






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
		import detectrona





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"layoutlmv2"

	def __init__(							self	,   UpperCAmelCase=3_0522	,   UpperCAmelCase=768	,   UpperCAmelCase=12	,   UpperCAmelCase=12	,   UpperCAmelCase=3072	,   UpperCAmelCase="gelu"	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=512	,   UpperCAmelCase=2	,   UpperCAmelCase=0.02	,   UpperCAmelCase=1e-12	,   UpperCAmelCase=0	,   UpperCAmelCase=1024	,   UpperCAmelCase=128	,   UpperCAmelCase=32	,   UpperCAmelCase=True	,   UpperCAmelCase=256	,   UpperCAmelCase=64	,   UpperCAmelCase=True	,   UpperCAmelCase=[7, 7, 256]	,   UpperCAmelCase=128	,   UpperCAmelCase=128	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase=None	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(
							    vocab_size=UpperCAmelCase	,   hidden_size=UpperCAmelCase	,   num_hidden_layers=UpperCAmelCase	,   num_attention_heads=UpperCAmelCase	,   intermediate_size=UpperCAmelCase	,   hidden_act=UpperCAmelCase	,   hidden_dropout_prob=UpperCAmelCase	,   attention_probs_dropout_prob=UpperCAmelCase	,   max_position_embeddings=UpperCAmelCase	,   type_vocab_size=UpperCAmelCase	,   initializer_range=UpperCAmelCase	,   layer_norm_eps=UpperCAmelCase	,   pad_token_id=UpperCAmelCase	,   **UpperCAmelCase	,   )
							_UpperCAmelCase     =      max_ad_position_embeddings
							_UpperCAmelCase     =      max_rel_pos
							_UpperCAmelCase     =      rel_pos_bins
							_UpperCAmelCase     =      fast_qkv
							_UpperCAmelCase     =      max_rel_ad_pos
							_UpperCAmelCase     =      rel_ad_pos_bins
							_UpperCAmelCase     =      convert_sync_batchnorm
							_UpperCAmelCase     =      image_feature_pool_shape
							_UpperCAmelCase     =      coordinate_size
							_UpperCAmelCase     =      shape_size
							_UpperCAmelCase     =      has_relative_attention_bias
							_UpperCAmelCase     =      has_spatial_attention_bias
							_UpperCAmelCase     =      has_visual_segment_embedding
							_UpperCAmelCase     =      (
							    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
							)

	@classmethod
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return {
							    "MODEL.MASK_ON": True,
							    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
							    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
							    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
							    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
							    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
							    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
							    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
							    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
							    "MODEL.POST_NMS_TOPK_TEST": 1000,
							    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
							    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
							    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
							    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
							    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
							    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
							    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
							    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
							    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
							    "MODEL.RESNETS.DEPTH": 101,
							    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
							    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
							    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
							    "MODEL.RESNETS.NUM_GROUPS": 32,
							    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
							    "MODEL.RESNETS.STRIDE_IN_1X1": False,
							}

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      detectrona.config.get_cfg()
							for k, v in self.detectrona_config_args.items():
													_UpperCAmelCase     =      k.split('.'   )
													_UpperCAmelCase     =      detectrona_config
													for attribute in attributes[:-1]:
																			_UpperCAmelCase     =      getattr(UpperCAmelCase	,   UpperCAmelCase   )
													setattr(UpperCAmelCase	,   attributes[-1]	,   UpperCAmelCase   )

							return detectrona_config








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


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"regnet"
	UpperCamelCase__								=				["x", "y"]

	def __init__(							self	,   UpperCAmelCase=3	,   UpperCAmelCase=32	,   UpperCAmelCase=[128, 192, 512, 1088]	,   UpperCAmelCase=[2, 6, 12, 2]	,   UpperCAmelCase=64	,   UpperCAmelCase="y"	,   UpperCAmelCase="relu"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase   )
							if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types   )}"""   )
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      embedding_size
							_UpperCAmelCase     =      hidden_sizes
							_UpperCAmelCase     =      depths
							_UpperCAmelCase     =      groups_width
							_UpperCAmelCase     =      layer_type
							_UpperCAmelCase     =      hidden_act
							# always downsample in the first stage
							_UpperCAmelCase     =      True
