





'''simple docstring'''







from typing import TYPE_CHECKING

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


snake_case_     :       Tuple						=  {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_     :       Optional[int]						=  ["RobertaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_     :       Optional[int]						=  [
					    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "RobertaForCausalLM",
					    "RobertaForMaskedLM",
					    "RobertaForMultipleChoice",
					    "RobertaForQuestionAnswering",
					    "RobertaForSequenceClassification",
					    "RobertaForTokenClassification",
					    "RobertaModel",
					    "RobertaPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_     :       Dict						=  [
					    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFRobertaForCausalLM",
					    "TFRobertaForMaskedLM",
					    "TFRobertaForMultipleChoice",
					    "TFRobertaForQuestionAnswering",
					    "TFRobertaForSequenceClassification",
					    "TFRobertaForTokenClassification",
					    "TFRobertaMainLayer",
					    "TFRobertaModel",
					    "TFRobertaPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_     :       int						=  [
					    "FlaxRobertaForCausalLM",
					    "FlaxRobertaForMaskedLM",
					    "FlaxRobertaForMultipleChoice",
					    "FlaxRobertaForQuestionAnswering",
					    "FlaxRobertaForSequenceClassification",
					    "FlaxRobertaForTokenClassification",
					    "FlaxRobertaModel",
					    "FlaxRobertaPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

					snake_case_     :       str						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












'''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


snake_case_     :       Optional[int]						=  16
snake_case_     :       Tuple						=  32





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

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

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

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

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

						# Instantiate dataloaders.
						UpperCAmelCase_			:      str     						=  DataLoader(
						    tokenized_datasets['''train'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  DataLoader(
						    tokenized_datasets['''validation'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )

						return train_dataloader, eval_dataloader





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

						UpperCAmelCase_			:      List[str]     						=  metric.compute()
						return eval_metric["accuracy"]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			int   ) ->	Tuple:
						# Initialize accelerator
						UpperCAmelCase_			:      Union[str, Any]     						=  Accelerator()

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

						set_seed(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  get_dataloaders(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_			:      List[Any]     						=  AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			return_dict=SCREAMING_SNAKE_CASE__   )

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

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_			:      List[Any]     						=  accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												UpperCAmelCase_			:      Tuple     						=  1
						UpperCAmelCase_			:      int     						=  (len(SCREAMING_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
						):
												UpperCAmelCase_			:      Tuple     						=  get_linear_schedule_with_warmup(
												    optimizer=SCREAMING_SNAKE_CASE__,			num_warmup_steps=0,			num_training_steps=SCREAMING_SNAKE_CASE__,			)
						else:
												UpperCAmelCase_			:      Any     						=  DummyScheduler(SCREAMING_SNAKE_CASE__,			total_num_steps=SCREAMING_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.
						UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  accelerator.prepare(
						    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_			:      Union[str, Any]     						=  0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_			:      Dict     						=  0
						UpperCAmelCase_			:      int     						=  evaluate.load('''glue''',			'''mrpc'''   )
						UpperCAmelCase_			:      Optional[Any]     						=  num_epochs

						if args.partial_train_epoch is not None:
												UpperCAmelCase_			:      List[Any]     						=  args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint   )
												UpperCAmelCase_			:      Tuple     						=  args.resume_from_checkpoint.split('''epoch_'''   )[1]
												UpperCAmelCase_			:      int     						=  ''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												UpperCAmelCase_			:      Union[str, Any]     						=  int(SCREAMING_SNAKE_CASE__   ) + 1
												UpperCAmelCase_			:      Dict     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												accelerator.print('''resumed checkpoint performance:''',			SCREAMING_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:
																		UpperCAmelCase_			:      Optional[int]     						=  json.load(SCREAMING_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
						UpperCAmelCase_			:      int     						=  {}
						for epoch in range(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
												model.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Optional[int]     						=  model(**SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      Any     						=  outputs.loss
																		UpperCAmelCase_			:      Tuple     						=  loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE__   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												UpperCAmelCase_			:      Tuple     						=  F"""epoch_{epoch}"""
												UpperCAmelCase_			:      Optional[int]     						=  os.path.join(args.output_dir,			SCREAMING_SNAKE_CASE__   )
												accelerator.save_state(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      int     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[Any]     						=  accuracy
												UpperCAmelCase_			:      Any     						=  lr_scheduler.get_lr()[0]
												UpperCAmelCase_			:      List[str]     						=  optimizer.param_groups[0]['''lr''']
												UpperCAmelCase_			:      Tuple     						=  epoch
												UpperCAmelCase_			:      Dict     						=  overall_step
												accelerator.print(F"""epoch {epoch}:""",			SCREAMING_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(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )





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

						training_function(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					main()





