



'''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


__lowerCAmelCase		     =  16
__lowerCAmelCase		     =  32



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 16		,     lowerCAmelCase_ = "bert-base-cased"    )     -> List[Any]:
		_a	: Optional[int]								=				AutoTokenizer.from_pretrained(lowerCAmelCase_    )
		_a	: int								=				load_dataset('glue'		,     'mrpc'    )

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

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

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

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

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

		return train_dataloader, eval_dataloader



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Tuple:
		model.eval()
		_a	: Dict								=				0
		for step, batch in enumerate(lowerCAmelCase_    ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device    )
				with torch.no_grad():
						_a	: Any								=				model(**lowerCAmelCase_    )
				_a	: Optional[Any]								=				outputs.logits.argmax(dim=-1    )
				# It is slightly faster to call this once, than multiple times
				_a			,   _a	: 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(lowerCAmelCase_    ) - 1:
								_a	: int								=				predictions[: len(eval_dataloader.dataset    ) - samples_seen]
								_a	: Dict								=				references[: len(eval_dataloader.dataset    ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=lowerCAmelCase_		,     references=lowerCAmelCase_		,     )

		_a	: Optional[int]								=				metric.compute()
		return eval_metric["accuracy"]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> List[str]:
		# Initialize accelerator
		_a	: Optional[int]								=				Accelerator()

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

		set_seed(lowerCAmelCase_    )
		_a			,   _a	: Any								=				get_dataloaders(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_a	: Optional[int]								=				AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_		,     return_dict=lowerCAmelCase_    )

		# Instantiate optimizer
		_a	: Tuple								=				(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_a	: str								=				optimizer_cls(params=model.parameters()		,     lr=lowerCAmelCase_    )

		if accelerator.state.deepspeed_plugin is not None:
				_a	: str								=				accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				_a	: str								=				1
		_a	: Dict								=				(len(lowerCAmelCase_    ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_a	: Dict								=				get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_		,     num_warmup_steps=0		,     num_training_steps=lowerCAmelCase_		,     )
		else:
				_a	: str								=				DummyScheduler(lowerCAmelCase_		,     total_num_steps=lowerCAmelCase_		,     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.
		_a			,   _a			,   _a			,   _a			,   _a	: Union[str, Any]								=				accelerator.prepare(
		    lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

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

		if args.partial_train_epoch is not None:
				_a	: Union[str, Any]								=				args.partial_train_epoch

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

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

				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(lowerCAmelCase_		,     lowerCAmelCase_    )



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

		training_function(lowerCAmelCase_		,     lowerCAmelCase_    )


if __name__ == "__main__":
							main()









'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class       __magic_name__		:
							lowerCAmelCase			:       Optional[Any]  							=   MBartConfig
							lowerCAmelCase			:       str  							=   {}
							lowerCAmelCase			:       Dict  							=   'gelu'







							def __init__(				self   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Tuple=13      ,_UpperCAmelCase   :			str=7      ,_UpperCAmelCase   :			Dict=True      ,_UpperCAmelCase   :			Optional[int]=False      ,_UpperCAmelCase   :			List[str]=99      ,_UpperCAmelCase   :			str=32      ,_UpperCAmelCase   :			List[str]=2      ,_UpperCAmelCase   :			Optional[int]=4      ,_UpperCAmelCase   :			str=37      ,_UpperCAmelCase   :			Union[str, Any]=0.1      ,_UpperCAmelCase   :			str=0.1      ,_UpperCAmelCase   :			Dict=20      ,_UpperCAmelCase   :			Optional[Any]=2      ,_UpperCAmelCase   :			Union[str, Any]=1      ,_UpperCAmelCase   :			List[str]=0      ,):
									_a	: Any								=				parent
									_a	: List[Any]								=				batch_size
									_a	: Any								=				seq_length
									_a	: List[str]								=				is_training
									_a	: str								=				use_labels
									_a	: List[Any]								=				vocab_size
									_a	: List[str]								=				hidden_size
									_a	: Union[str, Any]								=				num_hidden_layers
									_a	: Optional[Any]								=				num_attention_heads
									_a	: Optional[int]								=				intermediate_size
									_a	: Any								=				hidden_dropout_prob
									_a	: int								=				attention_probs_dropout_prob
									_a	: Union[str, Any]								=				max_position_embeddings
									_a	: Union[str, Any]								=				eos_token_id
									_a	: int								=				pad_token_id
									_a	: Union[str, Any]								=				bos_token_id







