



'''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 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Dict   				=					DebertaTokenizer
							lowerCAmelCase       :    Optional[int]   				=					True
							lowerCAmelCase       :    Dict   				=					DebertaTokenizerFast







							def 							__lowercase			(							self					:       Tuple		):
									super().setUp()

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

									_a     :      Tuple        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									_a     :      List[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(__lowerCAmelCase		) + '\n'		)
									with open(self.merges_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write('\n'.join(__lowerCAmelCase		)		)







							def 							__lowercase			(							self					:       Tuple	,**_UpperCAmelCase					:       Optional[int]		):
									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname	,**__lowerCAmelCase		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       List[str]		):
									_a     :      Dict        =							'lower newer'
									_a     :      Optional[Any]        =							'lower newer'
									return input_text, output_text







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Tuple        =							self.get_tokenizer()
									_a     :      str        =							'lower newer'
									_a     :      List[str]        =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									_a     :      Optional[int]        =							tokenizer.tokenize(__lowerCAmelCase		)
									self.assertListEqual(__lowerCAmelCase	,__lowerCAmelCase		)

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







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Dict        =							self.get_tokenizer()
									_a     :      List[str]        =							tokenizer('Hello'	,'World'		)
									_a     :      Tuple        =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd['token_type_ids']	,__lowerCAmelCase		)







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

									_a     :      Any        =							tokenizer.encode('sequence builders'	,add_special_tokens=__lowerCAmelCase		)
									_a     :      List[Any]        =							tokenizer.encode('multi-sequence build'	,add_special_tokens=__lowerCAmelCase		)

									_a     :      int        =							tokenizer.encode(
									    'sequence builders'	,add_special_tokens=__lowerCAmelCase	,add_prefix_space=__lowerCAmelCase		)
									_a     :      Optional[int]        =							tokenizer.encode(
									    'sequence builders'	,'multi-sequence build'	,add_special_tokens=__lowerCAmelCase	,add_prefix_space=__lowerCAmelCase		)

									_a     :      Union[str, Any]        =							tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase		)
									_a     :      Dict        =							tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase	,__lowerCAmelCase		)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







							@slow
							def 							__lowercase			(							self					:       Dict		):
									_a     :      str        =							[self.tokenizer_class]
									if self.test_rust_tokenizer:
											tokenizer_classes.append(self.rust_tokenizer_class		)

									for tokenizer_class in tokenizer_classes:
											_a     :      Any        =							tokenizer_class.from_pretrained('microsoft/deberta-base'		)

											_a     :      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.',
											]

											_a     :      Optional[Any]        =							tokenizer(__lowerCAmelCase	,padding=__lowerCAmelCase		)
											_a     :      List[Any]        =							[tokenizer.decode(__lowerCAmelCase	,skip_special_tokens=__lowerCAmelCase		) for seq in encoding['input_ids']]

											# fmt: off
											_a     :      Union[str, Any]        =							{
											    'input_ids': [
											        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

											_a     :      Optional[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.',
											]

											self.assertDictEqual(encoding.data	,__lowerCAmelCase		)

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





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       str					     ={
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Tuple							  =	'dpr'

      def __init__(						self		,       __lowerCAmelCase=3_0522		,       __lowerCAmelCase=768		,       __lowerCAmelCase=12		,       __lowerCAmelCase=12		,       __lowerCAmelCase=3072		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=512		,       __lowerCAmelCase=2		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-12		,       __lowerCAmelCase=0		,       __lowerCAmelCase="absolute"		,       __lowerCAmelCase = 0		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase		,       **__lowerCAmelCase						)

            lowercase														=	vocab_size
            lowercase														=	hidden_size
            lowercase														=	num_hidden_layers
            lowercase														=	num_attention_heads
            lowercase														=	hidden_act
            lowercase														=	intermediate_size
            lowercase														=	hidden_dropout_prob
            lowercase														=	attention_probs_dropout_prob
            lowercase														=	max_position_embeddings
            lowercase														=	type_vocab_size
            lowercase														=	initializer_range
            lowercase														=	layer_norm_eps
            lowercase														=	projection_dim
            lowercase														=	position_embedding_type



