





"""simple docstring"""





import math
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = 0	,  _SCREAMING_SNAKE_CASE = 0     )   ->list:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				end or len(_SCREAMING_SNAKE_CASE     )
  for i in range(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :List[Any]											=				i
    lowerCAmelCase__     :Any											=				array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      lowerCAmelCase__     :str											=				array[temp_index - 1]
      temp_index -= 1
    lowerCAmelCase__     :Tuple											=				temp_index_value
  return array
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->None:  # Max Heap





  """simple docstring"""


  lowerCAmelCase__     :Any											=				index
  lowerCAmelCase__     :Optional[int]											=				2 * index + 1  # Left Node
  lowerCAmelCase__     :int											=				2 * index + 2  # Right Node

  if left_index < heap_size and array[largest] < array[left_index]:
    lowerCAmelCase__     :Dict											=				left_index

  if right_index < heap_size and array[largest] < array[right_index]:
    lowerCAmelCase__     :str											=				right_index

  if largest != index:
    lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				array[largest], array[index]
    heapify(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				len(_SCREAMING_SNAKE_CASE     )

  for i in range(n // 2	,  -1	,  -1     ):
    heapify(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  for i in range(n - 1	,  0	,  -1     ):
    lowerCAmelCase__				,							lowerCAmelCase__     :int											=				array[0], array[i]
    heapify(_SCREAMING_SNAKE_CASE	,  0	,  _SCREAMING_SNAKE_CASE     )

  return array
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  if (array[first_index] > array[middle_index]) != (
      array[first_index] > array[last_index]
  ):
    return array[first_index]
  elif (array[middle_index] > array[first_index]) != (
      array[middle_index] > array[last_index]
  ):
    return array[middle_index]
  else:
    return array[last_index]
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				low
  lowerCAmelCase__     :int											=				high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				array[j], array[i]
    i += 1
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  if len(_SCREAMING_SNAKE_CASE     ) == 0:
    return array
  lowerCAmelCase__     :Dict											=				2 * math.ceil(math.loga(len(_SCREAMING_SNAKE_CASE     )     )     )
  lowerCAmelCase__     :Tuple											=				16
  return intro_sort(_SCREAMING_SNAKE_CASE	,  0	,  len(_SCREAMING_SNAKE_CASE     )	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(_SCREAMING_SNAKE_CASE     )
    max_depth -= 1
    lowerCAmelCase__     :Any											=				median_of_a(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  start + ((end - start) // 2) + 1	,  end - 1     )
    lowerCAmelCase__     :Tuple											=				partition(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    intro_sort(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :str											=				p
  return insertion_sort(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A											=						input("""Enter numbers separated by a comma : """).strip()
  __A											=						[float(item) for item in user_input.split(""",""")]
  print(sort(unsorted))




'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_A ,    _A ,    _A       )

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'       )
       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

