from ... import PretrainedConfig


SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}

class        UpperCamelCase						(				lowerCAmelCase__				):
						a__		:Optional[int]      =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
						a__		:Optional[int]      =			'''nezha'''





						def __init__(self			,     __UpperCamelCase=21_128			,     __UpperCamelCase=768			,     __UpperCamelCase=12			,     __UpperCamelCase=12			,     __UpperCamelCase=3_072			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=64			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0			,     __UpperCamelCase=2			,     __UpperCamelCase=3			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(pad_token_id=__UpperCAmelCase			,     bos_token_id=__UpperCAmelCase			,     eos_token_id=__UpperCAmelCase			,     **__UpperCAmelCase						)

												UpperCamelCase_		:							Optional[Any]  =		vocab_size
												UpperCamelCase_		:							str  =		hidden_size
												UpperCamelCase_		:							List[str]  =		num_hidden_layers
												UpperCamelCase_		:							Dict  =		num_attention_heads
												UpperCamelCase_		:							Optional[int]  =		hidden_act
												UpperCamelCase_		:							Dict  =		intermediate_size
												UpperCamelCase_		:							str  =		hidden_dropout_prob
												UpperCamelCase_		:							Union[str, Any]  =		attention_probs_dropout_prob
												UpperCamelCase_		:							str  =		max_position_embeddings
												UpperCamelCase_		:							Any  =		max_relative_position
												UpperCamelCase_		:							Tuple  =		type_vocab_size
												UpperCamelCase_		:							Dict  =		initializer_range
												UpperCamelCase_		:							Optional[int]  =		layer_norm_eps
												UpperCamelCase_		:							Optional[int]  =		classifier_dropout
												UpperCamelCase_		:							Tuple  =		use_cache











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






