


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


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)

UpperCAmelCase   :    int     =		{
    "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        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		"detr"
		UpperCamelCase     :					Tuple			       =		["past_key_values"]
		UpperCamelCase     :					Union[str, Any]			       =		{
		    "hidden_size": "d_model",
		    "num_attention_heads": "encoder_attention_heads",
		}







		def __init__( self		,      A=True		,      A=None		,      A=3		,      A=1_00		,      A=6		,      A=20_48		,      A=8		,      A=6		,      A=20_48		,      A=8		,      A=0.0		,      A=0.0		,      A=True		,      A="relu"		,      A=2_56		,      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		,      )  -> List[str]:







					'''simple docstring'''







					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."""	)
											lowerCamelCase    		=  CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]	)
								elif isinstance(A		,      A	):
											lowerCamelCase    		=  backbone_config.get("""model_type"""	)
											lowerCamelCase    		=  CONFIG_MAPPING[backbone_model_type]
											lowerCamelCase    		=  config_class.from_dict(A	)
								# set timm attributes to None
								lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase    		=  None, None, None

					lowerCamelCase    		=  use_timm_backbone
					lowerCamelCase    		=  backbone_config
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  num_queries
					lowerCamelCase    		=  d_model
					lowerCamelCase    		=  encoder_ffn_dim
					lowerCamelCase    		=  encoder_layers
					lowerCamelCase    		=  encoder_attention_heads
					lowerCamelCase    		=  decoder_ffn_dim
					lowerCamelCase    		=  decoder_layers
					lowerCamelCase    		=  decoder_attention_heads
					lowerCamelCase    		=  dropout
					lowerCamelCase    		=  attention_dropout
					lowerCamelCase    		=  activation_dropout
					lowerCamelCase    		=  activation_function
					lowerCamelCase    		=  init_std
					lowerCamelCase    		=  init_xavier_std
					lowerCamelCase    		=  encoder_layerdrop
					lowerCamelCase    		=  decoder_layerdrop
					lowerCamelCase    		=  encoder_layers
					lowerCamelCase    		=  auxiliary_loss
					lowerCamelCase    		=  position_embedding_type
					lowerCamelCase    		=  backbone
					lowerCamelCase    		=  use_pretrained_backbone
					lowerCamelCase    		=  dilation
					# Hungarian matcher
					lowerCamelCase    		=  class_cost
					lowerCamelCase    		=  bbox_cost
					lowerCamelCase    		=  giou_cost
					# Loss coefficients
					lowerCamelCase    		=  mask_loss_coefficient
					lowerCamelCase    		=  dice_loss_coefficient
					lowerCamelCase    		=  bbox_loss_coefficient
					lowerCamelCase    		=  giou_loss_coefficient
					lowerCamelCase    		=  eos_coefficient
					super().__init__(is_encoder_decoder=A		,      **A	)







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					return self.encoder_attention_heads







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					return self.d_model







		@classmethod
		def 					__A   ( cls		,      A		,      **A	)  -> Union[str, Any]:







					'''simple docstring'''







					return cls(backbone_config=A		,      **A	)







		def 					__A   ( self	)  -> Dict[str, any]:







					'''simple docstring'''







					lowerCamelCase    		=  copy.deepcopy(self.__dict__	)
					if output["backbone_config"] is not None:
								lowerCamelCase    		=  self.backbone_config.to_dict()
					lowerCamelCase    		=  self.__class__.model_type
					return output







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		version.parse("1.11"  )







		@property
		def 					__A   ( self	)  -> Mapping[str, Mapping[int, str]]:







					'''simple docstring'''







					return OrderedDict(
					    [
					        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
					        ("""pixel_mask""", {0: """batch"""}),
					    ]	)







		@property
		def 					__A   ( self	)  -> float:







					'''simple docstring'''







					return 1e-5







		@property
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					return 12





import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

			return x * tf.tanh(tf.math.softplus(lowerCamelCase__      )      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)      ))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}'      )

