
"""simple docstring"""


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def    _snake_case					(		snake_case__ :  Tuple  ):
	return EnvironmentCommand()






def    _snake_case					(		snake_case__ :  List[str]  ):
	return EnvironmentCommand(args.accelerate_config_file  )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   ArgumentParser   )  -> Any:
							A     = parser.add_parser('env'   )
							download_parser.set_defaults(func=A_   )
							download_parser.add_argument(
							    '--accelerate-config_file'	,default=A_	,help='The accelerate config file to use for the default values in the launching script.'	,)
							download_parser.set_defaults(func=A_   )


						def __init__(			self  :   List[Any]	,A_  :   Optional[int]	,*A_  :   Tuple   )  -> None:
							A     = accelerate_config_file


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Union[str, Any]:
							A     = 'not installed'
							if is_safetensors_available():
								import safetensors

								A     = safetensors.__version__
							elif importlib.util.find_spec('safetensors'   ) is not None:
								import safetensors

								A     = F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

							A     = 'not installed'
							A     = A     = 'not found'
							if is_accelerate_available():
								import accelerate
								from accelerate.commands.config import default_config_file, load_config_from_file

								A     = accelerate.__version__
								# Get the default from the config file.
								if self._accelerate_config_file is not None or os.path.isfile(A_   ):
									A     = load_config_from_file(self._accelerate_config_file   ).to_dict()

								A     = (
								    '\n'.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]   )
								    if isinstance(A_	,A_   )
								    else F'\t{accelerate_config}'
								)

							A     = 'not installed'
							A     = 'NA'
							if is_torch_available():
								import torch

								A     = torch.__version__
								A     = torch.cuda.is_available()

							A     = 'not installed'
							A     = 'NA'
							if is_tf_available():
								import tensorflow as tf

								A     = tf.__version__
								try:
									# deprecated in v2.1
									A     = tf.test.is_gpu_available()
								except AttributeError:
									# returns list of devices, convert to bool
									A     = bool(tf.config.list_physical_devices('GPU'   )   )

							A     = 'not installed'
							A     = 'not installed'
							A     = 'not installed'
							A     = 'NA'
							if is_flax_available():
								import flax
								import jax
								import jaxlib

								A     = flax.__version__
								A     = jax.__version__
								A     = jaxlib.__version__
								A     = jax.lib.xla_bridge.get_backend().platform

							A     = {
							    '`transformers` version': version,
							    'Platform': platform.platform(),
							    'Python version': platform.python_version(),
							    'Huggingface_hub version': huggingface_hub.__version__,
							    'Safetensors version': F'{safetensors_version}',
							    'Accelerate version': F'{accelerate_version}',
							    'Accelerate config': F'{accelerate_config_str}',
							    'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
							    'Tensorflow version (GPU?)': F'{tf_version} ({tf_cuda_available})',
							    'Flax version (CPU?/GPU?/TPU?)': F'{flax_version} ({jax_backend})',
							    'Jax version': F'{jax_version}',
							    'JaxLib version': F'{jaxlib_version}',
							    'Using GPU in script?': '<fill in>',
							    'Using distributed or parallel set-up in script?': '<fill in>',
							}

							print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'   )
							print(self.format_dict(A_   )   )

							return info




						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   Union[str, Any]   )  -> Dict:
							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]   ) + "\n"

"""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


_lowercase				 =					get_logger(__name__)






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

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






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  List[str]						,   snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  Any=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   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(snake_case__  ) != 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
			A     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A     = (
			    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
			    if model_index == 0
			    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading model from {input_model_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Model loaded from {input_model_file}'  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A     = (
			    os.path.join(snake_case__						,   F'{MODEL_NAME}_{model_index}'  )
			    if F'{MODEL_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading model from {ckpt_dir}'  )
			A     = {'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   planner=DefaultLoadPlanner()						,   )
			A     = state_dict['model']
			logger.info(F'Model loaded from {ckpt_dir}'  )
		model.load_state_dict(snake_case__  )






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






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  Optional[int]						,   snake_case__ :  List[str]						,   snake_case__ :  List[str]						,   snake_case__ :  int						,   snake_case__ :  Optional[int]=0  ):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__						,   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:
			A     = 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:
			A     = (
			    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
			)
			A     = os.path.join(snake_case__						,   snake_case__  )
			logger.info(F'Loading Optimizer state from {input_optimizer_file}'  )
			A     = torch.load(snake_case__  )
			logger.info(F'Optimizer state loaded from {input_optimizer_file}'  )
		else:
			A     = (
			    os.path.join(snake_case__						,   F'{OPTIMIZER_NAME}_{optimizer_index}'  )
			    if F'{OPTIMIZER_NAME}' not in input_dir
			    else input_dir
			)
			logger.info(F'Loading Optimizer from {ckpt_dir}'  )
			A     = load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()						,   optimizer_key='optimizer'						,   storage_reader=dist_cp.FileSystemReader(snake_case__  )						,   )
			A     = optim_state['optimizer']
			logger.info(F'Optimizer loaded from {ckpt_dir}'  )
		A     = FSDP.optim_state_dict_to_load(snake_case__						,   snake_case__						,   snake_case__  )
		optimizer.load_state_dict(snake_case__  )