




"""simple docstring"""





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_lowercase		 =      get_logger(__name__)

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						return TestCommand(
						    args.dataset ,     args.name ,     args.cache_dir ,     args.data_dir ,     args.all_configs ,     args.save_info or args.save_infos ,     args.ignore_verifications ,     args.force_redownload ,     args.clear_cache ,     )

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =False  # to tell pytest it's not a test class

				@staticmethod
				def  _lowerCamelCase   (				SCREAMING_SNAKE_CASE_				:       ArgumentParser ):
										snake_case__    :   List[Any]							     =   parser.add_parser('''test''' ,help='''Test dataset implementation.''' )
										test_parser.add_argument('''--name''' ,type=SCREAMING_SNAKE_CASE_ ,default=SCREAMING_SNAKE_CASE_ ,help='''Dataset processing name''' )
										test_parser.add_argument(
										    '''--cache_dir''' ,type=SCREAMING_SNAKE_CASE_ ,default=SCREAMING_SNAKE_CASE_ ,help='''Cache directory where the datasets are stored.''' ,)
										test_parser.add_argument(
										    '''--data_dir''' ,type=SCREAMING_SNAKE_CASE_ ,default=SCREAMING_SNAKE_CASE_ ,help='''Can be used to specify a manual directory to get the files from.''' ,)
										test_parser.add_argument('''--all_configs''' ,action='''store_true''' ,help='''Test all dataset configurations''' )
										test_parser.add_argument(
										    '''--save_info''' ,action='''store_true''' ,help='''Save the dataset infos in the dataset card (README.md)''' )
										test_parser.add_argument(
										    '''--ignore_verifications''' ,action='''store_true''' ,help='''Run the test without checksums and splits checks.''' ,)
										test_parser.add_argument('''--force_redownload''' ,action='''store_true''' ,help='''Force dataset redownload''' )
										test_parser.add_argument(
										    '''--clear_cache''' ,action='''store_true''' ,help='''Remove downloaded files and cached datasets after each config test''' ,)
										# aliases
										test_parser.add_argument('''--save_infos''' ,action='''store_true''' ,help='''alias to save_info''' )
										test_parser.add_argument('''dataset''' ,type=SCREAMING_SNAKE_CASE_ ,help='''Name of the dataset to download''' )
										test_parser.set_defaults(func=_test_command_factory )

				def __init__(				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       str ,SCREAMING_SNAKE_CASE_				:       bool ,SCREAMING_SNAKE_CASE_				:       bool ,SCREAMING_SNAKE_CASE_				:       bool ,SCREAMING_SNAKE_CASE_				:       bool ,SCREAMING_SNAKE_CASE_				:       bool ,):
										snake_case__    :   List[Any]							     =   dataset
										snake_case__    :   Optional[Any]							     =   name
										snake_case__    :   List[Any]							     =   cache_dir
										snake_case__    :   str							     =   data_dir
										snake_case__    :   Dict							     =   all_configs
										snake_case__    :   int							     =   save_infos
										snake_case__    :   Dict							     =   ignore_verifications
										snake_case__    :   Tuple							     =   force_redownload
										snake_case__    :   Dict							     =   clear_cache
										if clear_cache and not cache_dir:
																print(
																    '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
																    '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
																    '''Please provide a --cache_dir that will be used to test the dataset script.''' )
																exit(1 )
										if save_infos:
																snake_case__    :   str							     =   True





				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										fl_logger().setLevel(SCREAMING_SNAKE_CASE_ )
										if self._name is not None and self._all_configs:
																print('''Both parameters `config` and `all_configs` can\'t be used at once.''' )
																exit(1 )
										snake_case__    :   Any							     =   self._dataset, self._name
										snake_case__    :   Optional[Any]							     =   dataset_module_factory(SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Tuple							     =   import_main_class(module.module_path )
										snake_case__    :   Union[str, Any]							     =   len(builder_cls.BUILDER_CONFIGS ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

										def get_builders() -> Generator[DatasetBuilder, None, None]:
																if self._all_configs and builder_cls.BUILDER_CONFIGS:
																						for i, config in enumerate(builder_cls.BUILDER_CONFIGS ):
																												if "config_name" in module.builder_kwargs:
																																		yield builder_cls(
																																		    cache_dir=self._cache_dir ,data_dir=self._data_dir ,**module.builder_kwargs ,)
																												else:
																																		yield builder_cls(
																																		    config_name=config.name ,cache_dir=self._cache_dir ,data_dir=self._data_dir ,**module.builder_kwargs ,)
																else:
																						if "config_name" in module.builder_kwargs:
																												yield builder_cls(cache_dir=self._cache_dir ,data_dir=self._data_dir ,**module.builder_kwargs )
																						else:
																												yield builder_cls(
																												    config_name=SCREAMING_SNAKE_CASE_ ,cache_dir=self._cache_dir ,data_dir=self._data_dir ,**module.builder_kwargs ,)

