
"""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


lowerCamelCase__        =		16
lowerCamelCase__        =		32
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = 16	,  _UpperCamelCase = "bert-base-cased"		):
	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	__lowerCAmelCase      :						str						      =				load_dataset('glue'	,  'mrpc'		)

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

	# Apply the method we just defined to all the examples in all the splits of the dataset
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.map(
	    _UpperCamelCase	,  batched=_UpperCamelCase	,  remove_columns=['idx', 'sentence1', 'sentence2']	,  load_from_cache_file=_UpperCamelCase		)

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

	def collate_fn(_UpperCamelCase		):
		# 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(_UpperCamelCase	,  padding='max_length'	,  max_length=128	,  return_tensors='pt'		)
		return tokenizer.pad(_UpperCamelCase	,  padding='longest'	,  return_tensors='pt'		)

	# Instantiate dataloaders.
	__lowerCAmelCase      :						Dict						      =				DataLoader(
	    tokenized_datasets['train']	,  shuffle=_UpperCamelCase	,  collate_fn=_UpperCamelCase	,  batch_size=_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				DataLoader(
	    tokenized_datasets['validation']	,  shuffle=_UpperCamelCase	,  collate_fn=_UpperCamelCase	,  batch_size=_UpperCamelCase		)

	return train_dataloader, eval_dataloader
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	# Initialize accelerator
	__lowerCAmelCase      :						Any						      =				Accelerator()

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

	set_seed(_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				get_dataloaders(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowerCAmelCase      :						Union[str, Any]						      =				AutoModelForSequenceClassification.from_pretrained(_UpperCamelCase	,  return_dict=_UpperCamelCase		)

	# Instantiate optimizer
	__lowerCAmelCase      :						Any						      =				(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	__lowerCAmelCase      :						int						      =				optimizer_cls(params=model.parameters()	,  lr=_UpperCamelCase		)

	if accelerator.state.deepspeed_plugin is not None:
		__lowerCAmelCase      :						List[Any]						      =				accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		__lowerCAmelCase      :						Dict						      =				1
	__lowerCAmelCase      :						List[Any]						      =				(len(_UpperCamelCase		) * 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      :						int						      =				get_linear_schedule_with_warmup(
		    optimizer=_UpperCamelCase	,  num_warmup_steps=0	,  num_training_steps=_UpperCamelCase	,  )
	else:
		__lowerCAmelCase      :						str						      =				DummyScheduler(_UpperCamelCase	,  total_num_steps=_UpperCamelCase	,  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      :						Optional[int]						      =				accelerator.prepare(
	    _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)

	# We need to keep track of how many total steps we have iterated over
	__lowerCAmelCase      :						int						      =				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      :						Union[str, Any]						      =				evaluate.load('glue'	,  'mrpc'		)
	__lowerCAmelCase      :						Optional[Any]						      =				0
	__lowerCAmelCase      :						List[Any]						      =				{}
	for epoch in range(_UpperCamelCase	,  _UpperCamelCase		):
		model.train()
		for step, batch in enumerate(_UpperCamelCase		):
			__lowerCAmelCase      :						List[str]						      =				model(**_UpperCamelCase		)
			__lowerCAmelCase      :						Optional[Any]						      =				outputs.loss
			__lowerCAmelCase      :						str						      =				loss / gradient_accumulation_steps
			accelerator.backward(_UpperCamelCase		)
			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(_UpperCamelCase		):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device		)
			with torch.no_grad():
				__lowerCAmelCase      :						Any						      =				model(**_UpperCamelCase		)
			__lowerCAmelCase      :						List[Any]						      =				outputs.logits.argmax(dim=-1		)
			# It is slightly faster to call this once, than multiple times
			__lowerCAmelCase      :						int						      =				accelerator.gather(
			    (predictions, batch['labels'])		)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_UpperCamelCase		) - 1:
					__lowerCAmelCase      :						int						      =				predictions[: len(eval_dataloader.dataset		) - samples_seen]
					__lowerCAmelCase      :						Tuple						      =				references[: len(eval_dataloader.dataset		) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_UpperCamelCase	,  references=_UpperCamelCase	,  )

		__lowerCAmelCase      :						List[str]						      =				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"	,  _UpperCamelCase		)
		__lowerCAmelCase      :						Dict						      =				eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			__lowerCAmelCase      :						Union[str, Any]						      =				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(_UpperCamelCase	,  _UpperCamelCase		)
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						List[str]						      =				argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'		)
	parser.add_argument(
	    '--model_name_or_path'	,  type=_UpperCamelCase	,  default='bert-base-cased'	,  help='Path to pretrained model or model identifier from huggingface.co/models.'	,  required=_UpperCamelCase	,  )
	parser.add_argument(
	    '--output_dir'	,  type=_UpperCamelCase	,  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=_UpperCamelCase	,  default=_UpperCamelCase	,  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=_UpperCamelCase	,  default=3	,  help='Number of train epochs.'	,  )
	__lowerCAmelCase      :						Optional[int]						      =				parser.parse_args()
	__lowerCAmelCase      :						Dict						      =				{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
	training_function(_UpperCamelCase	,  _UpperCamelCase		)


if __name__ == "__main__":
							main()

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)