

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def      _lowerCAmelCase(	__A			):
  if "model" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("model."   ,       ""			)
  if "norm1" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("norm1"   ,       "attention.output.LayerNorm"			)
  if "norm2" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("norm2"   ,       "output.LayerNorm"			)
  if "norm" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("norm"   ,       "LayerNorm"			)
  if "transformer" in orig_key:
    UpperCAmelCase					      =  orig_key.split("."			)[0].split("_"			)[-1]
    UpperCAmelCase					      =  orig_key.replace(F"transformer_{layer_num}"   ,       F"encoder.layer.{layer_num}"			)
  if "mha.attn" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("mha.attn"   ,       "attention.self"			)
  if "mha" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("mha"   ,       "attention"			)
  if "W_q" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("W_q"   ,       "self.query"			)
  if "W_k" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("W_k"   ,       "self.key"			)
  if "W_v" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("W_v"   ,       "self.value"			)
  if "ff1" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("ff1"   ,       "intermediate.dense"			)
  if "ff2" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("ff2"   ,       "output.dense"			)
  if "ff" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("ff"   ,       "output.dense"			)
  if "mlm_class" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("mlm.mlm_class"   ,       "cls.predictions.decoder"			)
  if "mlm" in orig_key:
    UpperCAmelCase					      =  orig_key.replace("mlm"   ,       "cls.predictions.transform"			)
  if "cls" not in orig_key:
    UpperCAmelCase					      =  "yoso." + orig_key

  return orig_key
def      _lowerCAmelCase(	__A   ,       __A			):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase					      =  orig_state_dict.pop(__A			)

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

  UpperCAmelCase					      =  orig_state_dict["cls.predictions.decoder.bias"]
  UpperCAmelCase					      =  torch.arange(__A			).expand((1, -1)			) + 2

  return orig_state_dict
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)["model_state_dict"]
  UpperCAmelCase					      =  YosoConfig.from_json_file(__A			)
  UpperCAmelCase					      =  YosoForMaskedLM(__A			)

  UpperCAmelCase					      =  convert_checkpoint_helper(config.max_position_embeddings   ,       __A			)

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

  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)






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  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      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


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



