






import copy
from typing import Dict, Optional

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


__UpperCAmelCase										=		logging.get_logger(__name__)

__UpperCAmelCase										=		{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}



class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					int 		=						'''oneformer'''
		_snake_case					:					Tuple 		=						{'''hidden_size''': '''hidden_dim'''}



		def __init__(							self       ,	_UpperCamelCase = None       ,	_UpperCamelCase = 2_5_5       ,	_UpperCamelCase = 1_5_0       ,	_UpperCamelCase = 0.1       ,	_UpperCamelCase = 2.0       ,	_UpperCamelCase = 5.0       ,	_UpperCamelCase = 5.0       ,	_UpperCamelCase = 0.5       ,	_UpperCamelCase = 0.07       ,	_UpperCamelCase = 1_2_5_4_4       ,	_UpperCamelCase = 3.0       ,	_UpperCamelCase = 0.75       ,	_UpperCamelCase = 0.02       ,	_UpperCamelCase = 1.0       ,	_UpperCamelCase = 1E-05       ,	_UpperCamelCase = False       ,	_UpperCamelCase = True       ,	_UpperCamelCase = True       ,	_UpperCamelCase = [4, 8, 1_6, 3_2]       ,	_UpperCamelCase = 7_7       ,	_UpperCamelCase = 2_5_6       ,	_UpperCamelCase = 7_7       ,	_UpperCamelCase = 6       ,	_UpperCamelCase = 4_9_4_0_8       ,	_UpperCamelCase = 2       ,	_UpperCamelCase = 1_6       ,	_UpperCamelCase = 2_5_6       ,	_UpperCamelCase = 2_5_6       ,	_UpperCamelCase = 2_5_6       ,	_UpperCamelCase = 1_0_2_4       ,	_UpperCamelCase = "GN"       ,	_UpperCamelCase = 6       ,	_UpperCamelCase = 1_0       ,	_UpperCamelCase = True       ,	_UpperCamelCase = 8       ,	_UpperCamelCase = 0.1       ,	_UpperCamelCase = 2_0_4_8       ,	_UpperCamelCase = False       ,	_UpperCamelCase = False       ,	_UpperCamelCase = 2       ,	_UpperCamelCase = 4       ,	**_UpperCamelCase       ,	)			->							Optional[Any]:
							if backbone_config is None:
												logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'     )
												UpperCAmelCase_					:   Optional[Any]   =		CONFIG_MAPPING['swin'](
												    image_size=2_2_4       ,	in_channels=3       ,	patch_size=4       ,	embed_dim=9_6       ,	depths=[2, 2, 6, 2]       ,	num_heads=[3, 6, 1_2, 2_4]       ,	window_size=7       ,	drop_path_rate=0.3       ,	use_absolute_embeddings=_UpperCamelCase       ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,	)
							elif isinstance(_UpperCamelCase       ,	_UpperCamelCase     ):
												UpperCAmelCase_					:   Optional[Any]   =		backbone_config.get('model_type'     )
												UpperCAmelCase_					:   str   =		CONFIG_MAPPING[backbone_model_type]
												UpperCAmelCase_					:   Union[str, Any]   =		config_class.from_dict(_UpperCamelCase     )

							UpperCAmelCase_					:   Dict   =		backbone_config

							UpperCAmelCase_					:   Optional[Any]   =		ignore_value
							UpperCAmelCase_					:   Union[str, Any]   =		num_queries
							UpperCAmelCase_					:   Union[str, Any]   =		no_object_weight
							UpperCAmelCase_					:   Dict   =		class_weight
							UpperCAmelCase_					:   Optional[int]   =		mask_weight
							UpperCAmelCase_					:   Union[str, Any]   =		dice_weight
							UpperCAmelCase_					:   Any   =		contrastive_weight
							UpperCAmelCase_					:   Tuple   =		contrastive_temperature
							UpperCAmelCase_					:   Dict   =		train_num_points
							UpperCAmelCase_					:   str   =		oversample_ratio
							UpperCAmelCase_					:   Optional[Any]   =		importance_sample_ratio
							UpperCAmelCase_					:   int   =		init_std
							UpperCAmelCase_					:   Any   =		init_xavier_std
							UpperCAmelCase_					:   List[str]   =		layer_norm_eps
							UpperCAmelCase_					:   Optional[int]   =		is_training
							UpperCAmelCase_					:   str   =		use_auxiliary_loss
							UpperCAmelCase_					:   int   =		output_auxiliary_logits
							UpperCAmelCase_					:   Dict   =		strides
							UpperCAmelCase_					:   List[Any]   =		task_seq_len
							UpperCAmelCase_					:   List[str]   =		text_encoder_width
							UpperCAmelCase_					:   Optional[Any]   =		text_encoder_context_length
							UpperCAmelCase_					:   Optional[Any]   =		text_encoder_num_layers
							UpperCAmelCase_					:   Tuple   =		text_encoder_vocab_size
							UpperCAmelCase_					:   str   =		text_encoder_proj_layers
							UpperCAmelCase_					:   Optional[int]   =		text_encoder_n_ctx
							UpperCAmelCase_					:   List[Any]   =		conv_dim
							UpperCAmelCase_					:   int   =		mask_dim
							UpperCAmelCase_					:   Union[str, Any]   =		hidden_dim
							UpperCAmelCase_					:   Tuple   =		encoder_feedforward_dim
							UpperCAmelCase_					:   Union[str, Any]   =		norm
							UpperCAmelCase_					:   List[str]   =		encoder_layers
							UpperCAmelCase_					:   Any   =		decoder_layers
							UpperCAmelCase_					:   List[str]   =		use_task_norm
							UpperCAmelCase_					:   Optional[int]   =		num_attention_heads
							UpperCAmelCase_					:   Optional[Any]   =		dropout
							UpperCAmelCase_					:   Optional[Any]   =		dim_feedforward
							UpperCAmelCase_					:   int   =		pre_norm
							UpperCAmelCase_					:   List[str]   =		enforce_input_proj
							UpperCAmelCase_					:   Optional[int]   =		query_dec_layers
							UpperCAmelCase_					:   Any   =		common_stride
							UpperCAmelCase_					:   Dict   =		decoder_layers

							super().__init__(**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self     )			->							Dict[str, any]:
							UpperCAmelCase_					:   List[Any]   =		copy.deepcopy(self.__dict__     )
							UpperCAmelCase_					:   int   =		self.backbone_config.to_dict()
							UpperCAmelCase_					:   Optional[int]   =		self.__class__.model_type
							return output














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		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(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






