

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 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					int       =	inspect.getfile(accelerate.test_utils    )
						A__							:					str       =	os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_script.py"""]    )
						A__							:					List[Any]       =	os.path.sep.join(
						    mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]    )
						A__							:					Dict       =	os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_ops.py"""]    )






					@require_multi_gpu
					def 						__A    (				self    ):
						print(F"""Found {torch.cuda.device_count()} devices."""    )
						A__							:					Optional[int]       =	["""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 						__A    (				self    ):
						print(F"""Found {torch.cuda.device_count()} devices."""    )
						A__							:					Any       =	["""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 						__A    (				self    ):
						A__							:					List[Any]       =	["""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 						__A    (				self    ):
						print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""    )
						A__							:					Optional[int]       =	["""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__":
				A_ :				Optional[Any]						 =    Accelerator()
				A_ :				Tuple						 =    (accelerator.state.process_index + 2, 10)
				A_ :				Any						 =    torch.randint(0, 10, shape).to(accelerator.device)

				A_ :				Any						 =    ''

				A_ :				List[str]						 =    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)."

				A_ :				Tuple						 =    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."
				A_ :				List[str]						 =    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)






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



