



import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class      UpperCamelCase    (						a__       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Any				=	PhobertTokenizer
	lowerCAmelCase	:   str				=	False



	def 			__A							(       self		):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A__							=       ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
							A__							=       dict(zip(SCREAMING_SNAKE_CASE_			,	range(len(SCREAMING_SNAKE_CASE_		)		)		)		)
							A__							=       ['#version: 0.2', 'l à</w>']
							A__							=       {'unk_token': '<unk>'}

							A__							=       os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["vocab_file"]		)
							A__							=       os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["merges_file"]		)

							with open(self.vocab_file			,	"w"			,	encoding="utf-8"		) as fp:
													for token in vocab_tokens:
																			fp.write(F"""{token} {vocab_tokens[token]}\n"""		)
							with open(self.merges_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write("\n".join(SCREAMING_SNAKE_CASE_		)		)



	def 			__A							(       self			,	**UpperCAmelCase__		):
							kwargs.update(self.special_tokens_map		)
							return PhobertTokenizer.from_pretrained(self.tmpdirname			,	**SCREAMING_SNAKE_CASE_		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       'Tôi là VinAI Research'
							A__							=       'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



	def 			__A							(       self		):
							A__							=       PhobertTokenizer(self.vocab_file			,	self.merges_file			,	**self.special_tokens_map		)
							A__							=       'Tôi là VinAI Research'
							A__							=       'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							A__							=       tokenizer.tokenize(SCREAMING_SNAKE_CASE_		)
							print(SCREAMING_SNAKE_CASE_		)
							self.assertListEqual(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_		)

							A__							=       tokens + [tokenizer.unk_token]

							A__							=       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_		)			,	SCREAMING_SNAKE_CASE_		)










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





