


'''simple docstring'''




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)












'''simple docstring'''




import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=None     ,__magic_name__="no"     ,__magic_name__="29500"						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					str   =       False
			snake_case_				:					int   =       False
			if any(key.startswith("KAGGLE"						) for key in os.environ.keys()						):
						snake_case_				:					Any   =       True
			elif "IPython" in sys.modules:
						snake_case_				:					Union[str, Any]   =       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

			try:
						snake_case_				:					Any   =       PrecisionType(mixed_precision.lower()						)
			except ValueError:
						raise ValueError(
						    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''						)

			if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"     ,__magic_name__						) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state						) > 0:
									raise ValueError(
									    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
									    "your training function. Restart your notebook and make sure no cells initializes an "
									    "`Accelerator`."						)
						if num_processes is None:
									snake_case_				:					Tuple   =       8

						snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="TPU"						)
						print(F'''Launching a training on {num_processes} TPU cores.'''						)
						xmp.spawn(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("Launching training on one GPU."						)
						else:
									print("Launching training on one CPU."						)
						function(*__magic_name__						)
			else:
						if num_processes is None:
									raise ValueError(
									    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."						)

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state						) > 0:
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
												    "inside your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."						)

									if torch.cuda.is_initialized():
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
												    "function."						)

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port=__magic_name__     ,mixed_precision=__magic_name__						):
												snake_case_				:					Optional[int]   =       PrepareForLaunch(__magic_name__     ,distributed_type="MULTI_GPU"						)
												print(F'''Launching training on {num_processes} GPUs.'''						)
												try:
															start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																		    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																		    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																		    "which one is problematic."						) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												snake_case_				:					Any   =       "1"
												print("Launching training on MPS."						)
									elif torch.cuda.is_available():
												print("Launching training on one GPU."						)
									else:
												print("Launching training on CPU."						)
									function(*__magic_name__						)






def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=()     ,__magic_name__=2						)->    Dict:



			"""simple docstring"""



			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=__magic_name__     ,master_addr="127.0.01"     ,master_port="29500"     ,accelerate_mixed_precision="no"     ,accelerate_debug_rdv_file=tmp_file.name     ,accelerate_use_cpu="yes"     ,):
									snake_case_				:					Any   =       PrepareForLaunch(__magic_name__     ,debug=__magic_name__						)
									start_processes(__magic_name__     ,args=__magic_name__     ,nprocs=__magic_name__     ,start_method="fork"						)




