
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 							__A(lowerCAmelCase						,  lowerCAmelCase = 1_6						,  lowerCAmelCase = "bert-base-cased" )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	AutoTokenizer.from_pretrained(lowerCAmelCase )
		_UpperCamelCase						    =	load_dataset("""glue"""						,  """mrpc""" )

		def tokenize_function(lowerCAmelCase ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCamelCase						    =	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
		_UpperCamelCase						    =	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
		_UpperCamelCase						    =	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=1_2_8						,  return_tensors="""pt""" )
				return tokenizer.pad(lowerCAmelCase						,  padding="""longest"""						,  return_tensors="""pt""" )

		# Instantiate dataloaders.
		_UpperCamelCase						    =	DataLoader(
		    tokenized_datasets["""train"""]						,  shuffle=lowerCAmelCase						,  collate_fn=lowerCAmelCase						,  batch_size=lowerCAmelCase )
		_UpperCamelCase						    =	DataLoader(
		    tokenized_datasets["""validation"""]						,  shuffle=lowerCAmelCase						,  collate_fn=lowerCAmelCase						,  batch_size=lowerCAmelCase )

		return train_dataloader, eval_dataloader


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	Accelerator()

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

		set_seed(lowerCAmelCase )
		_UpperCamelCase    ,	_UpperCamelCase						    =	get_dataloaders(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

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

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

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCamelCase						    =	accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				_UpperCamelCase						    =	1
		_UpperCamelCase						    =	(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
		):
				_UpperCamelCase						    =	get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase						,  num_warmup_steps=0						,  num_training_steps=lowerCAmelCase						,  )
		else:
				_UpperCamelCase						    =	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.
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	accelerator.prepare(
		    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

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

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

						overall_step += 1

				model.eval()
				_UpperCamelCase						    =	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():
								_UpperCamelCase						    =	model(**lowerCAmelCase )
						_UpperCamelCase						    =	outputs.logits.argmax(dim=-1 )
						# It is slightly faster to call this once, than multiple times
						_UpperCamelCase    ,	_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	predictions[: len(eval_dataloader.dataset ) - samples_seen]
										_UpperCamelCase						    =	references[: len(eval_dataloader.dataset ) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=lowerCAmelCase						,  references=lowerCAmelCase						,  )

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

				if best_performance < eval_metric["accuracy"]:
						_UpperCamelCase						    =	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(lowerCAmelCase						,  lowerCAmelCase )


def 							__A()		->				Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	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(
		    """--performance_lower_bound"""						,  type=lowerCAmelCase						,  default=lowerCAmelCase						,  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=lowerCAmelCase						,  default=3						,  help="""Number of train epochs."""						,  )
		_UpperCamelCase						    =	parser.parse_args()
		_UpperCamelCase						    =	{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
		training_function(lowerCAmelCase						,  lowerCAmelCase )


if __name__ == "__main__":
		main()





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  max_position_embeddings=args.max_target_positions						,  num_layers=args.decoder_layers						,  attention_heads=args.decoder_attention_heads						,  ffn_dim=args.decoder_ffn_embed_dim						,  d_model=args.decoder_embed_dim						,  layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="""gelu"""						,  scale_embedding=not args.no_scale_embedding						,  tie_word_embeddings=args.share_decoder_input_output_embed						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



