




"""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


a_				:    str										=     16
a_				:    Dict										=     32





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

			def tokenize_function(A__:    Optional[Any]     ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCamelCase					:	Tuple	       =				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
			__lowerCamelCase					:	int	       =				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
			__lowerCamelCase					:	Any	       =				tokenized_datasets.rename_column('label'							,				'labels'     )

			def collate_fn(A__:    int     ):
						# 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.
			__lowerCamelCase					:	Any	       =				DataLoader(
			    tokenized_datasets['train']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=A__     )
			__lowerCamelCase					:	Any	       =				DataLoader(
			    tokenized_datasets['validation']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=A__     )

			return train_dataloader, eval_dataloader





def 						UpperCAmelCase				(			A__:    int							,				A__:    Tuple     )  ->     Optional[int]:
			# Initialize accelerator
			__lowerCamelCase					:	str	       =				Accelerator()

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

			set_seed(A__     )
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				get_dataloaders(A__							,				A__							,				A__     )

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

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

			if accelerator.state.deepspeed_plugin is not None:
						__lowerCamelCase					:	Optional[int]	       =				accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						__lowerCamelCase					:	Tuple	       =				1
			__lowerCamelCase					:	Any	       =				(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
			):
						__lowerCamelCase					:	Union[str, Any]	       =				get_linear_schedule_with_warmup(
						    optimizer=A__							,				num_warmup_steps=0							,				num_training_steps=A__							,				)
			else:
						__lowerCamelCase					:	Dict	       =				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.
			__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[Any]	       =				accelerator.prepare(
			    A__							,				A__							,				A__							,				A__							,				A__     )

			# We need to keep track of how many total steps we have iterated over
			__lowerCamelCase					:	List[str]	       =				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					:	Dict	       =				evaluate.load('glue'							,				'mrpc'     )
			__lowerCamelCase					:	Optional[Any]	       =				0
			__lowerCamelCase					:	str	       =				{}
			for epoch in range(A__							,				A__     ):
						model.train()
						for step, batch in enumerate(A__     ):
									__lowerCamelCase					:	Any	       =				model(**A__     )
									__lowerCamelCase					:	int	       =				outputs.loss
									__lowerCamelCase					:	Optional[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()
						__lowerCamelCase					:	Union[str, 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():
												__lowerCamelCase					:	Optional[int]	       =				model(**A__     )
									__lowerCamelCase					:	List[Any]	       =				outputs.logits.argmax(dim=-1     )
									# It is slightly faster to call this once, than multiple times
									__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				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:
															__lowerCamelCase					:	str	       =				predictions[: len(eval_dataloader.dataset     ) - samples_seen]
															__lowerCamelCase					:	List[Any]	       =				references[: len(eval_dataloader.dataset     ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=A__							,				references=A__							,				)

						__lowerCamelCase					:	Tuple	       =				metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''							,				A__     )
						__lowerCamelCase					:	Optional[int]	       =				eval_metric['accuracy']

						if best_performance < eval_metric["accuracy"]:
									__lowerCamelCase					:	str	       =				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 						UpperCAmelCase				(			)  ->     Optional[Any]:
			__lowerCamelCase					:	Optional[Any]	       =				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.'							,				)
			__lowerCamelCase					:	Union[str, Any]	       =				parser.parse_args()
			__lowerCamelCase					:	Optional[Any]	       =				{'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 DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
					    DebertaVaForMaskedLM,
					    DebertaVaForMultipleChoice,
					    DebertaVaForQuestionAnswering,
					    DebertaVaForSequenceClassification,
					    DebertaVaForTokenClassification,
					    DebertaVaModel,
					)
					from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=True						,	__a=True						,	__a=True						,	__a=99						,	__a=32						,	__a=5						,	__a=4						,	__a=37						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=512						,	__a=16						,	__a=2						,	__a=0.02						,	__a=False						,	__a=True						,	__a="None"						,	__a=3						,	__a=4						,	__a=None						,	):
								__lowerCamelCase					:	List[str]	       =				parent
								__lowerCamelCase					:	Dict	       =				batch_size
								__lowerCamelCase					:	str	       =				seq_length
								__lowerCamelCase					:	Optional[int]	       =				is_training
								__lowerCamelCase					:	Dict	       =				use_input_mask
								__lowerCamelCase					:	Dict	       =				use_token_type_ids
								__lowerCamelCase					:	Dict	       =				use_labels
								__lowerCamelCase					:	Optional[Any]	       =				vocab_size
								__lowerCamelCase					:	Any	       =				hidden_size
								__lowerCamelCase					:	List[Any]	       =				num_hidden_layers
								__lowerCamelCase					:	Tuple	       =				num_attention_heads
								__lowerCamelCase					:	Any	       =				intermediate_size
								__lowerCamelCase					:	Optional[Any]	       =				hidden_act
								__lowerCamelCase					:	Any	       =				hidden_dropout_prob
								__lowerCamelCase					:	Optional[Any]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	List[Any]	       =				max_position_embeddings
								__lowerCamelCase					:	Optional[Any]	       =				type_vocab_size
								__lowerCamelCase					:	Dict	       =				type_sequence_label_size
								__lowerCamelCase					:	Any	       =				initializer_range
								__lowerCamelCase					:	Union[str, Any]	       =				num_labels
								__lowerCamelCase					:	Tuple	       =				num_choices
								__lowerCamelCase					:	str	       =				relative_attention
								__lowerCamelCase					:	Optional[int]	       =				position_biased_input
								__lowerCamelCase					:	int	       =				pos_att_type
								__lowerCamelCase					:	str	       =				scope
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	int	       =				None
								if self.use_input_mask:
											__lowerCamelCase					:	int	       =				ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2     )

