



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__	      =       16
A__	      =       32


def 		_UpperCAmelCase				(      snake_case	,						snake_case = 16	,						snake_case = "bert-base-cased"			):





					"""simple docstring"""

					_lowerCAmelCase						=    AutoTokenizer.from_pretrained(snake_case			)
					_lowerCAmelCase						=    load_dataset("""glue"""	,						"""mrpc"""			)

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

					# Apply the method we just defined to all the examples in all the splits of the dataset
					_lowerCAmelCase						=    datasets.map(
					    snake_case	,						batched=snake_case	,						remove_columns=["""idx""", """sentence1""", """sentence2"""]	,						load_from_cache_file=snake_case			)

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

					def collate_fn(snake_case			):
										# 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(snake_case	,						padding="""max_length"""	,						max_length=1_28	,						return_tensors="""pt"""			)
										return tokenizer.pad(snake_case	,						padding="""longest"""	,						return_tensors="""pt"""			)

					# Instantiate dataloaders.
					_lowerCAmelCase						=    DataLoader(
					    tokenized_datasets["""train"""]	,						shuffle=snake_case	,						collate_fn=snake_case	,						batch_size=snake_case			)
					_lowerCAmelCase						=    DataLoader(
					    tokenized_datasets["""validation"""]	,						shuffle=snake_case	,						collate_fn=snake_case	,						batch_size=snake_case			)

					return train_dataloader, eval_dataloader


def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case	,						snake_case			):





					"""simple docstring"""

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

					_lowerCAmelCase						=    metric.compute()
					return eval_metric["accuracy"]


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_lowerCAmelCase						=    config["""lr"""]
					_lowerCAmelCase						=    int(config["""num_epochs"""]			)
					_lowerCAmelCase						=    int(config["""seed"""]			)
					_lowerCAmelCase						=    int(config["""batch_size"""]			)
					_lowerCAmelCase						=    args.model_name_or_path

					set_seed(snake_case			)
					_lowerCAmelCase	,	_lowerCAmelCase						=    get_dataloaders(snake_case	,						snake_case	,						snake_case			)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_lowerCAmelCase						=    AutoModelForSequenceClassification.from_pretrained(snake_case	,						return_dict=snake_case			)

					# Instantiate optimizer
					_lowerCAmelCase						=    (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					_lowerCAmelCase						=    optimizer_cls(params=model.parameters()	,						lr=snake_case			)

					if accelerator.state.deepspeed_plugin is not None:
										_lowerCAmelCase						=    accelerator.state.deepspeed_plugin.deepspeed_config[
										    """gradient_accumulation_steps"""
										]
					else:
										_lowerCAmelCase						=    1
					_lowerCAmelCase						=    (len(snake_case			) * 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						=    get_linear_schedule_with_warmup(
										    optimizer=snake_case	,						num_warmup_steps=0	,						num_training_steps=snake_case	,						)
					else:
										_lowerCAmelCase						=    DummyScheduler(snake_case	,						total_num_steps=snake_case	,						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	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    accelerator.prepare(
					    snake_case	,						snake_case	,						snake_case	,						snake_case	,						snake_case			)

					# We need to keep track of how many total steps we have iterated over
					_lowerCAmelCase						=    0
					# We also need to keep track of the stating epoch so files are named properly
					_lowerCAmelCase						=    0
					_lowerCAmelCase						=    evaluate.load("""glue"""	,						"""mrpc"""			)
					_lowerCAmelCase						=    num_epochs

