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							:      Union[str, Any]      							=get_logger(__name__)



def A__  (			lowercase:     int,		lowercase:     str,		lowercase:     Union[str, Any],		lowercase:     Optional[int],		lowercase:     Optional[int]=0					)				->		Optional[int]:
       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					):
              A						:     List[Any]         =model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     A						:     Optional[int]         =F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     A						:     Union[str, Any]         =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:
                     A						:     Tuple         =(
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     A						:     Dict         =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:
                     A						:     List[str]         =os.path.join(lowercase,		F'{MODEL_NAME}_{model_index}'					)
                     os.makedirs(lowercase,		exist_ok=lowercase					)
                     logger.info(F'Saving model to {ckpt_dir}'					)
                     A						:     Optional[int]         ={'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 A__  (			lowercase:     Optional[int],		lowercase:     Dict,		lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any=0					)				->		List[Any]:
       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
                     A						:     List[str]         =F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     A						:     Dict         =os.path.join(lowercase,		lowercase					)
                     logger.info(F'Loading model from {input_model_file}'					)
                     A						:     Optional[Any]         =torch.load(lowercase					)
                     logger.info(F'Model loaded from {input_model_file}'					)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     A						:     Dict         =(
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     A						:     Optional[Any]         =os.path.join(lowercase,		lowercase					)
                     logger.info(F'Loading model from {input_model_file}'					)
                     A						:     int         =torch.load(lowercase					)
                     logger.info(F'Model loaded from {input_model_file}'					)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     A						:     List[str]         =(
                         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}'					)
                     A						:     Optional[Any]         ={'model': model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=lowercase,		storage_reader=dist_cp.FileSystemReader(lowercase					),		planner=DefaultLoadPlanner(),		)
                     A						:     Optional[Any]         =state_dict['model']
                     logger.info(F'Model loaded from {ckpt_dir}'					)
              model.load_state_dict(lowercase					)



def A__  (			lowercase:     Any,		lowercase:     str,		lowercase:     str,		lowercase:     Optional[Any],		lowercase:     int,		lowercase:     List[str]=0					)				->		Optional[int]:
       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					):
              A						:     Union[str, Any]         =FSDP.optim_state_dict(lowercase,		lowercase					)
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            A						:     int         =(
                                F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            A						:     Union[str, Any]         =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:
                     A						:     int         =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 A__  (			lowercase:     Tuple,		lowercase:     int,		lowercase:     int,		lowercase:     Optional[int],		lowercase:     Union[str, Any],		lowercase:     Optional[Any]=0					)				->		int:
       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:
                     A						:     Any         =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						:     Optional[Any]         =(
                         F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     A						:     Union[str, Any]         =os.path.join(lowercase,		lowercase					)
                     logger.info(F'Loading Optimizer state from {input_optimizer_file}'					)
                     A						:     Tuple         =torch.load(lowercase					)
                     logger.info(F'Optimizer state loaded from {input_optimizer_file}'					)
              else:
                     A						:     Optional[Any]         =(
                         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}'					)
                     A						:     Tuple         =load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict(),		optimizer_key='optimizer',		storage_reader=dist_cp.FileSystemReader(lowercase					),		)
                     A						:     Optional[Any]         =optim_state['optimizer']
                     logger.info(F'Optimizer loaded from {ckpt_dir}'					)
              A						:     Tuple         =FSDP.optim_state_dict_to_load(lowercase,		lowercase,		lowercase					)
              optimizer.load_state_dict(lowercase					)







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,	)
         train_parser.add_argument(
             '--column_label'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=2      ,	help='Column of the dataset csv file with example ids.'     )
         train_parser.add_argument(
             '--skip_first_row'      ,	action='store_true'      ,	help='Skip the first row of the csv file (headers).'     )

         train_parser.add_argument('--validation_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0.1      ,	help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'      ,	)

         train_parser.add_argument('--output'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

         train_parser.add_argument(
             '--task'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='text_classification'      ,	help='Task to train the model on.'     )
         train_parser.add_argument(
             '--model'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='bert-base-uncased'      ,	help='Model\'s name or path to stored model.'     )
         train_parser.add_argument('--train_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=32      ,	help='Batch size for training.'     )
         train_parser.add_argument('--valid_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=64      ,	help='Batch size for validation.'     )
         train_parser.add_argument('--learning_rate'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=3e-5      ,	help='Learning rate.'     )
         train_parser.add_argument('--adam_epsilon'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1e-08      ,	help='Epsilon for Adam optimizer.'     )
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =TextClassificationPipeline.from_pretrained(args.model     )
         elif args.task == "token_classification":
                raise NotImplementedError
         elif args.task == "question_answering":
                raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'     )
         A						:     Tuple         =Processor.create_from_csv(
             args.train_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)
         A						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =Processor.create_from_csv(
                    args.validation_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)

         A						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         raise NotImplementedError







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         self.pipeline.fit(
             self.train_dataset      ,	validation_data=self.valid_dataset      ,	validation_split=self.validation_split      ,	learning_rate=self.learning_rate      ,	adam_epsilon=self.adam_epsilon      ,	train_batch_size=self.train_batch_size      ,	valid_batch_size=self.valid_batch_size      ,	)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output     )






