




"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_				:    str										=     16
a_				:    Dict										=     32





def 						UpperCAmelCase				(			A__:    Accelerator							,				A__:    int = 16							,				A__:    str = "bert-base-cased"     )  ->     Dict:
			__lowerCamelCase					:	Dict	       =				AutoTokenizer.from_pretrained(A__     )
			__lowerCamelCase					:	int	       =				load_dataset('glue'							,				'mrpc'     )

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

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowerCamelCase					:	int	       =				datasets.map(
			    A__							,				batched=A__							,				remove_columns=['idx', 'sentence1', 'sentence2']							,				load_from_cache_file=A__     )

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

			def collate_fn(A__:    int     ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A__							,				padding='max_length'							,				max_length=128							,				return_tensors='pt'     )
						return tokenizer.pad(A__							,				padding='longest'							,				return_tensors='pt'     )

			# Instantiate dataloaders.
			__lowerCamelCase					:	Any	       =				DataLoader(
			    tokenized_datasets['train']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=A__     )
			__lowerCamelCase					:	Any	       =				DataLoader(
			    tokenized_datasets['validation']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=A__     )

			return train_dataloader, eval_dataloader





def 						UpperCAmelCase				(			A__:    int							,				A__:    Tuple     )  ->     Optional[int]:
			# Initialize accelerator
			__lowerCamelCase					:	str	       =				Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__lowerCamelCase					:	List[Any]	       =				config['lr']
			__lowerCamelCase					:	int	       =				int(config['num_epochs']     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(config['seed']     )
			__lowerCamelCase					:	Optional[int]	       =				int(config['batch_size']     )
			__lowerCamelCase					:	Union[str, Any]	       =				args.model_name_or_path

			set_seed(A__     )
			__lowerCamelCase					:	Optional[int]	       =				get_dataloaders(A__							,				A__							,				A__     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__lowerCamelCase					:	Any	       =				AutoModelForSequenceClassification.from_pretrained(A__							,				return_dict=A__     )

			# Instantiate optimizer
			__lowerCamelCase					:	List[Any]	       =				(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			__lowerCamelCase					:	Tuple	       =				optimizer_cls(params=model.parameters()							,				lr=A__     )

			if accelerator.state.deepspeed_plugin is not None:
						__lowerCamelCase					:	Optional[int]	       =				accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						__lowerCamelCase					:	Tuple	       =				1
			__lowerCamelCase					:	Any	       =				(len(A__     ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						__lowerCamelCase					:	Union[str, Any]	       =				get_linear_schedule_with_warmup(
						    optimizer=A__							,				num_warmup_steps=0							,				num_training_steps=A__							,				)
			else:
						__lowerCamelCase					:	Dict	       =				DummyScheduler(A__							,				total_num_steps=A__							,				warmup_num_steps=0     )

			# 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.
			__lowerCamelCase					:	List[Any]	       =				accelerator.prepare(
			    A__							,				A__							,				A__							,				A__							,				A__     )

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

			# Now we train the model
			__lowerCamelCase					:	Dict	       =				evaluate.load('glue'							,				'mrpc'     )
			__lowerCamelCase					:	Optional[Any]	       =				0
			__lowerCamelCase					:	str	       =				{}
			for epoch in range(A__							,				A__     ):
						model.train()
						for step, batch in enumerate(A__     ):
									__lowerCamelCase					:	Any	       =				model(**A__     )
									__lowerCamelCase					:	int	       =				outputs.loss
									__lowerCamelCase					:	Optional[Any]	       =				loss / gradient_accumulation_steps
									accelerator.backward(A__     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						__lowerCamelCase					:	Union[str, Any]	       =				0
						for step, batch in enumerate(A__     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									with torch.no_grad():
												__lowerCamelCase					:	Optional[int]	       =				model(**A__     )
									__lowerCamelCase					:	List[Any]	       =				outputs.logits.argmax(dim=-1     )
									# It is slightly faster to call this once, than multiple times
									__lowerCamelCase					:	Optional[Any]	       =				accelerator.gather(
									    (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(A__     ) - 1:
															__lowerCamelCase					:	str	       =				predictions[: len(eval_dataloader.dataset     ) - samples_seen]
															__lowerCamelCase					:	List[Any]	       =				references[: len(eval_dataloader.dataset     ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=A__							,				references=A__							,				)

						__lowerCamelCase					:	Tuple	       =				metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''							,				A__     )
						__lowerCamelCase					:	Optional[int]	       =				eval_metric['accuracy']

						if best_performance < eval_metric["accuracy"]:
									__lowerCamelCase					:	str	       =				eval_metric['accuracy']

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir							,				'all_results.json'     )							,				'w'     ) as f:
									json.dump(A__							,				A__     )





def 						UpperCAmelCase				(			)  ->     Optional[Any]:
			__lowerCamelCase					:	Optional[Any]	       =				argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
			parser.add_argument(
			    '--model_name_or_path'							,				type=A__							,				default='bert-base-cased'							,				help='Path to pretrained model or model identifier from huggingface.co/models.'							,				required=A__							,				)
			parser.add_argument(
			    '--output_dir'							,				type=A__							,				default='.'							,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'							,				)
			parser.add_argument(
			    '--performance_lower_bound'							,				type=A__							,				default=A__							,				help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,				)
			parser.add_argument(
			    '--num_epochs'							,				type=A__							,				default=3							,				help='Number of train epochs.'							,				)
			__lowerCamelCase					:	Union[str, Any]	       =				parser.parse_args()
			__lowerCamelCase					:	Optional[Any]	       =				{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
			training_function(A__							,				A__     )


if __name__ == "__main__":
					main()








"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	List[Any]	       =				field
								__lowerCamelCase					:	Dict	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	str	       =				Json(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	field=__a						,	**__a						,	)





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[Any]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	__a = None						,	**__a						,	):
								if num_proc is not None and num_proc <= 0:
											raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''     )

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with fsspec.open(self.path_or_buf						,	'wb'						,	compression=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								else:
											if compression:
														raise NotImplementedError(
														    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
														    ' was passed. Please provide a local path instead.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0						,	len(self.dataset     )						,	self.batch_size     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				len(self.dataset     ), self.batch_size
											with multiprocessing.Pool(self.num_proc     ) as pool:
														for json_str in logging.tqdm(
														    pool.imap(
														        self._batch_json						,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,	__a						,	__a     )]						,	)						,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
																	written += file_obj.write(__a     )

								return written



