




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


__A								=       get_logger(__name__)







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: List[Any]					,	UpperCamelCase__							: List[str]					,	UpperCamelCase__							: str=0 )	->			str:





				"""simple docstring"""


				os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
				with FSDP.state_dict_type(
				    UpperCamelCase__					,	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(UpperCamelCase__					,	UpperCamelCase__ )
												if accelerator.process_index == 0:
																logger.info(F"""Saving model to {output_model_file}""" )
																torch.save(UpperCamelCase__					,	UpperCamelCase__ )
																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(UpperCamelCase__					,	UpperCamelCase__ )
												logger.info(F"""Saving model to {output_model_file}""" )
												torch.save(UpperCamelCase__					,	UpperCamelCase__ )
												logger.info(F"""Model saved to {output_model_file}""" )
								elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
												__lowerCamelCase									=       os.path.join(UpperCamelCase__					,	F"""{MODEL_NAME}_{model_index}""" )
												os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
												logger.info(F"""Saving model to {ckpt_dir}""" )
												__lowerCamelCase									=       {'model': state_dict}

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







def      lowerCamelCase_						(			UpperCamelCase__							: List[str]					,	UpperCamelCase__							: List[Any]					,	UpperCamelCase__							: int					,	UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: List[Any]=0 )	->			List[Any]:





				"""simple docstring"""


				accelerator.wait_for_everyone()
				with FSDP.state_dict_type(
				    UpperCamelCase__					,	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(UpperCamelCase__ ) != 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(UpperCamelCase__					,	UpperCamelCase__ )
												logger.info(F"""Loading model from {input_model_file}""" )
												__lowerCamelCase									=       torch.load(UpperCamelCase__ )
												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(UpperCamelCase__					,	UpperCamelCase__ )
												logger.info(F"""Loading model from {input_model_file}""" )
												__lowerCamelCase									=       torch.load(UpperCamelCase__ )
												logger.info(F"""Model loaded from {input_model_file}""" )
								elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
												__lowerCamelCase									=       (
												    os.path.join(UpperCamelCase__					,	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=UpperCamelCase__					,	storage_reader=dist_cp.FileSystemReader(UpperCamelCase__ )					,	planner=DefaultLoadPlanner()					,	)
												__lowerCamelCase									=       state_dict['model']
												logger.info(F"""Model loaded from {ckpt_dir}""" )
								model.load_state_dict(UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: str					,	UpperCamelCase__							: int					,	UpperCamelCase__							: List[str]					,	UpperCamelCase__							: List[Any]=0 )	->			List[Any]:





				"""simple docstring"""


				os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
				with FSDP.state_dict_type(
				    UpperCamelCase__					,	fsdp_plugin.state_dict_type					,	fsdp_plugin.state_dict_config					,	fsdp_plugin.optim_state_dict_config ):
								__lowerCamelCase									=       FSDP.optim_state_dict(UpperCamelCase__					,	UpperCamelCase__ )
								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(UpperCamelCase__					,	UpperCamelCase__ )
																logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
																torch.save(UpperCamelCase__					,	UpperCamelCase__ )
																logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
								else:
												__lowerCamelCase									=       os.path.join(UpperCamelCase__					,	F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
												os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
												logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
												dist_cp.save_state_dict(
												    state_dict={'optimizer': optim_state}					,	storage_writer=dist_cp.FileSystemWriter(UpperCamelCase__ )					,	planner=DefaultSavePlanner()					,	)
												logger.info(F"""Optimizer state saved in {ckpt_dir}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: int					,	UpperCamelCase__							: Any					,	UpperCamelCase__							: Any=0 )	->			Any:





				"""simple docstring"""


				accelerator.wait_for_everyone()
				with FSDP.state_dict_type(
				    UpperCamelCase__					,	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(UpperCamelCase__					,	UpperCamelCase__ )
												logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
												__lowerCamelCase									=       torch.load(UpperCamelCase__ )
												logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
								else:
												__lowerCamelCase									=       (
												    os.path.join(UpperCamelCase__					,	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(UpperCamelCase__ )					,	)
												__lowerCamelCase									=       optim_state['optimizer']
												logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
								__lowerCamelCase									=       FSDP.optim_state_dict_to_load(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )
								optimizer.load_state_dict(UpperCamelCase__ )








from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


