
"""simple docstring"""






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class       snake_case_       (    _lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					CpmAntTokenizer
	SCREAMING_SNAKE_CASE_:							Dict   			=					False


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().setUp()

					A__							 =       [
					    '<d>',
					    '</d>',
					    '<s>',
					    '</s>',
					    '</_>',
					    '<unk>',
					    '<pad>',
					    '</n>',
					    '我',
					    '是',
					    'C',
					    'P',
					    'M',
					    'A',
					    'n',
					    't',
					]
					A__							 =       os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']						)
					with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'						) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)






	@tooslow
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'						)
					A__							 =       '今天天气真好！'
					A__							 =       ['今天', '天气', '真', '好', '！']
					A__							 =       tokenizer.tokenize(__a						)
					self.assertListEqual(__a  ,					__a						)
					A__							 =       '今天天气真好！'
					A__							 =       [tokenizer.bos_token] + tokens

					A__							 =       [6, 9802, 1_4962, 2082, 831, 244]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(__a						)  ,					__a						)

					A__							 =       tokenizer.decode(__a						)
					self.assertEqual(__a  ,					__a						)










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


