






import numpy

# List of input, output pairs
__UpperCamelCase      :      List[str]     						=						(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__UpperCamelCase      :      List[Any]     						=						(((515, 22, 13), 555), ((61, 35, 49), 150))
__UpperCamelCase      :      List[Any]     						=						[2, 4, 1, 5]
__UpperCamelCase      :      Union[str, Any]     						=						len(train_data)
__UpperCamelCase      :      List[Any]     						=						0.0_0_9





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase="train"					):




	'''simple docstring'''







	return calculate_hypothesis_value(lowerCamelCase				,  lowerCamelCase					) - output(
	    lowerCamelCase				,  lowerCamelCase					)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  0
	for i in range(len(lowerCamelCase					) - 1					):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]					)
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]					)
	return None





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase=m					):




	'''simple docstring'''







	__lowercase             =  0
	for i in range(lowerCamelCase					):
		if index == -1:
			summation_value += _error(lowerCamelCase					)
		else:
			summation_value += _error(lowerCamelCase					) * train_data[i][0][index]
	return summation_value





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  summation_of_cost_derivative(lowerCamelCase				,  lowerCamelCase					) / m
	return cost_derivative_value





def 				snake_case  (     ):




	'''simple docstring'''







	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__lowercase             =  0.000002
	__lowercase             =  0
	__lowercase             =  0
	while True:
		j += 1
		__lowercase             =  [0, 0, 0, 0]
		for i in range(0				,  len(lowerCamelCase					)					):
			__lowercase             =  get_cost_derivative(i - 1					)
			__lowercase             =  (
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    lowerCamelCase				,  lowerCamelCase				,  atol=lowerCamelCase				,  rtol=lowerCamelCase				,  ):
			break
		__lowercase             =  temp_parameter_vector
	print(("""Number of iterations:""", j)					)





def 				snake_case  (     ):




	'''simple docstring'''







	for i in range(len(lowerCamelCase					)					):
		print(("""Actual output value:""", output(lowerCamelCase				,  """test"""					))					)
		print(("""Hypothesis output:""", calculate_hypothesis_value(lowerCamelCase				,  """test"""					))					)


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()









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)

