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  A__       (	a__	,       unittest.TestCase      ):
      _UpperCAmelCase     :Dict						   =       CpmAntTokenizer
      _UpperCAmelCase     :Optional[int]						   =       False





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

       UpperCamelCase     : List[str]	     =       [
           '''<d>''',
           '''</d>''',
           '''<s>''',
           '''</s>''',
           '''</_>''',
           '''<unk>''',
           '''<pad>''',
           '''</n>''',
           '''我''',
           '''是''',
           '''C''',
           '''P''',
           '''M''',
           '''A''',
           '''n''',
           '''t''',
       ]
       UpperCamelCase     : List[Any]	     =       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'''




       UpperCamelCase     : Union[str, Any]	     =       CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"       )
       UpperCamelCase     : Optional[Any]	     =       '''今天天气真好！'''
       UpperCamelCase     : str	     =       ['''今天''', '''天气''', '''真''', '''好''', '''！''']
       UpperCamelCase     : int	     =       tokenizer.tokenize(_lowerCamelCase       )
       self.assertListEqual(_lowerCamelCase	,							_lowerCamelCase       )
       UpperCamelCase     : List[str]	     =       '''今天天气真好！'''
       UpperCamelCase     : List[Any]	     =       [tokenizer.bos_token] + tokens

       UpperCamelCase     : Any	     =       [6, 9802, 1_4962, 2082, 831, 244]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase       )	,							_lowerCamelCase       )

       UpperCamelCase     : List[Any]	     =       tokenizer.decode(_lowerCamelCase       )
       self.assertEqual(_lowerCamelCase	,							_lowerCamelCase       )







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
