
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


_SCREAMING_SNAKE_CASE    :      Tuple          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      List[Any]          =			{
    '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__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"detr"
	_lowerCAmelCase							       =				["past_key_values"]
	_lowerCAmelCase							       =				{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}





	def __init__(			self				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=3				,       UpperCamelCase__=100				,       UpperCamelCase__=6				,       UpperCamelCase__=2048				,       UpperCamelCase__=8				,       UpperCamelCase__=6				,       UpperCamelCase__=2048				,       UpperCamelCase__=8				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0				,       UpperCamelCase__=True				,       UpperCamelCase__="relu"				,       UpperCamelCase__=256				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=1.0				,       UpperCamelCase__=False				,       UpperCamelCase__="sine"				,       UpperCamelCase__="resnet50"				,       UpperCamelCase__=True				,       UpperCamelCase__=False				,       UpperCamelCase__=1				,       UpperCamelCase__=5				,       UpperCamelCase__=2				,       UpperCamelCase__=1				,       UpperCamelCase__=1				,       UpperCamelCase__=5				,       UpperCamelCase__=2				,       UpperCamelCase__=0.1				,       **UpperCamelCase__				,       ):
					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__  :	int         =							CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
									elif isinstance(UpperCamelCase__				,       UpperCamelCase__		):
													A__  :	Optional[Any]         =							backbone_config.get('''model_type'''		)
													A__  :	int         =							CONFIG_MAPPING[backbone_model_type]
													A__  :	Optional[int]         =							config_class.from_dict(UpperCamelCase__		)
									# set timm attributes to None
									A__       ,							A__       ,							A__  :	List[Any]         =							None, None, None

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





	@property
	def   __snake_case				(			self		):
					return self.encoder_attention_heads





	@property
	def   __snake_case				(			self		):
					return self.d_model





	@classmethod
	def   __snake_case				(			cls				,       UpperCamelCase__				,       **UpperCamelCase__		):
					return cls(backbone_config=UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	str         =							copy.deepcopy(self.__dict__		)
					if output["backbone_config"] is not None:
									A__  :	Union[str, Any]         =							self.backbone_config.to_dict()
					A__  :	Any         =							self.__class__.model_type
					return output







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				version.parse("1.11"   )





	@property
	def   __snake_case				(			self		):
					return OrderedDict(
					    [
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					        ('''pixel_mask''', {0: '''batch'''}),
					    ]		)





	@property
	def   __snake_case				(			self		):
					return 1e-5





	@property
	def   __snake_case				(			self		):
					return 12

import numpy as np

_SCREAMING_SNAKE_CASE    :      Any          =			[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self		):
					A__  :	List[Any]         =							np.array(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__  :	Any         =							np.where(letter == self.SQUARE		)
					A__  :	int         =							np.concatenate([indexa + 1, indexa + 1]		)
					return indexes





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							self.SQUARE[indexa - 1, indexa - 1]
					return letter





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							message.lower()
					A__  :	str         =							message.replace(''' '''				,       ''''''		)
					A__  :	Union[str, Any]         =							message.replace('''j'''				,       '''i'''		)

					A__  :	List[Any]         =							np.empty((2, len(UpperCamelCase__		))		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							self.letter_to_numbers(message[letter_index]		)

									A__  :	Optional[Any]         =							numbers[0]
									A__  :	List[str]         =							numbers[1]

					A__  :	List[str]         =							first_step.reshape(2 * len(UpperCamelCase__		)		)
					A__  :	List[Any]         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Dict         =							int(second_step[numbers_index * 2]		)
									A__  :	List[str]         =							int(second_step[(numbers_index * 2) + 1]		)
									A__  :	Dict         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							encoded_message + letter

					return encoded_message





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							message.lower()
					message.replace(''' '''				,       ''''''		)
					A__  :	List[Any]         =							np.empty(2 * len(UpperCamelCase__		)		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	List[str]         =							self.letter_to_numbers(message[letter_index]		)
									A__  :	Dict         =							numbers[0]
									A__  :	int         =							numbers[1]

					A__  :	Optional[Any]         =							first_step.reshape((2, len(UpperCamelCase__		))		)
					A__  :	int         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Tuple         =							int(second_step[0, numbers_index]		)
									A__  :	Dict         =							int(second_step[1, numbers_index]		)
									A__  :	List[str]         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							decoded_message + letter

					return decoded_message