

'''simple docstring'''






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase__											=	'''true'''


def 		_A     (    A__     ,				A__=82     ,				A__=16      ):






 """simple docstring"""



 set_seed(42      )
 __lowercase							  = RegressionModel()
 __lowercase							  = deepcopy(A__      )
 __lowercase							  = RegressionDataset(length=A__      )
 __lowercase							  = DataLoader(A__     ,				batch_size=A__      )
 model.to(accelerator.device      )
 __lowercase  ,	__lowercase							  = accelerator.prepare(A__     ,				A__      )
 return model, ddp_model, dataloader


def 		_A     (    A__     ,				A__=False      ):






 """simple docstring"""



 __lowercase							  = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
 __lowercase							  = load_dataset('''glue'''     ,				'''mrpc'''     ,				split='''validation'''      )

 def tokenize_function(A__      ):
  __lowercase							  = tokenizer(examples['''sentence1''']     ,				examples['''sentence2''']     ,				truncation=A__     ,				max_length=A__      )
  return outputs

 with accelerator.main_process_first():
  __lowercase							  = dataset.map(
      A__     ,				batched=A__     ,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,				)

 __lowercase							  = tokenized_datasets.rename_column('''label'''     ,				'''labels'''      )

 def collate_fn(A__      ):
  if use_longest:
   return tokenizer.pad(A__     ,				padding='''longest'''     ,				return_tensors='''pt'''      )
  return tokenizer.pad(A__     ,				padding='''max_length'''     ,				max_length=128     ,				return_tensors='''pt'''      )

 return DataLoader(A__     ,				shuffle=A__     ,				collate_fn=A__     ,				batch_size=16      )


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = Accelerator(dispatch_batches=A__     ,				split_batches=A__      )
 __lowercase							  = get_dataloader(A__     ,				not dispatch_batches      )
 __lowercase							  = AutoModelForSequenceClassification.from_pretrained(
     '''hf-internal-testing/mrpc-bert-base-cased'''     ,				return_dict=A__      )
 __lowercase  ,	__lowercase							  = accelerator.prepare(A__     ,				A__      )
 return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 for batch in dataloader:
  __lowercase  ,	__lowercase							  = batch.values()
  with torch.no_grad():
   __lowercase							  = model(A__      )
   __lowercase  ,	__lowercase							  = accelerator.gather_for_metrics((logit, target)      )
   logits_and_targets.append((logit, target)      )
 __lowercase  ,	__lowercase							  = [], []
 for logit, targ in logits_and_targets:
  logits.append(A__      )
  targs.append(A__      )
 __lowercase  ,	__lowercase							  = torch.cat(A__      ), torch.cat(A__      )
 return logits, targs


def 		_A     (    A__     ,				A__=82     ,				A__=False     ,				A__=False     ,				A__=16      ):






 """simple docstring"""



 __lowercase  ,	__lowercase  ,	__lowercase							  = get_basic_setup(A__     ,				A__     ,				A__      )
 __lowercase  ,	__lowercase							  = generate_predictions(A__     ,				A__     ,				A__      )
 assert (
     len(A__      ) == num_samples
 ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__      )}"


def 		_A     (    A__ = False     ,				A__ = False      ):






 """simple docstring"""



 __lowercase							  = evaluate.load('''glue'''     ,				'''mrpc'''      )
 __lowercase  ,	__lowercase							  = get_mrpc_setup(A__     ,				A__      )
 # First do baseline
 __lowercase  ,	__lowercase  ,	__lowercase							  = setup['''no''']
 model.to(A__      )
 model.eval()
 for batch in dataloader:
  batch.to(A__      )
  with torch.inference_mode():
   __lowercase							  = model(**A__      )
  __lowercase							  = outputs.logits.argmax(dim=-1      )
  metric.add_batch(predictions=A__     ,				references=batch['''labels''']      )
 __lowercase							  = metric.compute()

 # Then do distributed
 __lowercase  ,	__lowercase  ,	__lowercase							  = setup['''ddp''']
 model.eval()
 for batch in dataloader:
  with torch.inference_mode():
   __lowercase							  = model(**A__      )
  __lowercase							  = outputs.logits.argmax(dim=-1      )
  __lowercase							  = batch['''labels''']
  __lowercase  ,	__lowercase							  = accelerator.gather_for_metrics((preds, references)      )
  metric.add_batch(predictions=A__     ,				references=A__      )
 __lowercase							  = metric.compute()

 for key in "accuracy f1".split():
  assert math.isclose(
      baseline[key]     ,				distributed[key]      ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = Accelerator(split_batches=A__     ,				dispatch_batches=A__      )
 if accelerator.is_local_main_process:
  datasets.utils.logging.set_verbosity_warning()
  transformers.utils.logging.set_verbosity_warning()
 else:
  datasets.utils.logging.set_verbosity_error()
  transformers.utils.logging.set_verbosity_error()
 # These are a bit slower so they should only be ran on the GPU or TPU
 if torch.cuda.is_available() or is_tpu_available():
  if accelerator.is_local_main_process:
   print('''**Testing gather_for_metrics**'''      )
  for split_batches in [True, False]:
   for dispatch_batches in [True, False]:
    if accelerator.is_local_main_process:
     print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
    test_mrpc(A__     ,				A__      )
    accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print('''**Test torch metrics**'''      )
 for split_batches in [True, False]:
  for dispatch_batches in [True, False]:
   __lowercase							  = Accelerator(split_batches=A__     ,				dispatch_batches=A__      )
   if accelerator.is_local_main_process:
    print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
   test_torch_metrics(A__     ,				99      )
   accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print('''**Test last batch is not dropped when perfectly divisible**'''      )
 __lowercase							  = Accelerator()
 test_torch_metrics(A__     ,				512      )
 accelerator.state._reset_state()


def 		_A     (    A__      ):






 """simple docstring"""



 main()


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()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		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.'''     ):
						lowercase__            =      '''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

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							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.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


