hidden_layers""": predefined_args["""num_layers"""],
			    """pad_token_id""": 1,  # 2 = BERT, 1 = RoBERTa
			    """type_vocab_size""": 1,  # 2 = BERT, 1 = RoBERTa
			    """vocab_size""": len(UpperCamelCase__   ),
			}

			UpperCAmelCase__					:   List[Any]       				=       BertConfig.from_dict(UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       BertForMaskedLM(UpperCamelCase__   )
			hf_bort_model.eval()

			# Parameter mapping table (Gluonnlp to Transformers)
			# * denotes layer index
			#
			# | Gluon Parameter                                                | Transformers Parameter
			# | -------------------------------------------------------------- | ----------------------
			# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
			# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
			# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
			# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
			# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
			# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
			# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

			# Helper function to convert MXNET Arrays to PyTorch
			def to_torch(UpperCamelCase__   ) -> nn.Parameter:
						return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()   )   )

			# Check param shapes and map new HF param back
			def check_and_map_params(UpperCamelCase__	, UpperCamelCase__   ):
						UpperCAmelCase__					:   str       				=       hf_param.shape

						UpperCAmelCase__					:   Any       				=       to_torch(params[gluon_param]   )
						UpperCAmelCase__					:   Optional[Any]       				=       gluon_param.shape

						assert (
						    shape_hf == shape_gluon
						), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

						return gluon_param

			UpperCAmelCase__					:   List[Any]       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight	, """word_embed.0.weight"""   )
			UpperCAmelCase__					:   Dict       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight	, """encoder.position_weight"""   )
			UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias	, """encoder.layer_norm.beta"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.weight	, """encoder.layer_norm.gamma"""   )

			# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
			UpperCAmelCase__					:   Tuple       				=       torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

			for i in range(hf_bort_config.num_hidden_layers   ):
						UpperCAmelCase__					:   BertLayer       				=       hf_bort_model.bert.encoder.layer[i]

						# self attention
						UpperCAmelCase__					:   BertSelfAttention       				=       layer.attention.self

						UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
						    self_attn.key.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''   )

						UpperCAmelCase__					:   Tuple       				=       check_and_map_params(
						    self_attn.key.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    self_attn.query.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''   )
						UpperCAmelCase__					:   str       				=       check_and_map_params(
						    self_attn.query.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''   )
						UpperCAmelCase__					:   List[Any]       				=       check_and_map_params(
						    self_attn.value.bias.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    self_attn.value.weight.data	, f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''   )

						# self attention output
						UpperCAmelCase__					:   BertSelfOutput       				=       layer.attention.output

						UpperCAmelCase__					:   Tuple       				=       check_and_map_params(
						    self_output.dense.bias	, f'''encoder.transformer_cells.{i}.proj.bias'''   )
						UpperCAmelCase__					:   List[str]       				=       check_and_map_params(
						    self_output.dense.weight	, f'''encoder.transformer_cells.{i}.proj.weight'''   )
						UpperCAmelCase__					:   Optional[Any]       				=       check_and_map_params(
						    self_output.LayerNorm.bias	, f'''encoder.transformer_cells.{i}.layer_norm.beta'''   )
						UpperCAmelCase__					:   Union[str, Any]       				=       check_and_map_params(
						    self_output.LayerNorm.weight	, f'''encoder.transformer_cells.{i}.layer_norm.gamma'''   )

						# intermediate
						UpperCAmelCase__					:   BertIntermediate       				=       layer.intermediate

						UpperCAmelCase__					:   int       				=       check_and_map_params(
						    intermediate.dense.bias	, f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''   )
						UpperCAmelCase__					:   Dict       				=       check_and_map_params(
						    intermediate.dense.weight	, f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''   )

						# output
						UpperCAmelCase__					:   BertOutput       				=       layer.output

						UpperCAmelCase__					:   Union[str, Any]       				=       check_and_map_params(
						    bert_output.dense.bias	, f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''   )
						UpperCAmelCase__					:   Any       				=       check_and_map_params(
						    bert_output.dense.weight	, f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''   )
						UpperCAmelCase__					:   Optional[int]       				=       check_and_map_params(
						    bert_output.LayerNorm.bias	, f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''   )
						UpperCAmelCase__					:   List[Any]       				=       check_and_map_params(
						    bert_output.LayerNorm.weight	, f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''   )

			# Save space and energy 🎄
			hf_bort_model.half()

			# Compare output of both models
			UpperCAmelCase__					:   Optional[int]       				=       RobertaTokenizer.from_pretrained("""roberta-base"""   )

			UpperCAmelCase__					:   str       				=       tokenizer.encode_plus(UpperCamelCase__   )["""input_ids"""]

			# Get gluon output
			UpperCAmelCase__					:   Dict       				=       mx.nd.array([input_ids]   )
			UpperCAmelCase__					:   str       				=       original_bort(inputs=UpperCamelCase__	, token_types=[]   )

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       BertModel.from_pretrained(UpperCamelCase__   )
			hf_bort_model.eval()

			UpperCAmelCase__					:   List[str]       				=       tokenizer.encode_plus(UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Tuple       				=       hf_bort_model(**UpperCamelCase__   )[0]

