





import logging
import os

from .state import PartialState


class   A      (logging.LoggerAdapter   ):
			'''simple docstring'''




			@staticmethod
			def        a_	(      __lowerCAmelCase       :   Tuple       )     ->     Dict:







					"""simple docstring"""






					A__  				=       PartialState()
					return not main_process_only or (main_process_only and state.is_main_process)







			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   Optional[Any]							,     *__lowerCAmelCase       :   Tuple							,     **__lowerCAmelCase       :   Any       )     ->     Any:







					"""simple docstring"""






					if PartialState._shared_state == {}:
							raise RuntimeError(
							    """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""       )
					A__  				=       kwargs.pop("""main_process_only"""							,     __lowerCAmelCase       )
					A__  				=       kwargs.pop("""in_order"""							,     __lowerCAmelCase       )

					if self.isEnabledFor(__lowerCAmelCase       ):
							if self._should_log(__lowerCAmelCase       ):
									A__				,      A__  				=       self.process(__lowerCAmelCase							,     __lowerCAmelCase       )
									self.logger.log(__lowerCAmelCase							,     __lowerCAmelCase							,     *__lowerCAmelCase							,     **__lowerCAmelCase       )

							elif in_order:
									A__  				=       PartialState()
									for i in range(state.num_processes       ):
											if i == state.process_index:
													A__				,      A__  				=       self.process(__lowerCAmelCase							,     __lowerCAmelCase       )
													self.logger.log(__lowerCAmelCase							,     __lowerCAmelCase							,     *__lowerCAmelCase							,     **__lowerCAmelCase       )
											state.wait_for_everyone()







def        __lowerCamelCase (							__a       :str   ,					__a       :str = None   )		->    int:






		"""simple docstring"""

		if log_level is None:
				A__  				=       os.environ.get("""ACCELERATE_LOG_LEVEL"""   ,					__a   )
		A__  				=       logging.getLogger(__a   )
		if log_level is not None:
				logger.setLevel(log_level.upper()   )
				logger.root.setLevel(log_level.upper()   )
		return MultiProcessAdapter(__a   ,					{}   )










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def        __lowerCamelCase (							__a       :List[str]   )		->    Tuple:






		"""simple docstring"""

		return (torch.arange(state.num_processes   ) + 1.0 + (state.num_processes * state.process_index)).to(state.device   )







def        __lowerCamelCase (							__a       :int   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       gather(__a   )
		assert gathered_tensor.tolist() == list(range(1   ,					state.num_processes**2 + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		A__  				=       [state.process_index]
		A__  				=       gather_object(__a   )
		assert len(__a   ) == state.num_processes, F'{gathered_obj}, {len(__a   )} != {state.num_processes}'
		assert gathered_obj == list(range(state.num_processes   )   ), F'{gathered_obj} != {list(range(state.num_processes   )   )}'







def        __lowerCamelCase (							__a       :List[str]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       broadcast(__a   )
		assert broadcasted_tensor.shape == torch.Size([state.num_processes]   )
		assert broadcasted_tensor.tolist() == list(range(1   ,					state.num_processes + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		if state.is_main_process:
				A__  				=       torch.arange(state.num_processes + 1   ).to(state.device   )
		else:
				A__  				=       torch.arange(state.num_processes   ).to(state.device   )
		A__  				=       pad_across_processes(__a   )
		assert padded_tensor.shape == torch.Size([state.num_processes + 1]   )
		if not state.is_main_process:
				assert padded_tensor.tolist() == list(range(0   ,					state.num_processes   )   ) + [0]







def        __lowerCamelCase (							__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""sum"""   )
		A__  				=       torch.tensor([4.0, 6]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""mean"""   )
		A__  				=       torch.tensor([2.0, 3]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		main()







def        __lowerCamelCase (							)		->    str:






		"""simple docstring"""

		A__  				=       PartialState()
		state.print(F'State: {state}'   )
		state.print("""testing gather"""   )
		test_gather(__a   )
		state.print("""testing gather_object"""   )
		test_gather_object(__a   )
		state.print("""testing broadcast"""   )
		test_broadcast(__a   )
		state.print("""testing pad_across_processes"""   )
		test_pad_across_processes(__a   )
		state.print("""testing reduce_sum"""   )
		test_reduce_sum(__a   )
		state.print("""testing reduce_mean"""   )
		test_reduce_mean(__a   )


if __name__ == "__main__":
					main()



