



import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'conditional_detr'
							lowerCamelCase__   			=		['past_key_values']
							lowerCamelCase__   			=		{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(   self,			__a=True,			__a=None,			__a=3,			__a=300,			__a=6,			__a=2048,			__a=8,			__a=6,			__a=2048,			__a=8,			__a=0.0,			__a=0.0,			__a=True,			__a="relu",			__a=256,			__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=2,			__a=5,			__a=2,			__a=1,			__a=1,			__a=2,			__a=5,			__a=2,			__a=0.25,			**__a,			):

														'''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     :							str    =    CONFIG_MAPPING["resnet"](out_features=["stage4"])
																					elif isinstance(__a,			__a):
																												_lowerCAmelCase     :							Union[str, Any]    =    backbone_config.get("model_type")
																												_lowerCAmelCase     :							Optional[Any]    =    CONFIG_MAPPING[backbone_model_type]
																												_lowerCAmelCase     :							Dict    =    config_class.from_dict(__a)

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.encoder_attention_heads







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.d_model







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    copy.deepcopy(self.__dict__)
														if self.backbone_config is not None:
																					_lowerCAmelCase     :							List[str]    =    self.backbone_config.to_dict()
														_lowerCAmelCase     :							int    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-5







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 12

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["ChineseCLIPFeatureExtractor"]
				__lowerCamelCase							 =     ["ChineseCLIPImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ChineseCLIPModel",
				    "ChineseCLIPPreTrainedModel",
				    "ChineseCLIPTextModel",
				    "ChineseCLIPVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ChineseCLIPConfig,
				    ChineseCLIPOnnxConfig,
				    ChineseCLIPTextConfig,
				    ChineseCLIPVisionConfig,
				)
				from .processing_chinese_clip import ChineseCLIPProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_chinese_clip import (
								    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ChineseCLIPModel,
								    ChineseCLIPPreTrainedModel,
								    ChineseCLIPTextModel,
								    ChineseCLIPVisionModel,
								)

else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