					if args.partial_train_epoch is not None:
										_lowerCAmelCase						=    args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint			)
										_lowerCAmelCase						=    args.resume_from_checkpoint.split("""epoch_"""			)[1]
										_lowerCAmelCase						=    """"""
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										_lowerCAmelCase						=    int(snake_case			) + 1
										_lowerCAmelCase						=    evaluation_loop(snake_case	,						snake_case	,						snake_case	,						snake_case			)
										accelerator.print("""resumed checkpoint performance:"""	,						snake_case			)
										accelerator.print("""resumed checkpoint's scheduler's lr:"""	,						lr_scheduler.get_lr()[0]			)
										accelerator.print("""resumed optimizers's lr:"""	,						optimizer.param_groups[0]["""lr"""]			)
										with open(os.path.join(args.output_dir	,						F'state_{starting_epoch-1}.json'			)	,						"""r"""			) as f:
															_lowerCAmelCase						=    json.load(snake_case			)
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					_lowerCAmelCase						=    {}
					for epoch in range(snake_case	,						snake_case			):
										model.train()
										for step, batch in enumerate(snake_case			):
															_lowerCAmelCase						=    model(**snake_case			)
															_lowerCAmelCase						=    outputs.loss
															_lowerCAmelCase						=    loss / gradient_accumulation_steps
															accelerator.backward(snake_case			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										_lowerCAmelCase						=    F'epoch_{epoch}'
										_lowerCAmelCase						=    os.path.join(args.output_dir	,						snake_case			)
										accelerator.save_state(snake_case			)
										_lowerCAmelCase						=    evaluation_loop(snake_case	,						snake_case	,						snake_case	,						snake_case			)
										_lowerCAmelCase						=    accuracy
										_lowerCAmelCase						=    lr_scheduler.get_lr()[0]
										_lowerCAmelCase						=    optimizer.param_groups[0]["""lr"""]
										_lowerCAmelCase						=    epoch
										_lowerCAmelCase						=    overall_step
										accelerator.print(F'epoch {epoch}:'	,						snake_case			)

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir	,						F'state_{epoch}.json'			)	,						"""w"""			) as f:
																				json.dump(snake_case	,						snake_case			)


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""			)
					parser.add_argument(
					    """--model_name_or_path"""	,						type=snake_case	,						default="""bert-base-cased"""	,						help="""Path to pretrained model or model identifier from huggingface.co/models."""	,						required=snake_case	,						)
					parser.add_argument(
					    """--output_dir"""	,						type=snake_case	,						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=snake_case	,						default=snake_case	,						help="""If the training should continue from a checkpoint folder."""	,						)
					parser.add_argument(
					    """--partial_train_epoch"""	,						type=snake_case	,						default=snake_case	,						help="""If passed, the training will stop after this number of epochs."""	,						)
					parser.add_argument(
					    """--num_epochs"""	,						type=snake_case	,						default=2	,						help="""Number of train epochs."""	,						)
					_lowerCAmelCase						=    parser.parse_args()
					_lowerCAmelCase						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

					training_function(snake_case	,						snake_case			)


if __name__ == "__main__":
	main()







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase    					=      {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      ["""BertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BertForMaskedLM""",
     """BertForMultipleChoice""",
     """BertForNextSentencePrediction""",
     """BertForPreTraining""",
     """BertForQuestionAnswering""",
     """BertForSequenceClassification""",
     """BertForTokenClassification""",
     """BertLayer""",
     """BertLMHeadModel""",
     """BertModel""",
     """BertPreTrainedModel""",
     """load_tf_weights_in_bert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFBertEmbeddings""",
     """TFBertForMaskedLM""",
     """TFBertForMultipleChoice""",
     """TFBertForNextSentencePrediction""",
     """TFBertForPreTraining""",
     """TFBertForQuestionAnswering""",
     """TFBertForSequenceClassification""",
     """TFBertForTokenClassification""",
     """TFBertLMHeadModel""",
     """TFBertMainLayer""",
     """TFBertModel""",
     """TFBertPreTrainedModel""",
 ]
try:
 if not is_tensorflow_text_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      ["""TFBertTokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """FlaxBertForCausalLM""",
     """FlaxBertForMaskedLM""",
     """FlaxBertForMultipleChoice""",
     """FlaxBertForNextSentencePrediction""",
     """FlaxBertForPreTraining""",
     """FlaxBertForQuestionAnswering""",
     """FlaxBertForSequenceClassification""",
     """FlaxBertForTokenClassification""",
     """FlaxBertModel""",
     """FlaxBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
 from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_fast import BertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert import (
      BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertForMaskedLM,
      BertForMultipleChoice,
      BertForNextSentencePrediction,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertForTokenClassification,
      BertLayer,
      BertLMHeadModel,
      BertModel,
      BertPreTrainedModel,
      load_tf_weights_in_bert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_bert import (
      TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFBertEmbeddings,
      TFBertForMaskedLM,
      TFBertForMultipleChoice,
      TFBertForNextSentencePrediction,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertForTokenClassification,
      TFBertLMHeadModel,
      TFBertMainLayer,
      TFBertModel,
      TFBertPreTrainedModel,
  )

 try:
  if not is_tensorflow_text_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_tf import TFBertTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_bert import (
      FlaxBertForCausalLM,
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
      FlaxBertPreTrainedModel,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






