


'''simple docstring'''
from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map


def      a (     __a  )    ->      Dict[str, torch.Tensor]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					[]
					UpperCamelCase__  :str				   =					[]
					UpperCamelCase__  :List[Any]				   =					[]

					for rt in rc.restypes:
										UpperCamelCase__  :str				   =					rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
										restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]  )
										UpperCamelCase__  :int				   =					{name: i for i, name in enumerate(a__  )}
										restype_atomaa_to_atomaa_list.append(
										    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]  )

										restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]  )

					# Add dummy mapping for restype 'UNK'
					restype_atomaa_to_atomaa_list.append([0] * 14  )
					restype_atomaa_to_atomaa_list.append([0] * 37  )
					restype_atomaa_mask_list.append([0.0] * 14  )

					UpperCamelCase__  :Tuple				   =					torch.tensor(
					    a__					,   dtype=torch.intaa					,   device=protein['''aatype'''].device					,   )
					UpperCamelCase__  :Dict				   =					torch.tensor(
					    a__					,   dtype=torch.intaa					,   device=protein['''aatype'''].device					,   )
					UpperCamelCase__  :Tuple				   =					torch.tensor(
					    a__					,   dtype=torch.floataa					,   device=protein['''aatype'''].device					,   )
					UpperCamelCase__  :Tuple				   =					protein["""aatype"""].to(torch.long  )

					# create the mapping for (residx, atom14) --> atom37, i.e. an array
					# with shape (num_res, 14) containing the atom37 indices for this protein
					UpperCamelCase__  :Tuple				   =					restype_atomaa_to_atomaa[protein_aatype]
					UpperCamelCase__  :Tuple				   =					restype_atomaa_mask[protein_aatype]

					UpperCamelCase__  :int				   =					residx_atomaa_mask
					UpperCamelCase__  :List[Any]				   =					residx_atomaa_to_atomaa.long()

					# create the gather indices for mapping back
					UpperCamelCase__  :List[str]				   =					restype_atomaa_to_atomaa[protein_aatype]
					UpperCamelCase__  :Optional[Any]				   =					residx_atomaa_to_atomaa.long()

					# create the corresponding mask
					UpperCamelCase__  :int				   =					torch.zeros([21, 37]					,   dtype=torch.floataa					,   device=protein['''aatype'''].device  )
					for restype, restype_letter in enumerate(rc.restypes  ):
										UpperCamelCase__  :List[Any]				   =					rc.restype_atoa[restype_letter]
										UpperCamelCase__  :Optional[Any]				   =					rc.residue_atoms[restype_name]
										for atom_name in atom_names:
															UpperCamelCase__  :Optional[Any]				   =					rc.atom_order[atom_name]
															UpperCamelCase__  :Optional[Any]				   =					1

					UpperCamelCase__  :int				   =					restype_atomaa_mask[protein_aatype]
					UpperCamelCase__  :Tuple				   =					residx_atomaa_mask

					return protein







def      a (     __a  )    ->      Dict[str, np.ndarray]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					tree_map(lambda __a  : torch.tensor(a__					,   device=batch['''aatype'''].device  )					,   a__					,   np.ndarray  )
					UpperCamelCase__  :int				   =					tensor_tree_map(lambda __a  : np.array(a__  )					,   make_atomaa_masks(a__  )  )
					return out


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   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(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT 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_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





