

import operator as op


UpperCAmelCase_							:					Union[str, Any]		 =   '''scaler.pt'''
UpperCAmelCase_							:					int		 =   '''pytorch_model'''
UpperCAmelCase_							:					Optional[Any]		 =   '''random_states'''
UpperCAmelCase_							:					Dict		 =   '''optimizer'''
UpperCAmelCase_							:					Dict		 =   '''scheduler'''
UpperCAmelCase_							:					int		 =   '''pytorch_model.bin'''
UpperCAmelCase_							:					List[Any]		 =   '''pytorch_model.bin.index.json'''
UpperCAmelCase_							:					Optional[Any]		 =   '''model.safetensors'''
UpperCAmelCase_							:					str		 =   '''model.safetensors.index.json'''
UpperCAmelCase_							:					Tuple		 =   '''1.10.2'''
UpperCAmelCase_							:					str		 =   '''py38'''
UpperCAmelCase_							:					List[str]		 =   '''4.17.0'''
UpperCAmelCase_							:					Any		 =   ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
UpperCAmelCase_							:					List[Any]		 =   ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
UpperCAmelCase_							:					Optional[int]		 =   ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
UpperCAmelCase_							:					Tuple		 =   ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
UpperCAmelCase_							:					Union[str, Any]		 =   ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
UpperCAmelCase_							:					int		 =   '''2.0.1'''
UpperCAmelCase_							:					List[Any]		 =   ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
UpperCAmelCase_							:					List[Any]		 =   ['''default''', '''reduce-overhead''', '''max-autotune''']

UpperCAmelCase_							:					Dict		 =   {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
UpperCAmelCase_							:					Tuple		 =   [
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

UpperCAmelCase_							:					Tuple		 =   ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
UpperCAmelCase_							:					Any		 =   ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']








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)





