

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


UpperCAmelCase_							:					Optional[int]		 =   16
UpperCAmelCase_							:					List[Any]		 =   32
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Accelerator			,a__    :   int = 16			,a__    :   str = "bert-base-cased"       )					->				Any:
	__A :			Optional[Any]		    =			AutoTokenizer.from_pretrained(a__       )
	__A :			List[str]		    =			load_dataset("""glue"""			,"""mrpc"""       )

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

	def collate_fn(a__    :   str       ):
		# 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.
	__A :			int		    =			DataLoader(
	    tokenized_datasets["""train"""]			,shuffle=a__			,collate_fn=a__			,batch_size=a__       )
	__A :			str		    =			DataLoader(
	    tokenized_datasets["""validation"""]			,shuffle=a__			,collate_fn=a__			,batch_size=a__       )

	return train_dataloader, eval_dataloader
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Optional[Any]       )					->				int:
	# Initialize accelerator
	__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 :			List[Any]		    =			int(config["""seed"""]       )
	__A :			Optional[Any]		    =			int(config["""batch_size"""]       )
	__A :			Any		    =			args.model_name_or_path

	set_seed(a__       )
	__A    ,				__A :			Union[str, Any]		    =			get_dataloaders(a__			,a__			,a__       )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__A :			List[Any]		    =			AutoModelForSequenceClassification.from_pretrained(a__			,return_dict=a__       )

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

	if accelerator.state.deepspeed_plugin is not None:
		__A :			Optional[int]		    =			accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		__A :			List[Any]		    =			1
	__A :			Optional[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
	):
		__A :			Optional[int]		    =			get_linear_schedule_with_warmup(
		    optimizer=a__			,num_warmup_steps=0			,num_training_steps=a__			,)
	else:
		__A :			List[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.
	__A    ,				__A    ,				__A    ,				__A    ,				__A :			int		    =			accelerator.prepare(
	    a__			,a__			,a__			,a__			,a__       )

	# 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 :			List[Any]		    =			0

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

		__A :			Any		    =			metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""			,a__       )
		__A :			Tuple		    =			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(a__			,a__       )
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			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."""			,)
	__A :			Dict		    =			parser.parse_args()
	__A :			List[Any]		    =			{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(a__			,a__       )


if __name__ == "__main__":
			main()









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






