

import os

import numpy
import onnx







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       a.name
						SCREAMING_SNAKE_CASE_: int  		=       b.name

						SCREAMING_SNAKE_CASE_: Any  		=       ""
						SCREAMING_SNAKE_CASE_: Tuple  		=       ""

						SCREAMING_SNAKE_CASE_: List[Any]  		=       a == b

						SCREAMING_SNAKE_CASE_: Any  		=       name_a
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       name_b

						return res







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for i, input_name in enumerate(node_proto.input    ):
												if input_name == name:
																		node_proto.input.insert(_UpperCAmelCase				,	_UpperCAmelCase    )
																		node_proto.input.pop(i + 1    )

						if node_proto.op_type == "If":
												_graph_replace_input_with(node_proto.attribute[0].g				,	_UpperCAmelCase				,	_UpperCAmelCase    )
												_graph_replace_input_with(node_proto.attribute[1].g				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						if node_proto.op_type == "Loop":
												_graph_replace_input_with(node_proto.attribute[0].g				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for n in graph_proto.node:
												_node_replace_input_with(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       list(model.graph.initializer    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       list(model_without_ext.graph.initializer    )
						for i, ref_i in ind_to_replace:
												assert inits_with_data[i].name == inits[i].name
												assert inits_with_data[ref_i].name == inits[ref_i].name
												assert i > ref_i

												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       inits[i].name
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       inits[ref_i].name

												model_without_ext.graph.initializer.remove(inits[i]    )

												# for n in model.graph.node:
												_graph_replace_input_with(model_without_ext.graph				,	_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.dirname(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Any  		=       os.path.basename(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: int  		=       onnx.load(os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )    )

						SCREAMING_SNAKE_CASE_: Any  		=       list(model.graph.initializer    )

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       set()
						SCREAMING_SNAKE_CASE_: Any  		=       {}
						SCREAMING_SNAKE_CASE_: Any  		=       []

						SCREAMING_SNAKE_CASE_: int  		=       0

						for i in range(len(_UpperCAmelCase    )    ):
												if i in dup_set:
																		continue

												for j in range(i + 1				,	len(_UpperCAmelCase    )    ):
																		if j in dup_set:
																								continue
																		if _is_equal_tensor_proto(inits[i]				,	inits[j]    ):
																								dup_set.add(_UpperCAmelCase    )
																								dup_set.add(_UpperCAmelCase    )

																								SCREAMING_SNAKE_CASE_: List[str]  		=       inits[j].data_type
																								SCREAMING_SNAKE_CASE_: Any  		=       numpy.prod(inits[j].dims    )
																								if dtype == 1:
																														mem_size *= 4
																								elif dtype == 6:
																														mem_size *= 4
																								elif dtype == 7 or dtype == 11:
																														mem_size *= 8
																								else:
																														print("unexpected data type: "				,	_UpperCAmelCase    )
																								total_reduced_size += mem_size

																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[i].name
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       inits[j].name

																								if name_i in dup_map:
																														dup_map[name_i].append(_UpperCAmelCase    )
																								else:
																														SCREAMING_SNAKE_CASE_: List[Any]  		=       [name_j]
																								ind_to_replace.append((j, i)    )

						print("total reduced size: "				,	total_reduced_size / 10_24 / 10_24 / 10_24				,	"GB"    )

						SCREAMING_SNAKE_CASE_: List[Any]  		=       sorted(_UpperCAmelCase    )
						_remove_dup_initializers_from_model(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "optimized_" + model_file_name
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )
						onnx.save(_UpperCAmelCase				,	_UpperCAmelCase    )

						return new_model








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )					)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""",     type=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ),     )

  # rest from the training program
  parser.add_argument("""training_script_args""",     nargs=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

  # Import training_script as a module.
  __magic_name__				     =       Path(args.training_script					)
  sys.path.append(str(script_fpath.parent.resolve()					)					)
  __magic_name__				     =       script_fpath.stem
  __magic_name__				     =       importlib.import_module(A_					)

  # Patch sys.argv
  __magic_name__				     =       [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores					)]

  xmp.spawn(mod._mp_fn,     args=(),     nprocs=args.num_cores					)


if __name__ == "__main__":
    main()

