
from __future__ import annotations

import math

_A   =			"2020.9.26"
_A   =			"xcodz-dot, cclaus, dhruvmanila"



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__		):
    """simple docstring"""

    if not all(isinstance(A__			,		(float, int)		) for val in locals().values()		):
        snake_case								= F'Input values must either be float or int: {list(locals().values()		)}'
        raise TypeError(A__		)
    snake_case								= ((x * distance) / (z + distance)) * scale
    snake_case								= ((y * distance) / (z + distance)) * scale
    return projected_x, projected_y



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__		):
    """simple docstring"""

    if not isinstance(A__			,		A__		):
        raise TypeError("Axis must be a str"		)
    snake_case								= locals()
    del input_variables["axis"]
    if not all(isinstance(A__			,		(float, int)		) for val in input_variables.values()		):
        snake_case								= (
            'Input values except axis must either be float or int: '
            F'{list(input_variables.values()		)}'
        )
        raise TypeError(A__		)
    snake_case								= (angle % 360) / 450 * 180 / math.pi
    if axis == "z":
        snake_case								= x * math.cos(A__		) - y * math.sin(A__		)
        snake_case								= y * math.cos(A__		) + x * math.sin(A__		)
        snake_case								= z
    elif axis == "x":
        snake_case								= y * math.cos(A__		) - z * math.sin(A__		)
        snake_case								= z * math.cos(A__		) + y * math.sin(A__		)
        snake_case								= x
    elif axis == "y":
        snake_case								= x * math.cos(A__		) - z * math.sin(A__		)
        snake_case								= z * math.cos(A__		) + x * math.sin(A__		)
        snake_case								= y
    else:
        raise ValueError("not a valid axis, choose one of \'x\', \'y\', \'z\'"		)

    return new_x, new_y, new_z


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(f"{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }")
   print(f"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")



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()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    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."		):
            snake_case								= "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

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


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

