
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   lowerCamelCase	(						UpperCamelCase__							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	CpmAntTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Any     )		->		List[Any]:
         super().setUp()

         snake_case				  =      [
             "<d>",
             "</d>",
             "<s>",
             "</s>",
             "</_>",
             "<unk>",
             "<pad>",
             "</n>",
             "我",
             "是",
             "C",
             "P",
             "M",
             "A",
             "n",
             "t",
         ]
         snake_case				  =      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   :					Dict     )		->		str:
         snake_case				  =      CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      ["今天", "天气", "真", "好", "！"]
         snake_case				  =      tokenizer.tokenize(__A     )
         self.assertListEqual(__A       ,					__A     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      [tokenizer.bos_token] + tokens

         snake_case				  =      [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A     )       ,					__A     )

         snake_case				  =      tokenizer.decode(__A     )
         self.assertEqual(__A       ,					__A     )



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