										for j, builder in enumerate(get_builders() ):
																print(F'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})''' )
																snake_case__    :   Tuple							     =   os.path.exists(
																    os.path.join(builder.get_imported_module_dir() ,datasets.config.DATASETDICT_INFOS_FILENAME ) )  # record checksums only if we need to update a (deprecated) dataset_infos.json
																builder.download_and_prepare(
																    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
																    if not self._force_redownload
																    else DownloadMode.FORCE_REDOWNLOAD ,verification_mode=VerificationMode.NO_CHECKS
																    if self._ignore_verifications
																    else VerificationMode.ALL_CHECKS ,try_from_hf_gcs=SCREAMING_SNAKE_CASE_ ,)
																builder.as_dataset()
																if self._save_infos:
																						builder._save_infos()

																# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
																# The dataset_infos are saved in the YAML part of the README.md

																# Let's move it to the original directory of the dataset script, to allow the user to
																# upload them on S3 at the same time afterwards.
																if self._save_infos:
																						snake_case__    :   int							     =   os.path.join(builder_cls.get_imported_module_dir() ,'''README.md''' )
																						snake_case__    :   Tuple							     =   Path(SCREAMING_SNAKE_CASE_ ).name + '''.py'''
																						snake_case__    :   Any							     =   os.path.join(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
																						if os.path.isfile(SCREAMING_SNAKE_CASE_ ):
																												snake_case__    :   str							     =   os.path.dirname(SCREAMING_SNAKE_CASE_ )
																						elif os.path.isfile(SCREAMING_SNAKE_CASE_ ):
																												snake_case__    :   Optional[Any]							     =   path
																						elif os.path.isdir(SCREAMING_SNAKE_CASE_ ):  # for local directories containing only data files
																												snake_case__    :   int							     =   path
																						else:  # in case of a remote dataset
																												snake_case__    :   List[Any]							     =   None
																												print(F'''Dataset card saved at {dataset_readme_path}''' )

																						# Move dataset_info back to the user
																						if dataset_dir is not None:
																												snake_case__    :   List[Any]							     =   os.path.join(SCREAMING_SNAKE_CASE_ ,'''README.md''' )
																												copyfile(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
																												print(F'''Dataset card saved at {user_dataset_readme_path}''' )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
																if self._clear_cache:
																						if os.path.isdir(builder._cache_dir ):
																												logger.warning(F'''Clearing cache at {builder._cache_dir}''' )
																												rmtree(builder._cache_dir )
																						snake_case__    :   int							     =   os.path.join(self._cache_dir ,datasets.config.DOWNLOADED_DATASETS_DIR )
																						if os.path.isdir(SCREAMING_SNAKE_CASE_ ):
																												logger.warning(F'''Clearing cache at {download_dir}''' )
																												rmtree(SCREAMING_SNAKE_CASE_ )

										print('''Test successful.''' )










"""simple docstring"""





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase		 =      16
_lowercase		 =      32

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase = 16						):
						'''simple docstring'''


						snake_case__    :   int							     =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						snake_case__    :   Dict							     =   load_dataset('''glue''' ,     '''mrpc'''						)

						def tokenize_function(lowerCamelCase						):
												# max_length=None => use the model max length (it's actually the default)
												snake_case__    :   Optional[int]							     =   tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=lowerCamelCase ,     max_length=lowerCamelCase						)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												snake_case__    :   Tuple							     =   datasets.map(
												    lowerCamelCase ,     batched=lowerCamelCase ,     remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,     )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						snake_case__    :   str							     =   tokenized_datasets.rename_column('''label''' ,     '''labels'''						)

						def collate_fn(lowerCamelCase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case__    :   Tuple							     =   128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case__    :   Optional[Any]							     =   16
												elif accelerator.mixed_precision != "no":
																		snake_case__    :   Dict							     =   8
												else:
																		snake_case__    :   str							     =   None

												return tokenizer.pad(
												    lowerCamelCase ,     padding='''longest''' ,     max_length=lowerCamelCase ,     pad_to_multiple_of=lowerCamelCase ,     return_tensors='''pt''' ,     )

