



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


__SCREAMING_SNAKE_CASE           =  16
__SCREAMING_SNAKE_CASE           =  32




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase = 16 ,    _lowerCamelCase = "bert-base-cased"    ):
			A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			A							:					Optional[int]              =						load_dataset("glue" ,    "mrpc"    )

			def tokenize_function(_lowerCamelCase    ):
						# max_length=None => use the model max length (it's actually the default)
						A							:					Optional[Any]              =						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
			A							:					str              =						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
			A							:					List[str]              =						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=128 ,    return_tensors="pt"    )
						return tokenizer.pad(_lowerCamelCase ,    padding="longest" ,    return_tensors="pt"    )

			# Instantiate dataloaders.
			A							:					Any              =						DataLoader(
			    tokenized_datasets["train"] ,    shuffle=_lowerCamelCase ,    collate_fn=_lowerCamelCase ,    batch_size=_lowerCamelCase    )
			A							:					int              =						DataLoader(
			    tokenized_datasets["validation"] ,    shuffle=_lowerCamelCase ,    collate_fn=_lowerCamelCase ,    batch_size=_lowerCamelCase    )

			return train_dataloader, eval_dataloader




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			# Initialize accelerator
			A							:					Optional[int]              =						Accelerator()

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

			set_seed(_lowerCamelCase    )
			A       , A							:					str              =						get_dataloaders(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

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

			# Instantiate optimizer
			A							:					int              =						(
			    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=_lowerCamelCase    )

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

			# We need to keep track of how many total steps we have iterated over
			A							:					Optional[int]              =						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							:					Tuple              =						evaluate.load("glue" ,    "mrpc"    )
			A							:					Dict              =						0
			A							:					Optional[Any]              =						{}
			for epoch in range(_lowerCamelCase ,    _lowerCamelCase    ):
						model.train()
						for step, batch in enumerate(_lowerCamelCase    ):
									A							:					Optional[Any]              =						model(**_lowerCamelCase    )
									A							:					Optional[int]              =						outputs.loss
									A							:					Tuple              =						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()
						A							:					str              =						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():
												A							:					Optional[Any]              =						model(**_lowerCamelCase    )
									A							:					Dict              =						outputs.logits.argmax(dim=-1    )
									# It is slightly faster to call this once, than multiple times
									A       , A							:					Optional[Any]              =						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:
															A							:					Tuple              =						predictions[: len(eval_dataloader.dataset    ) - samples_seen]
															A							:					Optional[Any]              =						references[: len(eval_dataloader.dataset    ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=_lowerCamelCase ,    references=_lowerCamelCase ,    )

						A							:					Any              =						metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:""" ,    _lowerCamelCase    )
						A							:					List[str]              =						eval_metric["accuracy"]

						if best_performance < eval_metric["accuracy"]:
									A							:					Union[str, Any]              =						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 	UpperCAmelCase   (						):
			A							:					Union[str, Any]              =						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." ,    )
			A							:					Dict              =						parser.parse_args()
			A							:					Tuple              =						{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
			training_function(_lowerCamelCase ,    _lowerCamelCase    )


if __name__ == "__main__":
							main()




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith("roberta."    ):
									A							:					int              =						"roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
									continue

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--checkpoint-repo""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)