import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowerCAmelCase__       = logging.getLogger(__name__)






@dataclass
class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						field(
     default=0.0	,						metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}    )
 UpperCAmelCase							    =						field(default=_snake_case	,						metadata={"""help""": """Whether to SortishSamler or not."""}    )
 UpperCAmelCase							    =						field(
     default=_snake_case	,						metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}    )
 UpperCAmelCase							    =						field(default=_snake_case	,						metadata={"""help""": """whether to use adafactor"""}    )
 UpperCAmelCase							    =						field(
     default=_snake_case	,						metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}    )
 UpperCAmelCase							    =						field(
     default=_snake_case	,						metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}    )
 UpperCAmelCase							    =						field(default=_snake_case	,						metadata={"""help""": """Dropout probability. Goes into model.config."""}    )
 UpperCAmelCase							    =						field(
     default=_snake_case	,						metadata={"""help""": """Attention dropout probability. Goes into model.config."""}    )
 UpperCAmelCase							    =						field(
     default="""linear"""	,						metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()    )}'''}	,						)






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()



