



"""simple docstring"""







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:

		# A local function to see if a dot lands in the circle.
		def is_in_circle(__lowerCamelCase						,     __lowerCamelCase	) -> bool:
				lowercase__   :    Union[str, Any]				=			sqrt((x**2) + (y**2)	)
				# Our circle has a radius of 1, so a distance
				# greater than 1 would land outside the circle.
				return distance_from_centre <= 1

		# The proportion of guesses that landed in the circle
		lowercase__   :    Optional[Any]				=			mean(
		    int(is_in_circle(uniform(-1.0						,     1.0	)						,     uniform(-1.0						,     1.0	)	)	)
		    for _ in range(__lowerCamelCase	)	)
		# The ratio of the area for circle to square is pi/4.
		lowercase__   :    Optional[int]				=			proportion * 4
		print(f"""The estimated value of pi is {pi_estimate}"""	)
		print(f"""The numpy value of pi is {pi}"""	)
		print(f"""The total error is {abs(pi - pi_estimate	)}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = 0.0						,     __lowerCamelCase = 1.0						,     )							->      float:
		return mean(
		    function_to_integrate(uniform(__lowerCamelCase						,     __lowerCamelCase	)	) for _ in range(__lowerCamelCase	)	) * (max_value - min_value)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 0.0						,     __lowerCamelCase = 1.0	)							->      None:

		def identity_function(__lowerCamelCase	) -> float:
				return x

		lowercase__   :    List[Any]				=			area_under_curve_estimator(
		    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		lowercase__   :    Dict				=			(max_value * max_value - min_value * min_value) / 2

		print('''******************'''	)
		print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""	)
		print(f"""Estimated value is {estimated_value}"""	)
		print(f"""Expected value is {expected_value}"""	)
		print(f"""Total error is {abs(estimated_value - expected_value	)}"""	)
		print('''******************'''	)



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      None:

		def function_to_integrate(__lowerCamelCase	) -> float:
				return sqrt(4.0 - x * x	)

		lowercase__   :    int				=			area_under_curve_estimator(
		    __lowerCamelCase						,     __lowerCamelCase						,     0.0						,     2.0	)

		print('''******************'''	)
		print('''Estimating pi using area_under_curve_estimator'''	)
		print(f"""Estimated value is {estimated_value}"""	)
		print(f"""Expected value is {pi}"""	)
		print(f"""Total error is {abs(estimated_value - pi	)}"""	)
		print('''******************'''	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
