

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


a__												=						logging.get_logger(__name__)

a__												=						{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[Any]             =       "detr"
							UpperCAmelCase__			:		int             =       ["past_key_values"]
							UpperCAmelCase__			:		int             =       {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}


							def __init__(     self			,				_a=True			,				_a=None			,				_a=3			,				_a=1_0_0			,				_a=6			,				_a=2_0_4_8			,				_a=8			,				_a=6			,				_a=2_0_4_8			,				_a=8			,				_a=0.0			,				_a=0.0			,				_a=True			,				_a="relu"			,				_a=2_5_6			,				_a=0.1			,				_a=0.0			,				_a=0.0			,				_a=0.02			,				_a=1.0			,				_a=False			,				_a="sine"			,				_a="resnet50"			,				_a=True			,				_a=False			,				_a=1			,				_a=5			,				_a=2			,				_a=1			,				_a=1			,				_a=5			,				_a=2			,				_a=0.1			,				**_a			,				)       ->       Any:
												if backbone_config is not None and use_timm_backbone:
																	raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''			)

												if not use_timm_backbone:
																	if backbone_config is None:
																						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''			)
																						_a      :       Dict   =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
																	elif isinstance(_a			,				_a			):
																						_a      :       Union[str, Any]   =						backbone_config.get('''model_type'''			)
																						_a      :       int   =						CONFIG_MAPPING[backbone_model_type]
																						_a      :       Optional[int]   =						config_class.from_dict(_a			)
																	# set timm attributes to None
																	_a ,   _a ,   _a      :       Any   =						None, None, None

												_a      :       int   =						use_timm_backbone
												_a      :       int   =						backbone_config
												_a      :       Optional[int]   =						num_channels
												_a      :       int   =						num_queries
												_a      :       Dict   =						d_model
												_a      :       Optional[Any]   =						encoder_ffn_dim
												_a      :       Any   =						encoder_layers
												_a      :       Optional[Any]   =						encoder_attention_heads
												_a      :       Dict   =						decoder_ffn_dim
												_a      :       Optional[Any]   =						decoder_layers
												_a      :       Optional[int]   =						decoder_attention_heads
												_a      :       str   =						dropout
												_a      :       List[Any]   =						attention_dropout
												_a      :       Dict   =						activation_dropout
												_a      :       Union[str, Any]   =						activation_function
												_a      :       Optional[Any]   =						init_std
												_a      :       Dict   =						init_xavier_std
												_a      :       Any   =						encoder_layerdrop
												_a      :       List[str]   =						decoder_layerdrop
												_a      :       int   =						encoder_layers
												_a      :       int   =						auxiliary_loss
												_a      :       Optional[Any]   =						position_embedding_type
												_a      :       str   =						backbone
												_a      :       List[Any]   =						use_pretrained_backbone
												_a      :       Any   =						dilation
												# Hungarian matcher
												_a      :       str   =						class_cost
												_a      :       int   =						bbox_cost
												_a      :       List[str]   =						giou_cost
												# Loss coefficients
												_a      :       Union[str, Any]   =						mask_loss_coefficient
												_a      :       Optional[Any]   =						dice_loss_coefficient
												_a      :       List[str]   =						bbox_loss_coefficient
												_a      :       List[Any]   =						giou_loss_coefficient
												_a      :       List[Any]   =						eos_coefficient
												super().__init__(is_encoder_decoder=_a			,				**_a			)


							@property
							def 							__lowercase							(     self			)       ->       int:
												return self.encoder_attention_heads


							@property
							def 							__lowercase							(     self			)       ->       int:
												return self.d_model


							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       Optional[int]:
												return cls(backbone_config=_a			,				**_a			)




							def 							__lowercase							(     self			)       ->       Dict[str, any]:
												_a      :       int   =						copy.deepcopy(self.__dict__			)
												if output["backbone_config"] is not None:
																	_a      :       Optional[Any]   =						self.backbone_config.to_dict()
												_a      :       str   =						self.__class__.model_type
												return output



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												        ('''pixel_mask''', {0: '''batch'''}),
												    ]			)


							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-5




							@property
							def 							__lowercase							(     self			)       ->       int:
												return 1_2






import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       LxmertTokenizer
							UpperCAmelCase__			:		Optional[Any]             =       LxmertTokenizerFast
							UpperCAmelCase__			:		Any             =       True
							UpperCAmelCase__			:		Dict             =       True


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().setUp()

												_a      :       int   =						[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       Tuple   =						'''UNwant\u00E9d,running'''
												_a      :       str   =						'''unwanted, running'''
												return input_text, output_text


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.tokenizer_class(self.vocab_file			)

												_a      :       str   =						tokenizer.tokenize('''UNwant\u00E9d,running'''			)
												self.assertListEqual(_a			,				['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_a			)			,				[7, 4, 5, 1_0, 8, 9]			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.test_rust_tokenizer:
																	return

												_a      :       Optional[Any]   =						self.get_tokenizer()
												_a      :       str   =						self.get_rust_tokenizer()

												_a      :       Optional[Any]   =						'''I was born in 92000, and this is falsé.'''

												_a      :       Optional[Any]   =						tokenizer.tokenize(_a			)
												_a      :       List[Any]   =						rust_tokenizer.tokenize(_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       List[Any]   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
												_a      :       Any   =						rust_tokenizer.encode(_a			,				add_special_tokens=_a			)
												self.assertListEqual(_a			,				_a			)

												_a      :       Dict   =						self.get_rust_tokenizer()
												_a      :       Optional[int]   =						tokenizer.encode(_a			)
												_a      :       Dict   =						rust_tokenizer.encode(_a			)
												self.assertListEqual(_a			,				_a			)