			UpperCAmelCase__					:   Tuple       				=       output_gluon[0].asnumpy()
			UpperCAmelCase__					:   Dict       				=       output_hf[0].detach().numpy()

			UpperCAmelCase__					:   Dict       				=       np.max(np.abs(hf_layer - gluon_layer   )   ).item()
			UpperCAmelCase__					:   Optional[Any]       				=       np.allclose(UpperCamelCase__	, UpperCamelCase__	, atol=1e-3   )

			if success:
						print("""✔️ Both model do output the same tensors"""   )
			else:
						print("""❌ Both model do **NOT** output the same tensors"""   )
						print("""Absolute difference is:"""	, UpperCamelCase__   )


if __name__ == "__main__":
		__A		=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__A		=parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



'''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


__A		=logging.get_logger(__name__)

__A		='▁'

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

__A		={
    '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'
        ),
    }
}

__A		={
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
__A		=['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   (       a__		):
					lowerCAmelCase :int						=   VOCAB_FILES_NAMES
					lowerCAmelCase :Dict						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase :str						=   PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase :List[str]						=   ['''input_ids''', '''attention_mask''']

					lowerCAmelCase :List[int]						=   []
					lowerCAmelCase :List[int]						=   []




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

								UpperCAmelCase__					:   Dict       				=       {} if sp_model_kwargs is None else sp_model_kwargs

								UpperCAmelCase__					:   Dict       				=       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=_lowerCamelCase							,							tgt_lang=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)

								UpperCAmelCase__					:   str       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(str(_lowerCamelCase))
								UpperCAmelCase__					:   int       				=       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
								UpperCAmelCase__					:   Union[str, Any]       				=       {"""<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
								UpperCAmelCase__					:   List[str]       				=       1

								UpperCAmelCase__					:   Optional[int]       				=       len(self.sp_model)
								UpperCAmelCase__					:   List[str]       				=       {
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowerCamelCase)
								}
								UpperCAmelCase__					:   Optional[int]       				=       {v: k for k, v in self.lang_code_to_id.items()}
								UpperCAmelCase__					:   Optional[int]       				=       len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
								UpperCAmelCase__					:   Union[str, Any]       				=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

								UpperCAmelCase__					:   Dict       				=       src_lang if src_lang is not None else """en_XX"""
								UpperCAmelCase__					:   str       				=       self.lang_code_to_id[self._src_lang]
								UpperCAmelCase__					:   Tuple       				=       tgt_lang
								self.set_src_lang_special_tokens(self._src_lang)




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




					@src_lang.setter
					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       new_src_lang
								self.set_src_lang_special_tokens(self._src_lang)




					def __getstate__( self):
								UpperCAmelCase__					:   int       				=       self.__dict__.copy()
								UpperCAmelCase__					:   Tuple       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__					:   Optional[Any]       				=       {}

								UpperCAmelCase__					:   Any       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase__					:   Dict       				=       self.sp_model.PieceToId(_lowerCamelCase)

								# 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							,							_lowerCamelCase):
								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							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       []
								UpperCAmelCase__					:   List[Any]       				=       """"""
								UpperCAmelCase__					:   Optional[int]       				=       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(_lowerCamelCase) + token
														UpperCAmelCase__					:   Tuple       				=       True
														UpperCAmelCase__					:   List[str]       				=       []
											else:
														current_sub_tokens.append(_lowerCamelCase)
														UpperCAmelCase__					:   Dict       				=       False
								out_string += self.sp_model.decode(_lowerCamelCase)
								return out_string.strip()




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   Any       				=       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) and os.path.isfile(self.vocab_file):
											copyfile(self.vocab_file							,							_lowerCamelCase)
								elif not os.path.isfile(self.vocab_file):
											with open(_lowerCamelCase							,							"""wb""") as fi:
														UpperCAmelCase__					:   List[str]       				=       self.sp_model.serialized_model_proto()
														fi.write(_lowerCamelCase)

								return (out_vocab_file,)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

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

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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = 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							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								if src_lang is None or tgt_lang is None:
											raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
								UpperCAmelCase__					:   Any       				=       src_lang
								UpperCAmelCase__					:   Dict       				=       self(_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       self.convert_tokens_to_ids(_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       tgt_lang_id
								return inputs




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = "en_XX"							,							_lowerCamelCase = None							,							_lowerCamelCase = "ro_RO"							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Tuple       				=       src_lang
								UpperCAmelCase__					:   Optional[Any]       				=       tgt_lang
								return super().prepare_seqaseq_batch(_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self):
								return self.set_src_lang_special_tokens(self.src_lang)




					def 			snake_case__  ( self):
								return self.set_tgt_lang_special_tokens(self.tgt_lang)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Tuple       				=       self.lang_code_to_id[src_lang]
								UpperCAmelCase__					:   Optional[int]       				=       [self.cur_lang_code_id]
								UpperCAmelCase__					:   Optional[int]       				=       [self.eos_token_id]




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       self.lang_code_to_id[tgt_lang]
								UpperCAmelCase__					:   Dict       				=       [self.cur_lang_code_id]
								UpperCAmelCase__					:   Optional[int]       				=       [self.eos_token_id]