






from timeit import timeit

a_		:    str   							=     {
    'MALAYALAM': True,
    'String': False,
    'rotor': True,
    'level': True,
    'A': True,
    'BB': True,
    'ABC': False,
    'amanaplanacanalpanama': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			0
			SCREAMING_SNAKE_CASE							=			len(_UpperCAmelCase) - 1
			while start_i < end_i:
						if s[start_i] == s[end_i]:
									start_i += 1
									end_i -= 1
						else:
									return False
			return True






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			len(_UpperCAmelCase) // 2
			SCREAMING_SNAKE_CASE							=			len(_UpperCAmelCase)

			# We need to traverse till half of the length of string
			# as we can get access of the i'th last element from
			# i'th index.
			# eg: [0,1,2,3,4,5] => 4th index can be accessed
			# with the help of 1st index (i==n-i-1)
			# where n is length of string
			return all(s[i] == s[n - i - 1] for i in range(_UpperCAmelCase))






def 					lowerCamelCase__					(_UpperCAmelCase):
			if len(_UpperCAmelCase) <= 2:
						return True
			if s[0] == s[len(_UpperCAmelCase) - 1]:
						return is_palindrome_recursive(s[1:-1])
			else:
						return False






def 					lowerCamelCase__					(_UpperCAmelCase):
			return s == s[::-1]






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			F'''all({name}(key) is value for key, value in test_data.items())'''
			SCREAMING_SNAKE_CASE							=			F'''from __main__ import test_data, {name}'''
			SCREAMING_SNAKE_CASE							=			50_0000
			SCREAMING_SNAKE_CASE							=			timeit(stmt=_UpperCAmelCase				,							setup=_UpperCAmelCase				,							number=_UpperCAmelCase)
			print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''')


if __name__ == "__main__":
				for key, value in test_data.items():
								assert is_palindrome(key) is is_palindrome_recursive(key)
								assert is_palindrome(key) is is_palindrome_slice(key)
								print(f"""{key:21} {value}""")
				print('a man a plan a canal panama')

				# finished 500,000 runs in 0.46793 seconds
				benchmark_function('is_palindrome_slice')
				# finished 500,000 runs in 0.85234 seconds
				benchmark_function('is_palindrome')
				# finished 500,000 runs in 1.32028 seconds
				benchmark_function('is_palindrome_recursive')
				# finished 500,000 runs in 2.08679 seconds
				benchmark_function('is_palindrome_traversal')










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   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	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									=				"""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									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, 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   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

