"""simple docstring"""



import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							# Initialise PyTorch model
							A__       :     List[Any]								=			BertConfig.from_json_file(lowerCAmelCase							)
							print(F'''Building PyTorch model from configuration: {config}'''							)
							A__       :     Optional[int]								=			BertForPreTraining(lowerCAmelCase							)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
							torch.save(model.state_dict()			,  lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--bert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained BERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				_UpperCamelCase								=		parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





