






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


lowercase  :							int			  =    16
lowercase  :							Tuple			  =    32

def      lowerCAmelCase__	(    _a       :			Accelerator  ,      _a       :			int = 16  ,      _a       :			str = "bert-base-cased"    ):
		snake_case_				:							Optional[int]						 =       AutoTokenizer.from_pretrained(_a    )
		snake_case_				:							Optional[int]						 =       load_dataset("glue"  ,      "mrpc"    )

		def tokenize_function(_a       :			Optional[Any]    ):
				# max_length=None => use the model max length (it's actually the default)
				snake_case_				:							List[str]						 =       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
		snake_case_				:							Optional[Any]						 =       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
		snake_case_				:							Optional[int]						 =       tokenized_datasets.rename_column("label"  ,      "labels"    )

		def collate_fn(_a       :			Optional[Any]    ):
				# 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=1_28  ,      return_tensors="pt"    )
				return tokenizer.pad(_a  ,      padding="longest"  ,      return_tensors="pt"    )

		# Instantiate dataloaders.
		snake_case_				:							Tuple						 =       DataLoader(
		    tokenized_datasets["train"]  ,      shuffle=_a  ,      collate_fn=_a  ,      batch_size=_a    )
		snake_case_				:							Dict						 =       DataLoader(
		    tokenized_datasets["validation"]  ,      shuffle=_a  ,      collate_fn=_a  ,      batch_size=_a    )

		return train_dataloader, eval_dataloader

def      lowerCAmelCase__	(    _a       :			Tuple  ,      _a       :			List[Any]    ):
		# Initialize accelerator
		snake_case_				:							Optional[Any]						 =       Accelerator()

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

		set_seed(_a    )
		snake_case_ ,       snake_case_				:							str						 =       get_dataloaders(_a  ,      _a  ,      _a    )

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

		# Instantiate optimizer
		snake_case_				:							Optional[Any]						 =       (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		snake_case_				:							Union[str, Any]						 =       optimizer_cls(params=model.parameters()  ,      lr=_a    )

		if accelerator.state.deepspeed_plugin is not None:
				snake_case_				:							Tuple						 =       accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				snake_case_				:							Tuple						 =       1
		snake_case_				:							Dict						 =       (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
		):
				snake_case_				:							Dict						 =       get_linear_schedule_with_warmup(
				    optimizer=_a  ,      num_warmup_steps=0  ,      num_training_steps=_a  ,      )
		else:
				snake_case_				:							Any						 =       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.
		snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_				:							Optional[Any]						 =       accelerator.prepare(
		    _a  ,      _a  ,      _a  ,      _a  ,      _a    )

		# We need to keep track of how many total steps we have iterated over
		snake_case_				:							Optional[Any]						 =       0
		# We also need to keep track of the stating epoch so files are named properly
		snake_case_				:							int						 =       0

		# Now we train the model
		snake_case_				:							Tuple						 =       evaluate.load("glue"  ,      "mrpc"    )
		snake_case_				:							List[str]						 =       0
		snake_case_				:							Union[str, Any]						 =       {}
		for epoch in range(_a  ,      _a    ):
				model.train()
				for step, batch in enumerate(_a    ):
						snake_case_				:							Dict						 =       model(**_a    )
						snake_case_				:							List[str]						 =       outputs.loss
						snake_case_				:							List[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()
				snake_case_				:							Optional[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():
								snake_case_				:							Any						 =       model(**_a    )
						snake_case_				:							Optional[int]						 =       outputs.logits.argmax(dim=-1    )
						# It is slightly faster to call this once, than multiple times
						snake_case_ ,       snake_case_				:							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(_a    ) - 1:
										snake_case_				:							List[Any]						 =       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
										snake_case_				:							Any						 =       references[: len(eval_dataloader.dataset    ) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=_a  ,      references=_a  ,      )

				snake_case_				:							Optional[int]						 =       metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'''epoch {epoch}:'''  ,      _a    )
				snake_case_				:							List[Any]						 =       eval_metric["accuracy"]

				if best_performance < eval_metric["accuracy"]:
						snake_case_				:							Tuple						 =       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      lowerCAmelCase__	(    ):
		snake_case_				:							Tuple						 =       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."  ,      )
		snake_case_				:							Optional[Any]						 =       parser.parse_args()
		snake_case_				:							Dict						 =       {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
		training_function(_a  ,      _a    )


if __name__ == "__main__":
							main()








import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowercase  :							Optional[Any]			  =    logging.getLogger(__name__)




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   str
						A     :   str
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   List[int]
						A     :   Optional[List[int]]						=					None
						A     :   Optional[List[int]]						=					None
						A     :   Optional[Union[int, float]]						=					None
						A     :   Optional[int]						=					None


if is_torch_available():
							import torch
							from torch.utils.data import Dataset




							class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


													'''simple docstring'''




													A     :   List[InputFeatures]






													def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE = False		,				) ->				Union[str, Any]:
															snake_case_				:							str						 =       hans_processors[task]()

															snake_case_				:							Tuple						 =       os.path.join(
															    _SCREAMING_SNAKE_CASE		,				"cached_{}_{}_{}_{}".format(
															        "dev" if evaluate else "train"		,				tokenizer.__class__.__name__		,				str(_SCREAMING_SNAKE_CASE				)		,				_SCREAMING_SNAKE_CASE		,				)		,				)
															snake_case_				:							Dict						 =       processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	snake_case_ ,       snake_case_				:							Any						 =       label_list[2], label_list[1]
															snake_case_				:							Tuple						 =       label_list

