




"""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  :			List[Any]        =       16
_lowerCamelCase  :			List[Any]        =       32

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = 16  ,      _UpperCAmelCase = "bert-base-cased"	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		AutoTokenizer.from_pretrained(_UpperCAmelCase	)
	A_  :				Optional[int]	   =		load_dataset('''glue'''  ,      '''mrpc'''	)

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

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

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

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

	# Instantiate dataloaders.
	A_  :				Any	   =		DataLoader(
	    tokenized_datasets['''train''']  ,      shuffle=_UpperCAmelCase  ,      collate_fn=_UpperCAmelCase  ,      batch_size=_UpperCAmelCase	)
	A_  :				Any	   =		DataLoader(
	    tokenized_datasets['''validation''']  ,      shuffle=_UpperCAmelCase  ,      collate_fn=_UpperCAmelCase  ,      batch_size=_UpperCAmelCase	)

	return train_dataloader, eval_dataloader

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Tuple	   =		Accelerator()

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

	set_seed(_UpperCAmelCase	)
	A_  :				Optional[int]	   =		get_dataloaders(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

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

	# 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_  :				Any	   =		optimizer_cls(params=model.parameters()  ,      lr=_UpperCAmelCase	)

	if accelerator.state.deepspeed_plugin is not None:
		A_  :				Optional[Any]	   =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		A_  :				Any	   =		1
	A_  :				Optional[int]	   =		(len(_UpperCAmelCase	) * 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_  :				Union[str, Any]	   =		get_linear_schedule_with_warmup(
		    optimizer=_UpperCAmelCase  ,      num_warmup_steps=0  ,      num_training_steps=_UpperCAmelCase  ,      )
	else:
		A_  :				str	   =		DummyScheduler(_UpperCAmelCase  ,      total_num_steps=_UpperCAmelCase  ,      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_  :				Optional[Any]	   =		accelerator.prepare(
	    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)

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

	# Now we train the model
	A_  :				List[Any]	   =		evaluate.load('''glue'''  ,      '''mrpc'''	)
	A_  :				Dict	   =		0
	A_  :				List[str]	   =		{}
	for epoch in range(_UpperCAmelCase  ,      _UpperCAmelCase	):
		model.train()
		for step, batch in enumerate(_UpperCAmelCase	):
			A_  :				Optional[int]	   =		model(**_UpperCAmelCase	)
			A_  :				Optional[Any]	   =		outputs.loss
			A_  :				Optional[Any]	   =		loss / gradient_accumulation_steps
			accelerator.backward(_UpperCAmelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

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

		A_  :				Optional[int]	   =		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""  ,      _UpperCAmelCase	)
		A_  :				List[str]	   =		eval_metric['''accuracy''']

		if best_performance < eval_metric["accuracy"]:
			A_  :				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(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
	parser.add_argument(
	    '''--model_name_or_path'''  ,      type=_UpperCAmelCase  ,      default='''bert-base-cased'''  ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,      required=_UpperCAmelCase  ,      )
	parser.add_argument(
	    '''--output_dir'''  ,      type=_UpperCAmelCase  ,      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=_UpperCAmelCase  ,      default=_UpperCAmelCase  ,      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=_UpperCAmelCase  ,      default=3  ,      help='''Number of train epochs.'''  ,      )
	A_  :				List[Any]	   =		parser.parse_args()
	A_  :				str	   =		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
	training_function(_UpperCAmelCase  ,      _UpperCAmelCase	)


if __name__ == "__main__":
		main()











"""simple docstring"""

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)

_lowerCamelCase  :			str        =       {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   """umt5"""
				__lowerCAmelCase    :    List[str]         =   ["""past_key_values"""]







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					Union[str, Any]=25_01_12      ,     _lowerCamelCase   :					Any=5_12      ,     _lowerCamelCase   :					Optional[int]=64      ,     _lowerCamelCase   :					str=10_24      ,     _lowerCamelCase   :					List[str]=8      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					Any=6      ,     _lowerCamelCase   :					List[Any]=32      ,     _lowerCamelCase   :					Optional[Any]=1_28      ,     _lowerCamelCase   :					int=0.1      ,     _lowerCamelCase   :					Union[str, Any]=1E-6      ,     _lowerCamelCase   :					Tuple=1.0      ,     _lowerCamelCase   :					Optional[int]="gated-gelu"      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Tuple="T5Tokenizer"      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					Optional[int]=0      ,     _lowerCamelCase   :					Tuple=1      ,     _lowerCamelCase   :					Tuple=0      ,     **_lowerCamelCase   :					List[str]      ,     ):



					"""simple docstring"""

					super().__init__(
					    is_encoder_decoder=_lowerCamelCase      ,     tokenizer_class=_lowerCamelCase      ,     tie_word_embeddings=_lowerCamelCase      ,     pad_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     decoder_start_token_id=_lowerCamelCase      ,     **_lowerCamelCase      ,     )
					A_  :				str	   =		vocab_size
					A_  :				List[Any]	   =		d_model
					A_  :				Optional[int]	   =		d_kv
					A_  :				int	   =		d_ff
					A_  :				Union[str, Any]	   =		num_layers
					A_  :				Optional[Any]	   =		(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					A_  :				Dict	   =		num_heads
					A_  :				Optional[int]	   =		relative_attention_num_buckets
					A_  :				Union[str, Any]	   =		relative_attention_max_distance
					A_  :				Any	   =		dropout_rate
					A_  :				Optional[int]	   =		layer_norm_epsilon
					A_  :				Tuple	   =		initializer_factor
					A_  :				Optional[int]	   =		feed_forward_proj
					A_  :				Dict	   =		use_cache

					A_  :				Any	   =		self.feed_forward_proj.split('''-'''       )
					A_  :				Tuple	   =		act_info[-1]
					A_  :				Any	   =		act_info[0] == '''gated'''

					if len(_lowerCamelCase       ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase       ) > 2:
						raise ValueError(
						    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
						    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
						    '''\'gated-gelu\' or \'relu\''''       )

					if feed_forward_proj == "gated-gelu":
						A_  :				Dict	   =		'''gelu_new'''







				@property
				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					return self.d_model







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					return self.num_heads







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					return self.num_layers

class        lowercase      (    __UpperCAmelCase):







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		{
					    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
					    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
					}
					if self.use_past:
						A_  :				Optional[Any]	   =		'''past_encoder_sequence + sequence'''
						A_  :				Optional[Any]	   =		{0: '''batch'''}
						A_  :				List[Any]	   =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
						A_  :				Optional[int]	   =		{0: '''batch''', 1: '''decoder_sequence'''}
						A_  :				List[str]	   =		{0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
						self.fill_with_past_key_values_(_lowerCamelCase      ,     direction='''inputs'''       )

					return common_inputs







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					return 13







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 5E-4





