






"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       inspect.getfile(accelerate.test_utils						)
					A__							 =       os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_script.py']						)
					A__							 =       os.path.sep.join(
					    mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_distributed_data_loop.py']						)
					A__							 =       os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_ops.py']						)


	@require_multi_gpu
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					print(f'''Found {torch.cuda.device_count()} devices.'''						)
					A__							 =       ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
					with patch_environment(omp_num_threads=1						):
									execute_subprocess_async(A_  ,					env=os.environ.copy()						)


	@require_multi_gpu
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					print(f'''Found {torch.cuda.device_count()} devices.'''						)
					A__							 =       ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
					print(f'''Command: {cmd}'''						)
					with patch_environment(omp_num_threads=1						):
									execute_subprocess_async(A_  ,					env=os.environ.copy()						)


	@require_multi_gpu
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
					with patch_environment(omp_num_threads=1						):
									execute_subprocess_async(A_  ,					env=os.environ.copy()						)






	@require_multi_gpu
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''						)
					A__							 =       ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
					with patch_environment(omp_num_threads=1  ,					cuda_visible_devices='0,1'						):
									execute_subprocess_async(A_  ,					env=os.environ.copy()						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						List[str]					   =       Accelerator()
					SCREAMING_SNAKE_CASE						:						Any					   =       (accelerator.state.process_index + 2, 10)
					SCREAMING_SNAKE_CASE						:						Any					   =       torch.randint(0, 10, shape).to(accelerator.device)

					SCREAMING_SNAKE_CASE						:						Tuple					   =       ''''''

					SCREAMING_SNAKE_CASE						:						Tuple					   =       accelerator.pad_across_processes(tensor)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
										error_msg += "Padding was not done with the right value (0)."

					SCREAMING_SNAKE_CASE						:						List[Any]					   =       accelerator.pad_across_processes(tensor, pad_first=True)
					if tensora.shape[0] != accelerator.state.num_processes + 1:
										error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
					SCREAMING_SNAKE_CASE						:						int					   =       accelerator.state.num_processes - accelerator.state.process_index - 1
					if not torch.equal(tensora[index:], tensor):
										error_msg += "Tensors have different values."
					if not torch.all(tensora[:index] == 0):
										error_msg += "Padding was not done with the right value (0)."

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



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

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(model.graph.initializer				)
				A__							 =       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

								A__							 =       inits[i].name
								A__							 =       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       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

				A__							 =       set()
				A__							 =       {}
				A__							 =       []

				A__							 =       0

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

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

																A__							 =       inits[j].data_type
																A__							 =       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: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

																A__							 =       inits[i].name
																A__							 =       inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCAmelCase__				)
																else:
																				A__							 =       [name_j]
																ind_to_replace.append((j, i)				)

				print('total reduced size: '       ,total_reduced_size / 1024 / 1024 / 1024       ,'GB'				)

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