						# Instantiate dataloaders.
						snake_case__    :   List[Any]							     =   DataLoader(
						    tokenized_datasets['''train'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)
						snake_case__    :   Tuple							     =   DataLoader(
						    tokenized_datasets['''validation'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase		 =      mocked_dataloaders  # noqa: F811

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     lowerCamelCase						) == "1":
												snake_case__    :   Any							     =   2
						# Initialize accelerator
						snake_case__    :   List[Any]							     =   Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case__    :   Any							     =   config['''lr''']
						snake_case__    :   int							     =   int(config['''num_epochs''']						)
						snake_case__    :   List[str]							     =   int(config['''seed''']						)
						snake_case__    :   Dict							     =   int(config['''batch_size''']						)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps ,     '''isdigit'''						):
												if args.checkpointing_steps == "epoch":
																		snake_case__    :   Optional[Any]							     =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case__    :   Union[str, Any]							     =   int(args.checkpointing_steps						)
												else:
																		raise ValueError(
																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
						else:
												snake_case__    :   Union[str, Any]							     =   None

						set_seed(lowerCamelCase						)

						snake_case__     ,		snake_case__    :   int							     =   get_dataloaders(lowerCamelCase ,     lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   evaluate.load('''glue''' ,     '''mrpc'''						)

						# If the batch size is too big we use gradient accumulation
						snake_case__    :   Optional[int]							     =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case__    :   Any							     =   batch_size // MAX_GPU_BATCH_SIZE
												snake_case__    :   Union[str, Any]							     =   MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case__    :   Tuple							     =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,     return_dict=lowerCamelCase						)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						snake_case__    :   Dict							     =   model.to(accelerator.device						)

						# Instantiate optimizer
						snake_case__    :   Optional[int]							     =   AdamW(params=model.parameters() ,     lr=lowerCamelCase						)

						# Instantiate scheduler
						snake_case__    :   Optional[Any]							     =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(lowerCamelCase						) * num_epochs) // gradient_accumulation_steps ,     )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Optional[Any]							     =   accelerator.prepare(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case__    :   Any							     =   0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case__    :   List[str]							     =   0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''						)
																		accelerator.load_state(args.resume_from_checkpoint						)
																		snake_case__    :   Optional[Any]							     =   os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		snake_case__    :   Any							     =   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		snake_case__    :   str							     =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case__    :   List[Any]							     =   os.path.splitext(lowerCamelCase						)[0]

												if "epoch" in training_difference:
																		snake_case__    :   Optional[Any]							     =   int(training_difference.replace('''epoch_''' ,     ''''''						)						) + 1
																		snake_case__    :   List[str]							     =   None
												else:
																		snake_case__    :   int							     =   int(training_difference.replace('''step_''' ,     ''''''						)						)
																		snake_case__    :   Union[str, Any]							     =   resume_step // len(lowerCamelCase						)
																		resume_step -= starting_epoch * len(lowerCamelCase						)

    # Now we train the model
						for epoch in range(lowerCamelCase ,     lowerCamelCase						):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case__    :   int							     =   accelerator.skip_first_batches(lowerCamelCase ,     lowerCamelCase						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case__    :   Tuple							     =   train_dataloader
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		snake_case__    :   Tuple							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.loss
																		snake_case__    :   Union[str, Any]							     =   loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(lowerCamelCase ,     lowerCamelCase						):
																								snake_case__    :   Tuple							     =   f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																														accelerator.save_state(lowerCamelCase						)

												model.eval()
												for step, batch in enumerate(lowerCamelCase						):
																		# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								snake_case__    :   List[str]							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.logits.argmax(dim=-1						)
																		snake_case__     ,		snake_case__    :   Dict							     =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																		metric.add_batch(
																		    predictions=lowerCamelCase ,     references=lowerCamelCase ,     )

												snake_case__    :   str							     =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,     lowerCamelCase						)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case__    :   str							     =   f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																		accelerator.save_state(lowerCamelCase						)

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,     help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.''' ,     )
						parser.add_argument('''--cpu''' ,     action='''store_true''' ,     help='''If passed, will train on the CPU.'''						)
						parser.add_argument(
						    '''--checkpointing_steps''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''' ,     )
						parser.add_argument(
						    '''--output_dir''' ,     type=lowerCamelCase ,     default='''.''' ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,     )
						parser.add_argument(
						    '''--resume_from_checkpoint''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     help='''If the training should continue from a checkpoint folder.''' ,     )
						snake_case__    :   List[str]							     =   parser.parse_args()
						snake_case__    :   str							     =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowerCamelCase ,     lowerCamelCase						)


if __name__ == "__main__":
							main()