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: str								=				ids_tensor([self.batch_size, self.seq_length - 1]      ,self.vocab_size						)
									_a	: List[str]								=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,1						)
									_a	: str								=				tf.concat([input_ids, eos_tensor]      ,axis=1						)

									_a	: Dict								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: List[str]								=				self.config_cls(
									    vocab_size=self.vocab_size      ,d_model=self.hidden_size      ,encoder_layers=self.num_hidden_layers      ,decoder_layers=self.num_hidden_layers      ,encoder_attention_heads=self.num_attention_heads      ,decoder_attention_heads=self.num_attention_heads      ,encoder_ffn_dim=self.intermediate_size      ,decoder_ffn_dim=self.intermediate_size      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,eos_token_ids=[2]      ,bos_token_id=self.bos_token_id      ,pad_token_id=self.pad_token_id      ,decoder_start_token_id=self.pad_token_id      ,**self.config_updates      ,)
									_a	: Optional[int]								=				prepare_mbart_inputs_dict(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)
									return config, inputs_dict







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[int]						):
									_a	: str								=				TFMBartModel(config=_UpperCAmelCase						).get_decoder()
									_a	: List[Any]								=				inputs_dict['input_ids']

									_a	: Optional[Any]								=				input_ids[:1, :]
									_a	: Tuple								=				inputs_dict['attention_mask'][:1, :]
									_a	: List[Any]								=				inputs_dict['head_mask']
									_a	: str								=				1

									# first forward pass
									_a	: Union[str, Any]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase      ,head_mask=_UpperCAmelCase      ,use_cache=_UpperCAmelCase						)

									_a			,   _a	: Optional[int]								=				outputs.to_tuple()
									_a	: Union[str, Any]								=				past_key_values[1]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     )     -> Union[str, Any]:
		if attention_mask is None:
				_a	: List[Any]								=				tf.cast(tf.math.not_equal(lowerCAmelCase_		,     config.pad_token_id    )		,     tf.inta    )
		if decoder_attention_mask is None:
				_a	: Any								=				tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape		,     dtype=tf.inta    ),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,     config.pad_token_id    )		,     tf.inta    ),
				    ]		,     axis=-1		,     )
		if head_mask is None:
				_a	: int								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				_a	: Any								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				_a	: int								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}

@require_tf
class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       List[str]  							=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCAmelCase			:       Optional[Any]  							=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase			:       Dict  							=   (
							    {
							        'conversational': TFMBartForConditionalGeneration,
							        'feature-extraction': TFMBartModel,
							        'summarization': TFMBartForConditionalGeneration,
							        'text2text-generation': TFMBartForConditionalGeneration,
							        'translation': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase			:       Dict  							=   True
							lowerCAmelCase			:       List[str]  							=   False
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			int						):
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				TFMBartModelTester(self						)
									_a	: Dict								=				ConfigTester(self      ,config_class=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase						)

@require_sentencepiece
@require_tokenizers
@require_tf
class       __magic_name__		(       unittest.TestCase  ):
							lowerCAmelCase			:       List[Any]  							=   [
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							lowerCAmelCase			:       Dict  							=   [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							lowerCAmelCase			:       Tuple  							=   'facebook/mbart-large-en-ro'







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									return AutoTokenizer.from_pretrained(self.model_name						)







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Any								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model







							def 				__lowercase     (				self   :			Optional[int]      ,**_UpperCAmelCase   :			Dict						):
									_a	: List[Any]								=				self.translate_src_text(**_UpperCAmelCase						)
									self.assertListEqual(self.expected_text      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple      ,**_UpperCAmelCase   :			str						):
									_a	: List[Any]								=				self.tokenizer(self.src_text      ,**_UpperCAmelCase      ,return_tensors='tf'						)
									_a	: Any								=				self.model.generate(
									    model_inputs.input_ids      ,attention_mask=model_inputs.attention_mask      ,num_beams=2						)
									_a	: Tuple								=				self.tokenizer.batch_decode(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)
									return generated_words







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									self._assert_generated_batch_equal_expected()




