



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


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """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 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Union[str, Any]             =   """layoutlmv2"""


							def __init__(							self					,					a_=30_522					,					a_=768					,					a_=12					,					a_=12					,					a_=3_072					,					a_="gelu"					,					a_=0.1					,					a_=0.1					,					a_=512					,					a_=2					,					a_=0.02					,					a_=1E-1_2					,					a_=0					,					a_=1_024					,					a_=128					,					a_=32					,					a_=True					,					a_=256					,					a_=64					,					a_=True					,					a_=[7, 7, 256]					,					a_=128					,					a_=128					,					a_=True					,					a_=True					,					a_=False					,					a_=None					,					**a_					,					):
											super().__init__(
											    vocab_size=a_					,					hidden_size=a_					,					num_hidden_layers=a_					,					num_attention_heads=a_					,					intermediate_size=a_					,					hidden_act=a_					,					hidden_dropout_prob=a_					,					attention_probs_dropout_prob=a_					,					max_position_embeddings=a_					,					type_vocab_size=a_					,					initializer_range=a_					,					layer_norm_eps=a_					,					pad_token_id=a_					,					**a_					,					)
											a__			 =						max_ad_position_embeddings
											a__			 =						max_rel_pos
											a__			 =						rel_pos_bins
											a__			 =						fast_qkv
											a__			 =						max_rel_ad_pos
											a__			 =						rel_ad_pos_bins
											a__			 =						convert_sync_batchnorm
											a__			 =						image_feature_pool_shape
											a__			 =						coordinate_size
											a__			 =						shape_size
											a__			 =						has_relative_attention_bias
											a__			 =						has_spatial_attention_bias
											a__			 =						has_visual_segment_embedding
											a__			 =						(
											    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
											)


							@classmethod
							def _a (							self						):
											return {
											    "MODEL.MASK_ON": True,
											    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
											    "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": 2_000,
											    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
											    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
											    "MODEL.POST_NMS_TOPK_TEST": 1_000,
											    "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 _a (							self						):
											a__			 =						detectrona.config.get_cfg()
											for k, v in self.detectrona_config_args.items():
															a__			 =						k.split("""."""						)
															a__			 =						detectrona_config
															for attribute in attributes[:-1]:
																			a__			 =						getattr(a_					,					a_						)
															setattr(a_					,					attributes[-1]					,					a_						)

											return detectrona_config






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						gpt.pos_emb
				trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()						)
				trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()						)
				trajectory_transformer.head.load_state_dict(gpt.head.state_dict()						)

				for i, block in enumerate(gpt.blocks						):
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()						)

								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()						)
								trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()						)
								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()						)
								trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()						)

				torch.save(trajectory_transformer.state_dict()	,						"""pytorch_model.bin"""						)


if __name__ == "__main__":
	UpperCAmelCase								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

