
import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase											=			16
_lowercase											=			32






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

	def tokenize_function(snake_case__				:       str       ):
		# 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
	# starting with the main process first:
	with accelerator.main_process_first():
		A  				=				datasets.map(
		    snake_case__		, batched=snake_case__		, remove_columns=['idx', 'sentence1', 'sentence2']		, )

	# 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__				:       Optional[Any]       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A  				=				128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A  				=				16
		elif accelerator.mixed_precision != "no":
			A  				=				8
		else:
			A  				=				None

		return tokenizer.pad(
		    snake_case__		, padding='longest'		, max_length=snake_case__		, pad_to_multiple_of=snake_case__		, 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


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		_lowercase											=			mocked_dataloaders  # noqa: F811






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       Optional[Any]       ):
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'		, snake_case__       ) == "1":
		A  				=				2
	# Initialize accelerator
	A  				=				Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision       )
	# 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']       )

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps		, 'isdigit'       ):
		if args.checkpointing_steps == "epoch":
			A  				=				args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			A  				=				int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'       )
	else:
		A  				=				None

	set_seed(snake_case__       )

	A				, A  				=				get_dataloaders(snake_case__		, snake_case__       )
	A  				=				evaluate.load('glue'		, 'mrpc'       )

	# If the batch size is too big we use gradient accumulation
	A  				=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A  				=				batch_size // MAX_GPU_BATCH_SIZE
		A  				=				MAX_GPU_BATCH_SIZE

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

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A  				=				model.to(accelerator.device       )

	# Instantiate optimizer
	A  				=				AdamW(params=model.parameters()		, lr=snake_case__       )

	# Instantiate scheduler
	A  				=				get_linear_schedule_with_warmup(
	    optimizer=snake_case__		, num_warmup_steps=100		, num_training_steps=(len(snake_case__       ) * num_epochs) // gradient_accumulation_steps		, )

	# 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__       )

	# New Code #
	# 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

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'       )
			accelerator.load_state(args.resume_from_checkpoint       )
			A  				=				os.path.basename(args.resume_from_checkpoint       )
		else:
			# Get the most recent checkpoint
			A  				=				[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
			dirs.sort(key=os.path.getctime       )
			A  				=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		A  				=				os.path.splitext(snake_case__       )[0]

		if "epoch" in training_difference:
			A  				=				int(training_difference.replace('epoch_'		, ''       )       ) + 1
			A  				=				None
		else:
			A  				=				int(training_difference.replace('step_'		, ''       )       )
			A  				=				resume_step // len(snake_case__       )
			resume_step -= starting_epoch * len(snake_case__       )

    # Now we train the model
	for epoch in range(snake_case__		, snake_case__       ):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			A  				=				accelerator.skip_first_batches(snake_case__		, snake_case__       )
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			A  				=				train_dataloader
		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       )
			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()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(snake_case__		, snake_case__       ):
				A  				=				F'step_{overall_step}'
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A  				=				os.path.join(args.output_dir		, snake_case__       )
					accelerator.save_state(snake_case__       )

		model.eval()
		for step, batch in enumerate(snake_case__       ):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device       )
			with torch.no_grad():
				A  				=				model(**snake_case__       )
			A  				=				outputs.logits.argmax(dim=-1       )
			A				, A  				=				accelerator.gather_for_metrics((predictions, batch['labels'])       )
			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__       )

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			A  				=				F'epoch_{epoch}'
			if args.output_dir is not None:
				A  				=				os.path.join(args.output_dir		, snake_case__       )
			accelerator.save_state(snake_case__       )






def  _snake_case			(						):
	A  				=				argparse.ArgumentParser(description='Simple example of training script.'       )
	parser.add_argument(
	    '--mixed_precision'		, type=snake_case__		, default=snake_case__		, choices=['no', 'fp16', 'bf16', 'fp8']		, help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.'		, )
	parser.add_argument('--cpu'		, action='store_true'		, help='If passed, will train on the CPU.'       )
	parser.add_argument(
	    '--checkpointing_steps'		, type=snake_case__		, default=snake_case__		, help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		, )
	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(
	    '--resume_from_checkpoint'		, type=snake_case__		, default=snake_case__		, help='If the training should continue from a checkpoint folder.'		, )
	A  				=				parser.parse_args()
	A  				=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__		, snake_case__       )


if __name__ == "__main__":
		main()

"""simple docstring"""


from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							A  				=				vocab_size
							A  				=				hidden_size
							A  				=				encoder_ffn_dim
							A  				=				num_encoder_layers
							A  				=				num_encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				num_decoder_layers
							A  				=				num_decoder_attention_heads
							A  				=				max_position_embeddings
							A  				=				init_std  # Normal(0, this parameter)
							A  				=				activation_function

							# parameters for prophetnet
							A  				=				ngram
							A  				=				num_buckets
							A  				=				relative_max_distance
							A  				=				disable_ngram_loss
							A  				=				eps

							# 3 Types of Dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				dropout

							A  				=				use_cache

							super().__init__(
							    pad_token_id=A_					,bos_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,add_cross_attention=A_					,decoder_start_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )