'''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


_A			:      Tuple					      =				16
_A			:      Tuple					      =				32







def      UpperCamelCase_						(					snake_case_       :       Tuple						,		snake_case_       :       Dict = 16						,		snake_case_       :       List[str] = "bert-base-cased"      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained(_UpperCAmelCase      )
 __lowerCAmelCase											=						load_dataset("""glue"""						,		"""mrpc"""      )

 def tokenize_function(snake_case_       :       Optional[Any]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=_UpperCAmelCase						,		max_length=_UpperCAmelCase      )
  return outputs

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

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

 def collate_fn(snake_case_       :       int      ):
  # 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(_UpperCAmelCase						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(_UpperCAmelCase						,		padding="""longest"""						,		return_tensors="""pt"""      )

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

 return train_dataloader, eval_dataloader







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Tuple						,		snake_case_       :       Dict						,		snake_case_       :       Dict      )					->     int:






 '''simple docstring'''

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

 __lowerCAmelCase											=						metric.compute()
 return eval_metric["accuracy"]







def      UpperCamelCase_						(					snake_case_       :       Any						,		snake_case_       :       Dict      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						Accelerator()

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

 set_seed(_UpperCAmelCase      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						get_dataloaders(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase      )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  __lowerCAmelCase											=						accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  __lowerCAmelCase											=						1
 __lowerCAmelCase											=						(len(_UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __lowerCAmelCase											=						get_linear_schedule_with_warmup(
      optimizer=_UpperCAmelCase						,		num_warmup_steps=0						,		num_training_steps=_UpperCAmelCase						,		)
 else:
  __lowerCAmelCase											=						DummyScheduler(_UpperCAmelCase						,		total_num_steps=_UpperCAmelCase						,		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.
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						accelerator.prepare(
     _UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase      )

 # We need to keep track of how many total steps we have iterated over
 __lowerCAmelCase											=						0
 # We also need to keep track of the stating epoch so files are named properly
 __lowerCAmelCase											=						0
 __lowerCAmelCase											=						evaluate.load("""glue"""						,		"""mrpc"""      )
 __lowerCAmelCase											=						num_epochs

 if args.partial_train_epoch is not None:
  __lowerCAmelCase											=						args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint      )
  __lowerCAmelCase											=						args.resume_from_checkpoint.split("""epoch_"""      )[1]
  __lowerCAmelCase											=						""""""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  __lowerCAmelCase											=						int(_UpperCAmelCase      ) + 1
  __lowerCAmelCase											=						evaluation_loop(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase      )
  accelerator.print("""resumed checkpoint performance:"""						,		_UpperCAmelCase      )
  accelerator.print("""resumed checkpoint\'s scheduler\'s lr:"""						,		lr_scheduler.get_lr()[0]      )
  accelerator.print("""resumed optimizers\'s lr:"""						,		optimizer.param_groups[0]["""lr"""]      )
  with open(os.path.join(args.output_dir						,		f"""state_{starting_epoch-1}.json"""      )						,		"""r"""      ) as f:
   __lowerCAmelCase											=						json.load(_UpperCAmelCase      )
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 __lowerCAmelCase											=						{}
 for epoch in range(_UpperCAmelCase						,		_UpperCAmelCase      ):
  model.train()
  for step, batch in enumerate(_UpperCAmelCase      ):
   __lowerCAmelCase											=						model(**_UpperCAmelCase      )
   __lowerCAmelCase											=						outputs.loss
   __lowerCAmelCase											=						loss / gradient_accumulation_steps
   accelerator.backward(_UpperCAmelCase      )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  __lowerCAmelCase											=						f"""epoch_{epoch}"""
  __lowerCAmelCase											=						os.path.join(args.output_dir						,		_UpperCAmelCase      )
  accelerator.save_state(_UpperCAmelCase      )
  __lowerCAmelCase											=						evaluation_loop(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase      )
  __lowerCAmelCase											=						accuracy
  __lowerCAmelCase											=						lr_scheduler.get_lr()[0]
  __lowerCAmelCase											=						optimizer.param_groups[0]["""lr"""]
  __lowerCAmelCase											=						epoch
  __lowerCAmelCase											=						overall_step
  accelerator.print(f"""epoch {epoch}:"""						,		_UpperCAmelCase      )

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir						,		f"""state_{epoch}.json"""      )						,		"""w"""      ) as f:
    json.dump(_UpperCAmelCase						,		_UpperCAmelCase      )







def      UpperCamelCase_						(					)					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""      )
 parser.add_argument(
     """--model_name_or_path"""						,		type=_UpperCAmelCase						,		default="""bert-base-cased"""						,		help="""Path to pretrained model or model identifier from huggingface.co/models."""						,		required=_UpperCAmelCase						,		)
 parser.add_argument(
     """--output_dir"""						,		type=_UpperCAmelCase						,		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=_UpperCAmelCase						,		default=_UpperCAmelCase						,		help="""If the training should continue from a checkpoint folder."""						,		)
 parser.add_argument(
     """--partial_train_epoch"""						,		type=_UpperCAmelCase						,		default=_UpperCAmelCase						,		help="""If passed, the training will stop after this number of epochs."""						,		)
 parser.add_argument(
     """--num_epochs"""						,		type=_UpperCAmelCase						,		default=2						,		help="""Number of train epochs."""						,		)
 __lowerCAmelCase											=						parser.parse_args()
 __lowerCAmelCase											=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

 training_function(_UpperCAmelCase						,		_UpperCAmelCase      )


if __name__ == "__main__":
 main()











import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    # Initialise PyTorch model
    __a						       =				BigBirdConfig.from_json_file(_UpperCAmelCase				)
    print(f'Building PyTorch model from configuration: {config}'				)

    if is_trivia_qa:
        __a						       =				BigBirdForQuestionAnswering(_UpperCAmelCase				)
    else:
        __a						       =				BigBirdForPreTraining(_UpperCAmelCase				)

    # Load weights from tf checkpoint
    load_tf_weights_in_big_bird(_UpperCAmelCase   , _UpperCAmelCase   , is_trivia_qa=_UpperCAmelCase				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Tuple      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--big_bird_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained BERT model. \n'''
         '''This specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
 )
 __snake_case						:Any      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
 )





