

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
SCREAMING_SNAKE_CASE		:List[Any]						=     False
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     False




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    return TrainCommand(SCREAMING_SNAKE_CASE_			)






class 						__magic_name__					(							snake_case				):






     @staticmethod
     def 	UpperCAmelCase_				(				_lowercase	)->     str:
         UpperCamelCase_			  =						parser.add_parser("train"		,						help="CLI tool to train a model on a task."	)

         train_parser.add_argument(
             "--train_data"		,						type=_lowercase		,						required=_lowercase		,						help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."		,						)
         train_parser.add_argument(
             "--column_label"		,						type=_lowercase		,						default=0		,						help="Column of the dataset csv file with example labels."	)
         train_parser.add_argument(
             "--column_text"		,						type=_lowercase		,						default=1		,						help="Column of the dataset csv file with example texts."	)
         train_parser.add_argument(
             "--column_id"		,						type=_lowercase		,						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=_lowercase		,						default=""		,						help="path to validation dataset."	)
         train_parser.add_argument(
             "--validation_split"		,						type=_lowercase		,						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=_lowercase		,						default="./"		,						help="path to saved the trained model."	)

         train_parser.add_argument(
             "--task"		,						type=_lowercase		,						default="text_classification"		,						help="Task to train the model on."	)
         train_parser.add_argument(
             "--model"		,						type=_lowercase		,						default="bert-base-uncased"		,						help="Model's name or path to stored model."	)
         train_parser.add_argument("--train_batch_size"		,						type=_lowercase		,						default=32		,						help="Batch size for training."	)
         train_parser.add_argument("--valid_batch_size"		,						type=_lowercase		,						default=64		,						help="Batch size for validation."	)
         train_parser.add_argument("--learning_rate"		,						type=_lowercase		,						default=3e-5		,						help="Learning rate."	)
         train_parser.add_argument("--adam_epsilon"		,						type=_lowercase		,						default=1e-0_8		,						help="Epsilon for Adam optimizer."	)
         train_parser.set_defaults(func=_lowercase	)






     def __init__(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						logging.get_logger("transformers-cli/training"	)

         UpperCamelCase_			  =						"tf" if is_tf_available() else "torch"

         os.makedirs(args.output		,						exist_ok=_lowercase	)
         UpperCamelCase_			  =						args.output

         UpperCamelCase_			  =						args.column_label
         UpperCamelCase_			  =						args.column_text
         UpperCamelCase_			  =						args.column_id

         self.logger.info(F"Loading {args.task} pipeline for {args.model}"	)
         if args.task == "text_classification":
             UpperCamelCase_			  =						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}"	)
         UpperCamelCase_			  =						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		,						)
         UpperCamelCase_			  =						None
         if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"	)
             UpperCamelCase_			  =						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		,						)

         UpperCamelCase_			  =						args.validation_split
         UpperCamelCase_			  =						args.train_batch_size
         UpperCamelCase_			  =						args.valid_batch_size
         UpperCamelCase_			  =						args.learning_rate
         UpperCamelCase_			  =						args.adam_epsilon






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






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         raise NotImplementedError






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         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	)



import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_=2			,						SCREAMING_SNAKE_CASE_=3			,						SCREAMING_SNAKE_CASE_=1_6			,						SCREAMING_SNAKE_CASE_ = 1_0			,						SCREAMING_SNAKE_CASE_ = 2			)->						Optional[Any]:



    """simple docstring"""






    def get_dataset(SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						torch.randn(batch_size * n_batches			,						1			)
        return TensorDataset(SCREAMING_SNAKE_CASE_			,						a * x + b + 0.1 * torch.randn(batch_size * n_batches			,						1			)			)

    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    return (train_dataloader, valid_dataloader)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    for epoch in range(SCREAMING_SNAKE_CASE_			):
        # Train quickly
        model.train()
        for batch in dataloader:
            UpperCamelCase_  ,       UpperCamelCase_			  =						batch
            UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            accelerator.backward(SCREAMING_SNAKE_CASE_			)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()			)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands






class 						__magic_name__					(							nn.Module				):






     def __init__(				self	)->     List[Any]:
         super().__init__()
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)
         UpperCamelCase_			  =						nn.Parameter(torch.randn(1	)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         return x * self.a + self.b






class 						__magic_name__					(							unittest.TestCase				):






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(total_limit=1		,						project_dir=_lowercase		,						automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir	)	)		,						1	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             # Train baseline
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"initial"	)
             accelerator.save_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"checkpoint"	)
             accelerator.save_state(_lowercase	)

             # Load everything back in and make sure all states work
             accelerator.load_state(_lowercase	)
             test_rands += train(1		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)

             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(iteration=1		,						automatic_checkpoint_naming=_lowercase	)
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_1"	)	)
             test_rands += train(1		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						torch.tensor([1, 2, 3]	)
         UpperCamelCase_			  =						torch.tensor([2, 3, 4]	)
         UpperCamelCase_			  =						DummyModel()
         UpperCamelCase_			  =						torch.optim.Adam(net.parameters()	)
         UpperCamelCase_			  =						Accelerator()
         with self.assertRaises(_lowercase	) as ve:
             accelerator.register_for_checkpointing(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						str(ve.exception	)
         self.assertTrue("Item at index 0" in message	)
         self.assertTrue("Item at index 1" in message	)
         self.assertFalse("Item at index 2" in message	)
         self.assertFalse("Item at index 3" in message	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_			  =						torch.optim.lr_scheduler.StepLR(_lowercase		,						step_size=1		,						gamma=0.99	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             UpperCamelCase_			  =						scheduler.state_dict()
             train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             self.assertNotEqual(_lowercase		,						scheduler.state_dict()	)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)
             self.assertEqual(_lowercase		,						scheduler.state_dict()	)






     def 	UpperCAmelCase_				(				self	)->     str:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase		,						total_limit=2	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_			  =						accelerator.prepare(_lowercase	)
             # Save 3 states:
             for _ in range(11	):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_9"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_10"	)	)	)






     @require_cuda
     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__	)]
         execute_subprocess_async(_lowercase		,						env=os.environ.copy()	)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Any						=     """/tmp/accelerate/state_checkpointing"""
 SCREAMING_SNAKE_CASE		:Any						=     DummyModel()
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     torch.optim.Adam(params=model.parameters(), lr=1e-3)
 SCREAMING_SNAKE_CASE		:Dict						=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:str						=     dummy_dataloaders()
 SCREAMING_SNAKE_CASE		:Tuple						=     ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 SCREAMING_SNAKE_CASE		:int						=     Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:Tuple						=     accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:List[str]						=     accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:Optional[Any]						=     group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 SCREAMING_SNAKE_CASE		:List[str]						=     model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:Optional[int]						=     group["""params"""][0].device
  break
 assert (
     param_device.type == torch.device("""cpu""").type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
 for group in optimizer.param_groups:
  SCREAMING_SNAKE_CASE		:str						=     group["""params"""][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()
