






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch


@require_torch
@require_optimum
@slow
class        __UpperCamelCase    (     unittest.TestCase   ):







	def  _a   (  self      :							Union[str, Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  """hf-internal-testing/tiny-random-t5"""
		__lowercase             =  AutoTokenizer.from_pretrained(_lowerCAmelCase			)
		__lowercase             =  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

		__lowercase             =  tokenizer("""This is me"""       ,				return_tensors="""pt"""			)

		__lowercase             =  model.to_bettertransformer()

		self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

		__lowercase             =  model.generate(**_lowerCAmelCase			)

		__lowercase             =  model.reverse_bettertransformer()

		self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

		with tempfile.TemporaryDirectory() as tmpdirname:
			model.save_pretrained(_lowerCAmelCase			)

			__lowercase             =  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

			self.assertFalse(
			    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

			__lowercase             =  model_reloaded.generate(**_lowerCAmelCase			)
			self.assertTrue(torch.allclose(_lowerCAmelCase       ,				_lowerCAmelCase			)			)







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  """hf-internal-testing/tiny-random-t5"""
		__lowercase             =  AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase			)

		__lowercase             =  model.to_bettertransformer()

		with tempfile.TemporaryDirectory() as tmpdirname:
			with self.assertRaises(_lowerCAmelCase			):
				model.save_pretrained(_lowerCAmelCase			)

			__lowercase             =  model.reverse_bettertransformer()
			model.save_pretrained(_lowerCAmelCase			)









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()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  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=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						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."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

