
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					__lowerCAmelCase    (    a__						) ->						Any:
				if "model" in orig_key:
								__a    							=					orig_key.replace('''model.'''		,					''''''						)
				if "norm1" in orig_key:
								__a    							=					orig_key.replace('''norm1'''		,					'''attention.output.LayerNorm'''						)
				if "norm2" in orig_key:
								__a    							=					orig_key.replace('''norm2'''		,					'''output.LayerNorm'''						)
				if "norm" in orig_key:
								__a    							=					orig_key.replace('''norm'''		,					'''LayerNorm'''						)
				if "transformer" in orig_key:
								__a    							=					orig_key.split('''.'''						)[0].split('''_'''						)[-1]
								__a    							=					orig_key.replace(F"""transformer_{layer_num}"""		,					F"""encoder.layer.{layer_num}"""						)
				if "mha.attn" in orig_key:
								__a    							=					orig_key.replace('''mha.attn'''		,					'''attention.self'''						)
				if "mha" in orig_key:
								__a    							=					orig_key.replace('''mha'''		,					'''attention'''						)
				if "W_q" in orig_key:
								__a    							=					orig_key.replace('''W_q'''		,					'''self.query'''						)
				if "W_k" in orig_key:
								__a    							=					orig_key.replace('''W_k'''		,					'''self.key'''						)
				if "W_v" in orig_key:
								__a    							=					orig_key.replace('''W_v'''		,					'''self.value'''						)
				if "ff.0" in orig_key:
								__a    							=					orig_key.replace('''ff.0'''		,					'''intermediate.dense'''						)
				if "ff.2" in orig_key:
								__a    							=					orig_key.replace('''ff.2'''		,					'''output.dense'''						)
				if "ff" in orig_key:
								__a    							=					orig_key.replace('''ff'''		,					'''output.dense'''						)
				if "mlm_class" in orig_key:
								__a    							=					orig_key.replace('''mlm.mlm_class'''		,					'''cls.predictions.decoder'''						)
				if "mlm" in orig_key:
								__a    							=					orig_key.replace('''mlm'''		,					'''cls.predictions.transform'''						)
				if "backbone.backbone.encoders" in orig_key:
								__a    							=					orig_key.replace('''backbone.backbone.encoders'''		,					'''encoder.layer'''						)
				if "cls" not in orig_key:
								__a    							=					'''mra.''' + orig_key

				return orig_key


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Any:
				for key in orig_state_dict.copy().keys():
								__a    							=					orig_state_dict.pop(a__						)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__a    							=					val

				__a    							=					orig_state_dict['''cls.predictions.decoder.bias''']
				__a    							=					torch.arange(a__						).expand((1, -1)						) + 2

				return orig_state_dict


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					torch.load(a__		,					map_location='''cpu'''						)['''model_state_dict''']
				__a    							=					MraConfig.from_json_file(a__						)
				__a    							=					MraForMaskedLM(a__						)

				__a    							=					convert_checkpoint_helper(config.max_position_embeddings		,					a__						)

				print(model.load_state_dict(a__						)						)
				model.eval()
				model.save_pretrained(a__						)

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					A     				=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					A     				=				parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

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


A  :			int     				=				logging.get_logger(__name__)

A  :			Dict     				=				{
    '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     __A(				a    ):
				snake_case_		     =					'''layoutlmv2'''




				def __init__(  self	,  _snake_case=30_522	,  _snake_case=768	,  _snake_case=12	,  _snake_case=12	,  _snake_case=3_072	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case=0	,  _snake_case=1_024	,  _snake_case=128	,  _snake_case=32	,  _snake_case=True	,  _snake_case=256	,  _snake_case=64	,  _snake_case=True	,  _snake_case=[7, 7, 256]	,  _snake_case=128	,  _snake_case=128	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								super().__init__(
								    vocab_size=_snake_case	,  hidden_size=_snake_case	,  num_hidden_layers=_snake_case	,  num_attention_heads=_snake_case	,  intermediate_size=_snake_case	,  hidden_act=_snake_case	,  hidden_dropout_prob=_snake_case	,  attention_probs_dropout_prob=_snake_case	,  max_position_embeddings=_snake_case	,  type_vocab_size=_snake_case	,  initializer_range=_snake_case	,  layer_norm_eps=_snake_case	,  pad_token_id=_snake_case	,  **_snake_case	,  )
								__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        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								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        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__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(_snake_case	,  _snake_case       )
												setattr(_snake_case	,  attributes[-1]	,  _snake_case       )

								return detectrona_config