


"""simple docstring"""






import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			PhobertTokenizer
	SCREAMING_SNAKE_CASE					   =			False

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> List[str]:

				"""simple docstring"""

				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				__lowerCAmelCase   :						List[Any]				= ["T@@", "i", "I", "R@@", "r", "e@@"]
				__lowerCAmelCase   :						Optional[Any]				= dict(zip(_SCREAMING_SNAKE_CASE ,    range(len(_SCREAMING_SNAKE_CASE))))
				__lowerCAmelCase   :						str				= ["#version: 0.2", "l à</w>"]
				__lowerCAmelCase   :						str				= {"unk_token": "<unk>"}

				__lowerCAmelCase   :						Union[str, Any]				= os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["vocab_file"])
				__lowerCAmelCase   :						Optional[int]				= 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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	List[str])       -> List[str]:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map)
				return PhobertTokenizer.from_pretrained(self.tmpdirname ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= "Tôi là VinAI Research"
				__lowerCAmelCase   :						str				= "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
				return input_text, output_text







	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= PhobertTokenizer(self.vocab_file ,    self.merges_file ,    **self.special_tokens_map)
				__lowerCAmelCase   :						Optional[int]				= "Tôi là VinAI Research"
				__lowerCAmelCase   :						Optional[Any]				= "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
				__lowerCAmelCase   :						Dict				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				print(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						int				= tokens + [tokenizer.unk_token]

				__lowerCAmelCase   :						str				= [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)




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			torch.save(model.state_dict()      ,__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)