

import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




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

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											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:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\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 PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(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




