






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__:					List[str]							=     logging.get_logger(__name__)

__magic_name__:					str							=     {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Optional[int]											=   '''vit_mae'''
	def __init__(    self      ,       lowerCAmelCase__=7_68      ,       lowerCAmelCase__=12      ,       lowerCAmelCase__=12      ,       lowerCAmelCase__=30_72      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1e-1_2      ,       lowerCAmelCase__=2_24      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=16      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=8      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=0.7_5      ,       lowerCAmelCase__=False      ,       **lowerCAmelCase__      ,       )							->				Tuple:
							super().__init__(**lowerCAmelCase__			)

							__magic_name__      :       str       						= hidden_size
							__magic_name__      :       Any       						= num_hidden_layers
							__magic_name__      :       Optional[int]       						= num_attention_heads
							__magic_name__      :       Union[str, Any]       						= intermediate_size
							__magic_name__      :       List[Any]       						= hidden_act
							__magic_name__      :       Optional[Any]       						= hidden_dropout_prob
							__magic_name__      :       int       						= attention_probs_dropout_prob
							__magic_name__      :       List[Any]       						= initializer_range
							__magic_name__      :       Optional[int]       						= layer_norm_eps
							__magic_name__      :       str       						= image_size
							__magic_name__      :       Optional[int]       						= patch_size
							__magic_name__      :       Any       						= num_channels
							__magic_name__      :       Optional[int]       						= qkv_bias
							__magic_name__      :       Tuple       						= decoder_num_attention_heads
							__magic_name__      :       str       						= decoder_hidden_size
							__magic_name__      :       List[str]       						= decoder_num_hidden_layers
							__magic_name__      :       str       						= decoder_intermediate_size
							__magic_name__      :       Optional[int]       						= mask_ratio
							__magic_name__      :       Union[str, Any]       						= norm_pix_loss








from typing import TYPE_CHECKING

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


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
