



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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
   A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
       _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

   # convert state_dict
   A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
   A							:					Union[str, Any]              =						{}
   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							:					int              =						"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							:					Any              =						tensor_value

   A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
   model.save_pretrained(_lowerCamelCase    )

   # convert tokenizer
   A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
   tokenizer.save_pretrained(_lowerCamelCase    )


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




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						else:
									raise ZeroDivisionError("Could not find root"    ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess    ) < precision:
									return next_guess

						A							:					int              =						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")