





'''simple docstring'''






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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


A_		:  Optional[int]		 =						16
A_		:  Optional[Any]		 =						32



def        UpperCamelCase__		(						__magic_name__  :   Accelerator		,       __magic_name__  :   int = 16      )				->			int:
 '''simple docstring'''






 snake_case__     :					List[Any]    =  AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 snake_case__     :					Optional[Any]    =  load_dataset("""glue"""		,       """mrpc"""      )

 def tokenize_function(__magic_name__  :   List[Any]      ):
  # max_length=None => use the model max length (it's actually the default)
  snake_case__     :					Dict    =  tokenizer(examples["""sentence1"""]		,       examples["""sentence2"""]		,       truncation=__magic_name__		,       max_length=__magic_name__      )
  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():
  snake_case__     :					Dict    =  datasets.map(
      __magic_name__		,       batched=__magic_name__		,       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
 snake_case__     :					Union[str, Any]    =  tokenized_datasets.rename_column("""label"""		,       """labels"""      )

 def collate_fn(__magic_name__  :   str      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  snake_case__     :					Union[str, Any]    =  1_28 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   snake_case__     :					Dict    =  16
  elif accelerator.mixed_precision != "no":
   snake_case__     :					Union[str, Any]    =  8
  else:
   snake_case__     :					Optional[int]    =  None

  return tokenizer.pad(
      __magic_name__		,       padding="""longest"""		,       max_length=__magic_name__		,       pad_to_multiple_of=__magic_name__		,       return_tensors="""pt"""		,       )

 # Instantiate dataloaders.
 snake_case__     :					str    =  DataLoader(
     tokenized_datasets["""train"""]		,       shuffle=__magic_name__		,       collate_fn=__magic_name__		,       batch_size=__magic_name__      )
 snake_case__     :					str    =  DataLoader(
     tokenized_datasets["""validation"""]		,       shuffle=__magic_name__		,       collate_fn=__magic_name__		,       batch_size=__magic_name__      )

 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

    A_		:  Tuple		 =						mocked_dataloaders  # noqa: F811



def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   Any      )				->			Union[str, Any]:
 '''simple docstring'''






 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,       __magic_name__      ) == "1":
  snake_case__     :					Optional[Any]    =  2
 # Initialize accelerator
 snake_case__     :					Any    =  Accelerator(cpu=args.cpu		,       mixed_precision=args.mixed_precision      )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 snake_case__     :					Any    =  config["""lr"""]
 snake_case__     :					List[str]    =  int(config["""num_epochs"""]      )
 snake_case__     :					int    =  int(config["""seed"""]      )
 snake_case__     :					Any    =  int(config["""batch_size"""]      )

 snake_case__     :					Dict    =  evaluate.load("""glue"""		,       """mrpc"""      )

 # If the batch size is too big we use gradient accumulation
 snake_case__     :					Optional[int]    =  1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  snake_case__     :					Tuple    =  batch_size // MAX_GPU_BATCH_SIZE
  snake_case__     :					Optional[Any]    =  MAX_GPU_BATCH_SIZE

 set_seed(__magic_name__      )
 snake_case__  ,							snake_case__     :					List[Any]    =  get_dataloaders(__magic_name__		,       __magic_name__      )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 snake_case__     :					str    =  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,       return_dict=__magic_name__      )

 # 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).
 snake_case__     :					List[Any]    =  model.to(accelerator.device      )

 # Instantiate optimizer
 snake_case__     :					List[str]    =  AdamW(params=model.parameters()		,       lr=__magic_name__      )

 # Instantiate scheduler
 snake_case__     :					Optional[int]    =  get_linear_schedule_with_warmup(
     optimizer=__magic_name__		,       num_warmup_steps=1_00		,       num_training_steps=(len(__magic_name__      ) * 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.
 snake_case__  ,							snake_case__  ,							snake_case__  ,							snake_case__  ,							snake_case__     :					int    =  accelerator.prepare(
     __magic_name__		,       __magic_name__		,       __magic_name__		,       __magic_name__		,       __magic_name__      )

 # Now we train the model
 for epoch in range(__magic_name__      ):
  model.train()
  for step, batch in enumerate(__magic_name__      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   snake_case__     :					Union[str, Any]    =  model(**__magic_name__      )
   snake_case__     :					str    =  outputs.loss
   snake_case__     :					List[Any]    =  loss / gradient_accumulation_steps
   accelerator.backward(__magic_name__      )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  snake_case__     :					Tuple    =  0
  for step, batch in enumerate(__magic_name__      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   with torch.no_grad():
    snake_case__     :					Optional[int]    =  model(**__magic_name__      )
   snake_case__     :					Tuple    =  outputs.logits.argmax(dim=-1      )
   snake_case__  ,							snake_case__     :					int    =  accelerator.gather((predictions, batch["""labels"""])      )
   # New Code #
   # First we check if it's a distributed system
   if accelerator.use_distributed:
    # Then see if we're on the last batch of our eval dataloader
    if step == len(__magic_name__      ) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     snake_case__     :					Dict    =  predictions[: len(eval_dataloader.dataset      ) - samples_seen]
     snake_case__     :					int    =  references[: len(eval_dataloader.dataset      ) - samples_seen]
    else:
     # Otherwise we add the number of samples seen
     samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
   metric.add_batch(
       predictions=__magic_name__		,       references=__magic_name__		,       )

  snake_case__     :					Tuple    =  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:"		,       __magic_name__      )



def        UpperCamelCase__		(						)				->			str:
 '''simple docstring'''






 snake_case__     :					str    =  argparse.ArgumentParser(description="""Simple example of training script."""      )
 parser.add_argument(
     """--mixed_precision"""		,       type=__magic_name__		,       default=__magic_name__		,       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."""      )
 snake_case__     :					Optional[Any]    =  parser.parse_args()
 snake_case__     :					Any    =  {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(__magic_name__		,       __magic_name__      )


if __name__ == "__main__":
    main()





'''simple docstring'''


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     A ( pl.LightningModule ):





							def __init__(   self					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

	# init longformer question answering model
	__UpperCAmelCase      					=      LongformerForQuestionAnswering.from_pretrained(UpperCamelCase__							)

	# transfer weights
	longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()							)
	longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()							)
	longformer_for_qa.eval()

	# save model
	longformer_for_qa.save_pretrained(UpperCamelCase__							)

	print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""							)


if __name__ == "__main__":
					__lowerCAmelCase						:							Optional[Any]    							=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--longformer_model",
					    default=None,
					    type=str,
					    required=True,
					    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
					)
					parser.add_argument(
					    "--longformer_question_answering_ckpt_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path the official PyTorch Lightning Checkpoint.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