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															snake_case_				:							Dict						 =       cached_features_file + ".lock"
															with FileLock(_SCREAMING_SNAKE_CASE				):
																	if os.path.exists(_SCREAMING_SNAKE_CASE				) and not overwrite_cache:
																			logger.info(f'''Loading features from cached file {cached_features_file}'''				)
																			snake_case_				:							Dict						 =       torch.load(_SCREAMING_SNAKE_CASE				)
																	else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''				)

																			snake_case_				:							Any						 =       (
																			    processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
																			)

																			logger.info("Training examples: %s"		,				len(_SCREAMING_SNAKE_CASE				)				)
																			snake_case_				:							Union[str, Any]						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
																			logger.info("Saving features into cached file %s"		,				_SCREAMING_SNAKE_CASE				)
																			torch.save(self.features		,				_SCREAMING_SNAKE_CASE				)






													def __len__(	self				) ->				Tuple:
															return len(self.features				)






													def __getitem__(	self		,				_SCREAMING_SNAKE_CASE				) ->				InputFeatures:
															return self.features[i]






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list


if is_tf_available():
							import tensorflow as tf




							class 		UpperCAmelCase_						:


													'''simple docstring'''




													A     :   List[InputFeatures]






													def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 128		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE = False		,				) ->				Optional[int]:
															snake_case_				:							Tuple						 =       hans_processors[task]()
															snake_case_				:							Dict						 =       processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	snake_case_ ,       snake_case_				:							List[str]						 =       label_list[2], label_list[1]
															snake_case_				:							Dict						 =       label_list

															snake_case_				:							int						 =       processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
															snake_case_				:							str						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

															def gen():
																	for ex_index, ex in tqdm.tqdm(enumerate(self.features				)		,				desc="convert examples to features"				):
																			if ex_index % 1_0000 == 0:
																					logger.info("Writing example %d of %d" % (ex_index, len(_SCREAMING_SNAKE_CASE				))				)

																			yield (
																			    {
																			        "example_id": 0,
																			        "input_ids": ex.input_ids,
																			        "attention_mask": ex.attention_mask,
																			        "token_type_ids": ex.token_type_ids,
																			    },
																			    ex.label,
																			)

															snake_case_				:							List[Any]						 =       tf.data.Dataset.from_generator(
															    _SCREAMING_SNAKE_CASE		,				(
															        {
															            "example_id": tf.intaa,
															            "input_ids": tf.intaa,
															            "attention_mask": tf.intaa,
															            "token_type_ids": tf.intaa,
															        },
															        tf.intaa,
															    )		,				(
															        {
															            "example_id": tf.TensorShape([]				),
															            "input_ids": tf.TensorShape([None, None]				),
															            "attention_mask": tf.TensorShape([None, None]				),
															            "token_type_ids": tf.TensorShape([None, None]				),
															        },
															        tf.TensorShape([]				),
															    )		,				)






													def       _lowerCAmelCase       (	self				) ->				int:
															return self.dataset






													def __len__(	self				) ->				int:
															return len(self.features				)






													def __getitem__(	self		,				_SCREAMING_SNAKE_CASE				) ->				InputFeatures:
															return self.features[i]






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_train_set.txt"				)				)		,				"train"				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_evaluation_set.txt"				)				)		,				"dev"				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return ["contradiction", "entailment", "neutral"]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							Optional[Any]						 =       []
								for i, line in enumerate(_SCREAMING_SNAKE_CASE				):
										if i == 0:
												continue
										snake_case_				:							List[Any]						 =       "%s-%s" % (set_type, line[0])
										snake_case_				:							int						 =       line[5]
										snake_case_				:							str						 =       line[6]
										snake_case_				:							Any						 =       line[7][2:] if line[7].startswith("ex"				) else line[7]
										snake_case_				:							Dict						 =       line[0]
										examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE		,				text_a=_SCREAMING_SNAKE_CASE		,				text_b=_SCREAMING_SNAKE_CASE		,				label=_SCREAMING_SNAKE_CASE		,				pairID=_SCREAMING_SNAKE_CASE				)				)
								return examples




def      lowerCAmelCase__	(    _a       :			List[InputExample]  ,      _a       :			List[str]  ,      _a       :			int  ,      _a       :			PreTrainedTokenizer  ,      ):
		snake_case_				:							Optional[Any]						 =       {label: i for i, label in enumerate(_a    )}

		snake_case_				:							Optional[Any]						 =       []
		for ex_index, example in tqdm.tqdm(enumerate(_a    )  ,      desc="convert examples to features"    ):
				if ex_index % 1_00_00 == 0:
						logger.info("Writing example %d" % (ex_index)    )

				snake_case_				:							Optional[Any]						 =       tokenizer(
				    example.text_a  ,      example.text_b  ,      add_special_tokens=_a  ,      max_length=_a  ,      padding="max_length"  ,      truncation=_a  ,      return_overflowing_tokens=_a  ,      )

				snake_case_				:							Dict						 =       label_map[example.label] if example.label in label_map else 0

				snake_case_				:							Optional[Any]						 =       int(example.pairID    )

				features.append(InputFeatures(**_a  ,      label=_a  ,      pairID=_a    )    )

		for i, example in enumerate(examples[:5]    ):
				logger.info("*** Example ***"    )
				logger.info(F'''guid: {example}'''    )
				logger.info(F'''features: {features[i]}'''    )

		return features


lowercase  :							List[str]			  =    {
    '''hans''': 3,
}

lowercase  :							int			  =    {
    '''hans''': HansProcessor,
}
