






'''simple docstring'''
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							DebertaTokenizer
 snake_case_												=							True
 snake_case_												=							DebertaTokenizerFast




 def        __magic_name__		( self		:							Optional[int] )		->  List[Any]:
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE__       :			str													=[
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '[UNK]',
       ]
       SCREAMING_SNAKE_CASE__       :			Dict													=dict(zip(__lowercase   , range(len(__lowercase ) ) ) )
       SCREAMING_SNAKE_CASE__       :			Any													=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       SCREAMING_SNAKE_CASE__       :			Any													={'unk_token': '[UNK]'}

       SCREAMING_SNAKE_CASE__       :			Tuple													=os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES['''vocab_file'''] )
       SCREAMING_SNAKE_CASE__       :			str													=os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES['''merges_file'''] )
       with open(self.vocab_file   , '''w'''   , encoding='''utf-8''' ) as fp:
             fp.write(json.dumps(__lowercase ) + '''\n''' )
       with open(self.merges_file   , '''w'''   , encoding='''utf-8''' ) as fp:
             fp.write('''\n'''.join(__lowercase ) )




 def        __magic_name__		( self		:							List[str]   , **__lowercase		:							Union[str, Any] )		->  Any:
       kwargs.update(self.special_tokens_map )
       return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							Dict )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													='lower newer'
       SCREAMING_SNAKE_CASE__       :			List[Any]													='lower newer'
       return input_text, output_text




 def        __magic_name__		( self		:							List[str] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.get_tokenizer()
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='lower newer'
       SCREAMING_SNAKE_CASE__       :			str													=['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       SCREAMING_SNAKE_CASE__       :			List[Any]													=tokenizer.tokenize(__lowercase )
       self.assertListEqual(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tokens + [tokenizer.unk_token]
       SCREAMING_SNAKE_CASE__       :			int													=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )   , __lowercase )




 def        __magic_name__		( self		:							Any )		->  str:
       SCREAMING_SNAKE_CASE__       :			Any													=self.get_tokenizer()
       SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer('''Hello'''   , '''World''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['''token_type_ids''']   , __lowercase )




 @slow
 def        __magic_name__		( self		:							str )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													=tokenizer.encode('''sequence builders'''   , add_special_tokens=__lowercase )
       SCREAMING_SNAKE_CASE__       :			str													=tokenizer.encode('''multi-sequence build'''   , add_special_tokens=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=tokenizer.encode(
           '''sequence builders'''   , add_special_tokens=__lowercase   , add_prefix_space=__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=tokenizer.encode(
           '''sequence builders'''   , '''multi-sequence build'''   , add_special_tokens=__lowercase   , add_prefix_space=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Dict													=tokenizer.build_inputs_with_special_tokens(__lowercase )
       SCREAMING_SNAKE_CASE__       :			str													=tokenizer.build_inputs_with_special_tokens(__lowercase   , __lowercase )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




 @slow
 def        __magic_name__		( self		:							Any )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class )

       for tokenizer_class in tokenizer_classes:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tokenizer_class.from_pretrained('''microsoft/deberta-base''' )

             SCREAMING_SNAKE_CASE__       :			int													=[
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             SCREAMING_SNAKE_CASE__       :			Tuple													=tokenizer(__lowercase   , padding=__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[tokenizer.decode(__lowercase   , skip_special_tokens=__lowercase ) for seq in encoding['input_ids']]

             # fmt: off
             SCREAMING_SNAKE_CASE__       :			List[str]													={
                 'input_ids': [
                     [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                 ],
                 'token_type_ids': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 'attention_mask': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             SCREAMING_SNAKE_CASE__       :			Any													=[
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data   , __lowercase )

             for expected, decoded in zip(__lowercase   , __lowercase ):
                   self.assertEqual(__lowercase   , __lowercase )







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )