





import numpy

# List of input, output pairs
UpperCAmelCase     :     List[Any]							=	(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase     :     Optional[int]							=	(((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase     :     Tuple							=	[2, 4, 1, 5]
UpperCAmelCase     :     Tuple							=	len(train_data)
UpperCAmelCase     :     List[Any]							=	0.0_0_9
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       str="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : List[Any]  =0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """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 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       str						):





  """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 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Dict=m						):





  """simple docstring"""



  a__     : Any  =0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : Optional[int]  =summation_of_cost_derivative(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def 					_A	(		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__     : Dict  =0.0_0_0_0_0_2
  a__     : Any  =0
  a__     : Any  =0
  while True:
    j += 1
    a__     : Optional[int]  =[0, 0, 0, 0]
    for i in range(0	,	len(SCREAMING_SNAKE_CASE						)						):
      a__     : Optional[Any]  =get_cost_derivative(i - 1						)
      a__     : Any  =(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	atol=SCREAMING_SNAKE_CASE	,	rtol=SCREAMING_SNAKE_CASE	,	):
      break
    a__     : str  =temp_parameter_vector
  print(("Number of iterations:", j)						)
def 					_A	(		):





  """simple docstring"""



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


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













import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Tuple  =set()
  a__     : Optional[Any]  =[]

  def parse_line(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    for line in fp:
      if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : str  =line.decode("UTF-8"						)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(" "						):
        # process a single warning and move it to `selected_warnings`.
        if len(SCREAMING_SNAKE_CASE						) > 0:
          a__     : Union[str, Any]  ="\n".join(SCREAMING_SNAKE_CASE						)
          # Only keep the warnings specified in `targets`
          if any(f''': {x}: ''' in warning for x in targets						):
            selected_warnings.add(SCREAMING_SNAKE_CASE						)
          buffer.clear()
        continue
      else:
        a__     : Optional[Any]  =line.strip()
        buffer.append(SCREAMING_SNAKE_CASE						)

  if from_gh:
    for filename in os.listdir(SCREAMING_SNAKE_CASE						):
      a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(SCREAMING_SNAKE_CASE						) as fp:
          parse_line(SCREAMING_SNAKE_CASE						)
  else:
    try:
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE						) as z:
        for filename in z.namelist():
          if not os.path.isdir(SCREAMING_SNAKE_CASE						):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(SCREAMING_SNAKE_CASE						) as fp:
              parse_line(SCREAMING_SNAKE_CASE						)
    except Exception:
      logger.warning(
          f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''						)

  return selected_warnings
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =set()

  a__     : Any  =[os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for p in os.listdir(SCREAMING_SNAKE_CASE						) if (p.endswith(".zip"						) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  return selected_warnings


if __name__ == "__main__":
     def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





       """simple docstring"""



       return values.split(","						)

     UpperCAmelCase     :     Any							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
     parser.add_argument(
         """--output_dir""",
         type=str,
         required=True,
         help="""Where to store the downloaded artifacts and other result files.""",
     )
     parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
     # optional parameters
     parser.add_argument(
         """--targets""",
         default="""DeprecationWarning,UserWarning,FutureWarning""",
         type=list_str,
         help="""Comma-separated list of target warning(s) which we want to extract.""",
     )
     parser.add_argument(
         """--from_gh""",
         action="""store_true""",
         help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
     )

     UpperCAmelCase     :     List[Any]							=	parser.parse_args()

     UpperCAmelCase     :     str							=	args.from_gh
     if from_gh:
          # The artifacts have to be downloaded using `actions/download-artifact@v3`
          pass
     else:
          os.makedirs(args.output_dir, exist_ok=True)

          # get download links
          UpperCAmelCase     :     Dict							=	get_artifacts_links(args.workflow_run_id, token=args.token)
          with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
               json.dump(artifacts, fp, ensure_ascii=False, indent=4)

          # download artifacts
          for idx, (name, url) in enumerate(artifacts.items()):
               print(name)
               print(url)
               print("""=""" * 80)
               download_artifact(name, url, args.output_dir, args.token)
               # Be gentle to GitHub
               time.sleep(1)

    # extract warnings from artifacts
     UpperCAmelCase     :     Tuple							=	extract_warnings(args.output_dir, args.targets)
     UpperCAmelCase     :     Optional[Any]							=	sorted(selected_warnings)
     with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
          json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






