
"""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				 =					16
_lowercase				 =					32






def    _snake_case					(		snake_case__ :  Accelerator						,   snake_case__ :  int = 16						,   snake_case__ :  str = "bert-base-cased"  ):
	A     = AutoTokenizer.from_pretrained(snake_case__  )
	A     = load_dataset('glue'						,   'mrpc'  )

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

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

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

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

	# Instantiate dataloaders.
	A     = DataLoader(
	    tokenized_datasets['train']						,   shuffle=snake_case__						,   collate_fn=snake_case__						,   batch_size=snake_case__  )
	A     = DataLoader(
	    tokenized_datasets['validation']						,   shuffle=snake_case__						,   collate_fn=snake_case__						,   batch_size=snake_case__  )

	return train_dataloader, eval_dataloader






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  Union[str, Any]  ):
	# Initialize accelerator
	A     = Accelerator()

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

	set_seed(snake_case__  )
	A ,   A     = get_dataloaders(snake_case__						,   snake_case__						,   snake_case__  )

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

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

	if accelerator.state.deepspeed_plugin is not None:
		A     = accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		A     = 1
	A     = (len(snake_case__  ) * 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     = get_linear_schedule_with_warmup(
		    optimizer=snake_case__						,   num_warmup_steps=0						,   num_training_steps=snake_case__						,   )
	else:
		A     = DummyScheduler(snake_case__						,   total_num_steps=snake_case__						,   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     = accelerator.prepare(
	    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__  )

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

	# Now we train the model
	A     = evaluate.load('glue'						,   'mrpc'  )
	A     = 0
	A     = {}
	for epoch in range(snake_case__						,   snake_case__  ):
		model.train()
		for step, batch in enumerate(snake_case__  ):
			A     = model(**snake_case__  )
			A     = outputs.loss
			A     = loss / gradient_accumulation_steps
			accelerator.backward(snake_case__  )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

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

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

		if best_performance < eval_metric["accuracy"]:
			A     = 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(snake_case__						,   snake_case__  )






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


if __name__ == "__main__":
		main()

"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MCTCTForCTC''',
		    '''MCTCTModel''',
		    '''MCTCTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)