

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 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    DebertaTokenizer
				__SCREAMING_SNAKE_CASE      :				Any     =    True
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    DebertaTokenizerFast




				def 					a							(      self ):
											super().setUp()

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

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




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'lower newer'
											snake_case_           =					'lower newer'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					self.get_tokenizer()
											snake_case_           =					'lower newer'
											snake_case_           =					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
											snake_case_           =					tokenizer.tokenize(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]
											snake_case_           =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




				def 					a							(      self ):
											snake_case_           =					self.get_tokenizer()
											snake_case_           =					tokenizer('Hello'   ,     'World' )
											snake_case_           =					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
											self.assertListEqual(tokd['token_type_ids']   ,     snake_case )




				@slow
				def 					a							(      self ):
											snake_case_           =					self.tokenizer_class.from_pretrained('microsoft/deberta-base' )

											snake_case_           =					tokenizer.encode('sequence builders'   ,     add_special_tokens=snake_case )
											snake_case_           =					tokenizer.encode('multi-sequence build'   ,     add_special_tokens=snake_case )

											snake_case_           =					tokenizer.encode(
											    'sequence builders'   ,     add_special_tokens=snake_case   ,     add_prefix_space=snake_case )
											snake_case_           =					tokenizer.encode(
											    'sequence builders'   ,     'multi-sequence build'   ,     add_special_tokens=snake_case   ,     add_prefix_space=snake_case )

											snake_case_           =					tokenizer.build_inputs_with_special_tokens(snake_case )
											snake_case_           =					tokenizer.build_inputs_with_special_tokens(snake_case   ,     snake_case )

											assert encoded_sentence == encoded_text_from_decode
											assert encoded_pair == encoded_pair_from_decode




				@slow
				def 					a							(      self ):
											snake_case_           =					[self.tokenizer_class]
											if self.test_rust_tokenizer:
																		tokenizer_classes.append(self.rust_tokenizer_class )

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

																		snake_case_           =					[
																		    '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.',
																		]

																		snake_case_           =					tokenizer(snake_case   ,     padding=snake_case )
																		snake_case_           =					[tokenizer.decode(snake_case   ,     skip_special_tokens=snake_case ) for seq in encoding['input_ids']]

																		# fmt: off
																		snake_case_           =					{
																		    'input_ids': [
																		        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 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, 1_1126, 565, 2_4536, 80, 4_3797, 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, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

																		snake_case_           =					[
																		    '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   ,     snake_case )

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







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''upernet'''




				def __init__(      self   ,     snake_case=None   ,     snake_case=512   ,     snake_case=0.02   ,     snake_case=[1, 2, 3, 6]   ,     snake_case=True   ,     snake_case=0.4   ,     snake_case=384   ,     snake_case=256   ,     snake_case=1   ,     snake_case=False   ,     snake_case=255   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

											if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
																		snake_case_           =					CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
											elif isinstance(snake_case   ,     snake_case ):
																		snake_case_           =					backbone_config.get('model_type' )
																		snake_case_           =					CONFIG_MAPPING[backbone_model_type]
																		snake_case_           =					config_class.from_dict(snake_case )

											snake_case_           =					backbone_config
											snake_case_           =					hidden_size
											snake_case_           =					initializer_range
											snake_case_           =					pool_scales
											snake_case_           =					use_auxiliary_head
											snake_case_           =					auxiliary_loss_weight
											snake_case_           =					auxiliary_in_channels
											snake_case_           =					auxiliary_channels
											snake_case_           =					auxiliary_num_convs
											snake_case_           =					auxiliary_concat_input
											snake_case_           =					loss_ignore_index




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.backbone_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output




