
"""simple docstring"""




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


UpperCAmelCase__				:   Optional[Any]   							=			logging.get_logger(__name__)

UpperCAmelCase__				:   Tuple   							=			{
    '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_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						Tuple 	=       '''layoutlmv2'''



				def __init__(self       ,	SCREAMING_SNAKE_CASE__=3_05_22       ,	SCREAMING_SNAKE_CASE__=7_68       ,	SCREAMING_SNAKE_CASE__=12       ,	SCREAMING_SNAKE_CASE__=12       ,	SCREAMING_SNAKE_CASE__=30_72       ,	SCREAMING_SNAKE_CASE__="gelu"       ,	SCREAMING_SNAKE_CASE__=0.1       ,	SCREAMING_SNAKE_CASE__=0.1       ,	SCREAMING_SNAKE_CASE__=5_12       ,	SCREAMING_SNAKE_CASE__=2       ,	SCREAMING_SNAKE_CASE__=0.02       ,	SCREAMING_SNAKE_CASE__=1E-12       ,	SCREAMING_SNAKE_CASE__=0       ,	SCREAMING_SNAKE_CASE__=10_24       ,	SCREAMING_SNAKE_CASE__=1_28       ,	SCREAMING_SNAKE_CASE__=32       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=2_56       ,	SCREAMING_SNAKE_CASE__=64       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=[7, 7, 2_56]       ,	SCREAMING_SNAKE_CASE__=1_28       ,	SCREAMING_SNAKE_CASE__=1_28       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=False       ,	SCREAMING_SNAKE_CASE__=None       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		List[str]:



											"""simple docstring"""




											super().__init__(
											    vocab_size=SCREAMING_SNAKE_CASE__       ,	hidden_size=SCREAMING_SNAKE_CASE__       ,	num_hidden_layers=SCREAMING_SNAKE_CASE__       ,	num_attention_heads=SCREAMING_SNAKE_CASE__       ,	intermediate_size=SCREAMING_SNAKE_CASE__       ,	hidden_act=SCREAMING_SNAKE_CASE__       ,	hidden_dropout_prob=SCREAMING_SNAKE_CASE__       ,	attention_probs_dropout_prob=SCREAMING_SNAKE_CASE__       ,	max_position_embeddings=SCREAMING_SNAKE_CASE__       ,	type_vocab_size=SCREAMING_SNAKE_CASE__       ,	initializer_range=SCREAMING_SNAKE_CASE__       ,	layer_norm_eps=SCREAMING_SNAKE_CASE__       ,	pad_token_id=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       max_ad_position_embeddings
											SCREAMING_SNAKE_CASE__						:	Dict									=       max_rel_pos
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       rel_pos_bins
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       fast_qkv
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       max_rel_ad_pos
											SCREAMING_SNAKE_CASE__						:	Dict									=       rel_ad_pos_bins
											SCREAMING_SNAKE_CASE__						:	Tuple									=       convert_sync_batchnorm
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       image_feature_pool_shape
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       coordinate_size
											SCREAMING_SNAKE_CASE__						:	List[str]									=       shape_size
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       has_relative_attention_bias
											SCREAMING_SNAKE_CASE__						:	int									=       has_spatial_attention_bias
											SCREAMING_SNAKE_CASE__						:	str									=       has_visual_segment_embedding
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       (
											    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
											)



				@classmethod
				def      __magic_name__   (self   ) ->		Union[str, Any]:



											"""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], [1_28], [2_56], [5_12]],
											    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
											    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
											    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
											    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
											    "MODEL.POST_NMS_TOPK_TEST": 10_00,
											    "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": 1_01,
											    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
											    "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      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       detectrona.config.get_cfg()
											for k, v in self.detectrona_config_args.items():
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       k.split("""."""   )
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       detectrona_config
																		for attribute in attributes[:-1]:
																									SCREAMING_SNAKE_CASE__						:	str									=       getattr(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																		setattr(SCREAMING_SNAKE_CASE__       ,	attributes[-1]       ,	SCREAMING_SNAKE_CASE__   )

											return detectrona_config






"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

							# Encoder Layers
							for layer in range(config.num_hidden_layers						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--fnet_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained FNet model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




