
'''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


lowercase					:							List[str]   =      16
lowercase					:							Tuple   =      32







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A = 16    ,       __A = "bert-base-cased"			) ->			Optional[int]:
	_snake_case     		=	AutoTokenizer.from_pretrained(__A			)
	_snake_case     		=	load_dataset('glue'    ,       'mrpc'			)

	def tokenize_function(__A			):
		# max_length=None => use the model max length (it's actually the default)
		_snake_case     		=	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     		=	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     		=	tokenized_datasets.rename_column('label'    ,       'labels'			)

	def collate_fn(__A			):
		# 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.
	_snake_case     		=	DataLoader(
	    tokenized_datasets['train']    ,       shuffle=__A    ,       collate_fn=__A    ,       batch_size=__A			)
	_snake_case     		=	DataLoader(
	    tokenized_datasets['validation']    ,       shuffle=__A    ,       collate_fn=__A    ,       batch_size=__A			)

	return train_dataloader, eval_dataloader







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			str:
	# Initialize accelerator
	_snake_case     		=	Accelerator()

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

	set_seed(__A			)
	_snake_case		,				_snake_case     		=	get_dataloaders(__A    ,       __A    ,       __A			)

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

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

	if accelerator.state.deepspeed_plugin is not None:
		_snake_case     		=	accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		_snake_case     		=	1
	_snake_case     		=	(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     		=	get_linear_schedule_with_warmup(
		    optimizer=__A    ,       num_warmup_steps=0    ,       num_training_steps=__A    ,       )
	else:
		_snake_case     		=	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     		=	accelerator.prepare(
	    __A    ,       __A    ,       __A    ,       __A    ,       __A			)

	# We need to keep track of how many total steps we have iterated over
	_snake_case     		=	0
	# We also need to keep track of the stating epoch so files are named properly
	_snake_case     		=	0

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

		_snake_case     		=	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'    ,       __A			)
		_snake_case     		=	eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			_snake_case     		=	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        SCREAMING_SNAKE_CASE__			(     ) ->			int:
	_snake_case     		=	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     		=	parser.parse_args()
	_snake_case     		=	{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
	training_function(__A    ,       __A			)


if __name__ == "__main__":
					main()



'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=32					,     lowerCAmelCase_=5					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	seq_length
								_snake_case     		=	is_training
								_snake_case     		=	use_token_type_ids
								_snake_case     		=	use_labels
								_snake_case     		=	vocab_size
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	self.vocab_size - 1


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size						)

								_snake_case     		=	None
								if self.use_token_type_ids:
									_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size						)

								_snake_case     		=	None
								_snake_case     		=	None
								_snake_case     		=	None
								if self.use_labels:
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
									_snake_case     		=	ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels						)
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.num_choices						)

								_snake_case     		=	OpenAIGPTConfig(
								    vocab_size=self.vocab_size					,     n_embd=self.hidden_size					,     n_layer=self.num_hidden_layers					,     n_head=self.num_attention_heads					,     n_positions=self.max_position_embeddings					,     pad_token_id=self.pad_token_id					,     )

								_snake_case     		=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,     2						)

								return (
								    config,
								    input_ids,
								    head_mask,
								    token_type_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     head_mask=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)

								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTDoubleHeadsModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	OpenAIGPTForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				(
								    _snake_case
								)		,				
								)     		=	config_and_inputs
								_snake_case     		=	{
								    'input_ids': input_ids,
								    'token_type_ids': token_type_ids,
								    'head_mask': head_mask,
								}

								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							__lowercase   	= (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							__lowercase   	= (
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
									# tiny config could not be created.
									return True

								return False


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	super()._prepare_for_class(lowerCAmelCase_					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)

								if return_labels:
									if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowerCAmelCase_						)
								return inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     n_embd=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_						)




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	OpenAIGPTModel.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)


@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'						)
								model.to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([[4_81, 47_35, 5_44]]					,     dtype=torch.long					,     device=lowerCAmelCase_						)  # the president is
								_snake_case     		=	[
								    4_81,
								    47_35,
								    5_44,
								    2_46,
								    9_63,
								    8_70,
								    7_62,
								    2_39,
								    2_44,
								    4_04_77,
								    2_44,
								    2_49,
								    7_19,
								    8_81,
								    4_87,
								    5_44,
								    2_40,
								    2_44,
								    6_03,
								    4_81,
								]  # the president is a very good man. " \n " i\'m sure he is, " said the

								_snake_case     		=	model.generate(lowerCAmelCase_					,     do_sample=lowerCAmelCase_						)
								self.assertListEqual(output_ids[0].tolist()					,     lowerCAmelCase_						)

