



"""simple docstring"""
import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
			import torch.distributed.checkpoint as dist_cp
			from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
			from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
			from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
			from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							List[str]						,   lowercase_     :							str						,   lowercase_     :							Optional[int]						,   lowercase_     :							Tuple=0							)	->       Any:
				os.makedirs(lowercase_						,   exist_ok=lowercase_							)
				with FSDP.state_dict_type(
				    lowercase_						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config							):
								_lowerCamelCase        =				model.state_dict()
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												_lowerCamelCase        =				F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
												_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
												if accelerator.process_index == 0:
																logger.info(F"""Saving model to {output_model_file}"""							)
																torch.save(lowercase_						,   lowercase_							)
																logger.info(F"""Model saved to {output_model_file}"""							)
								elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
												_lowerCamelCase        =				(
												    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
												    if model_index == 0
												    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
												)
												_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
												logger.info(F"""Saving model to {output_model_file}"""							)
												torch.save(lowercase_						,   lowercase_							)
												logger.info(F"""Model saved to {output_model_file}"""							)
								elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
												_lowerCamelCase        =				os.path.join(lowercase_						,   F"""{MODEL_NAME}_{model_index}"""							)
												os.makedirs(lowercase_						,   exist_ok=lowercase_							)
												logger.info(F"""Saving model to {ckpt_dir}"""							)
												_lowerCamelCase        =				{'''model''': state_dict}

												dist_cp.save_state_dict(
												    state_dict=lowercase_						,   storage_writer=dist_cp.FileSystemWriter(lowercase_							)						,   planner=DefaultSavePlanner()						,   )
												logger.info(F"""Model saved to {ckpt_dir}"""							)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							str						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Any						,   lowercase_     :							Any=0							)	->       str:
				accelerator.wait_for_everyone()
				with FSDP.state_dict_type(
				    lowercase_						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config							):
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												if type(lowercase_							) != FSDP and accelerator.process_index != 0:
																if not fsdp_plugin.sync_module_states:
																				raise ValueError(
																				    '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when '''
																				    '''initializing FSDP object'''							)
																return
												_lowerCamelCase        =				F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
												_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
												logger.info(F"""Loading model from {input_model_file}"""							)
												_lowerCamelCase        =				torch.load(lowercase_							)
												logger.info(F"""Model loaded from {input_model_file}"""							)
								elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
												_lowerCamelCase        =				(
												    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
												    if model_index == 0
												    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
												)
												_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
												logger.info(F"""Loading model from {input_model_file}"""							)
												_lowerCamelCase        =				torch.load(lowercase_							)
												logger.info(F"""Model loaded from {input_model_file}"""							)
								elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
												_lowerCamelCase        =				(
												    os.path.join(lowercase_						,   F"""{MODEL_NAME}_{model_index}"""							)
												    if F"""{MODEL_NAME}""" not in input_dir
												    else input_dir
												)
												logger.info(F"""Loading model from {ckpt_dir}"""							)
												_lowerCamelCase        =				{'''model''': model.state_dict()}
												dist_cp.load_state_dict(
												    state_dict=lowercase_						,   storage_reader=dist_cp.FileSystemReader(lowercase_							)						,   planner=DefaultLoadPlanner()						,   )
												_lowerCamelCase        =				state_dict['''model''']
												logger.info(F"""Model loaded from {ckpt_dir}"""							)
								model.load_state_dict(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							Optional[int]						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							Optional[int]						,   lowercase_     :							str						,   lowercase_     :							Dict=0							)	->       Tuple:
				os.makedirs(lowercase_						,   exist_ok=lowercase_							)
				with FSDP.state_dict_type(
				    lowercase_						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config							):
								_lowerCamelCase        =				FSDP.optim_state_dict(lowercase_						,   lowercase_							)
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												if accelerator.process_index == 0:
																_lowerCamelCase        =				(
																    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																)
																_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
																logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""							)
																torch.save(lowercase_						,   lowercase_							)
																logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
								else:
												_lowerCamelCase        =				os.path.join(lowercase_						,   F"""{OPTIMIZER_NAME}_{optimizer_index}"""							)
												os.makedirs(lowercase_						,   exist_ok=lowercase_							)
												logger.info(F"""Saving Optimizer state to {ckpt_dir}"""							)
												dist_cp.save_state_dict(
												    state_dict={'''optimizer''': optim_state}						,   storage_writer=dist_cp.FileSystemWriter(lowercase_							)						,   planner=DefaultSavePlanner()						,   )
												logger.info(F"""Optimizer state saved in {ckpt_dir}"""							)




def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Optional[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Dict						,   lowercase_     :							Any						,   lowercase_     :							Union[str, Any]=0							)	->       Tuple:
				accelerator.wait_for_everyone()
				with FSDP.state_dict_type(
				    lowercase_						,   fsdp_plugin.state_dict_type						,   fsdp_plugin.state_dict_config						,   fsdp_plugin.optim_state_dict_config							):
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												_lowerCamelCase        =				None
												# below check should work but currently it isn't working (mostly opytorch issue),
												# in the meantime disabling it at the cost of excess memory usage
												# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
												_lowerCamelCase        =				(
												    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
												)
												_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
												logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""							)
												_lowerCamelCase        =				torch.load(lowercase_							)
												logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""							)
								else:
												_lowerCamelCase        =				(
												    os.path.join(lowercase_						,   F"""{OPTIMIZER_NAME}_{optimizer_index}"""							)
												    if F"""{OPTIMIZER_NAME}""" not in input_dir
												    else input_dir
												)
												logger.info(F"""Loading Optimizer from {ckpt_dir}"""							)
												_lowerCamelCase        =				load_sharded_optimizer_state_dict(
												    model_state_dict=model.state_dict()						,   optimizer_key='''optimizer'''						,   storage_reader=dist_cp.FileSystemReader(lowercase_							)						,   )
												_lowerCamelCase        =				optim_state['''optimizer''']
												logger.info(F"""Optimizer loaded from {ckpt_dir}"""							)
								_lowerCamelCase        =				FSDP.optim_state_dict_to_load(lowercase_						,   lowercase_						,   lowercase_							)
								optimizer.load_state_dict(lowercase_							)





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