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

								__lowerCamelCase					:	Optional[Any]	       =				None
								__lowerCamelCase					:	Optional[Any]	       =				None
								__lowerCamelCase					:	int	       =				None
								if self.use_labels:
											__lowerCamelCase					:	Optional[int]	       =				ids_tensor([self.batch_size]						,	self.type_sequence_label_size     )
											__lowerCamelCase					:	Optional[int]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels     )
											__lowerCamelCase					:	List[Any]	       =				ids_tensor([self.batch_size]						,	self.num_choices     )

								__lowerCamelCase					:	Optional[Any]	       =				self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def     snake_case_				(    self     ):
								return DebertaVaConfig(
								    vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	initializer_range=self.initializer_range						,	relative_attention=self.relative_attention						,	position_biased_input=self.position_biased_input						,	pos_att_type=self.pos_att_type						,	)
					def     snake_case_				(    self						,	__a     ):
								self.parent.assertListEqual(list(result.loss.size()     )						,	[]     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	int	       =				DebertaVaModel(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a     )[0]
								__lowerCamelCase					:	str	       =				model(__a						,	token_type_ids=__a     )[0]
								__lowerCamelCase					:	Optional[Any]	       =				model(__a     )[0]

								self.parent.assertListEqual(list(sequence_output.size()     )						,	[self.batch_size, self.seq_length, self.hidden_size]     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	List[str]	       =				DebertaVaForMaskedLM(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )

								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				self.num_labels
								__lowerCamelCase					:	List[Any]	       =				DebertaVaForSequenceClassification(__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Optional[Any]	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )
								self.parent.assertListEqual(list(result.logits.size()     )						,	[self.batch_size, self.num_labels]     )
								self.check_loss_output(__a     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	int	       =				self.num_labels
								__lowerCamelCase					:	Dict	       =				DebertaVaForTokenClassification(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	int	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				DebertaVaForQuestionAnswering(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Any	       =				model(
								    __a						,	attention_mask=__a						,	token_type_ids=__a						,	start_positions=__a						,	end_positions=__a						,	)
								self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)     )
								self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Any	       =				DebertaVaForMultipleChoice(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Union[str, Any]	       =				input_ids.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	Optional[int]	       =				token_type_ids.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	int	       =				input_mask.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	List[Any]	       =				model(
								    __a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a						,	)
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.prepare_config_and_inputs()
								(
								    (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      
								)					:	List[str]	       =				config_and_inputs
								__lowerCamelCase					:	int	       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= (
					    (
					        DebertaVaModel,
					        DebertaVaForMaskedLM,
					        DebertaVaForSequenceClassification,
					        DebertaVaForTokenClassification,
					        DebertaVaForQuestionAnswering,
					        DebertaVaForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					__a						:						Tuple     		= (
					    {
					        'feature-extraction': DebertaVaModel,
					        'fill-mask': DebertaVaForMaskedLM,
					        'question-answering': DebertaVaForQuestionAnswering,
					        'text-classification': DebertaVaForSequenceClassification,
					        'token-classification': DebertaVaForTokenClassification,
					        'zero-shot': DebertaVaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					__a						:						str     		= True
					__a						:						Dict     		= False
					__a						:						Tuple     		= False
					__a						:						Optional[Any]     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				DebertaVaModelTester(self     )
								__lowerCamelCase					:	Tuple	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_sequence_classification(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_masked_lm(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_question_answering(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_token_classification(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_multiple_choice(*__a     )





					@slow
					def     snake_case_				(    self     ):
								for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase					:	Tuple	       =				DebertaVaModel.from_pretrained(__a     )
											self.assertIsNotNone(__a     )



@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@unittest.skip(reason='Model not available yet'     )
					def     snake_case_				(    self     ):
								pass





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'     )

								__lowerCamelCase					:	Any	       =				torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     )
								__lowerCamelCase					:	Any	       =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
								with torch.no_grad():
											__lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	attention_mask=__a     )[0]
								# compare the actual values for a slice.
								__lowerCamelCase					:	str	       =				torch.tensor(
								    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]     )
								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,	__a						,	atol=1E-4     )						,	f'''{output[:, 1:4, 1:4]}'''     )



