

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

							A__      :     int       =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




