




"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


A_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  R"""\w+[.]\d+"""
  _snake_case							:		Union[str, Any]								=  re.findall(snake_case__		,   snake_case__    )
  for pat in pats:
    _snake_case							:		str								=  key.replace(snake_case__		,   """_""".join(pat.split("""."""    )    )    )
  return key


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  pt_tuple_key[:-1] + ("""scale""",)
  if (
      any("""norm""" in str_ for str_ in pt_tuple_key    )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    _snake_case							:		List[str]								=  pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""embedding""",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    _snake_case							:		Dict								=  pt_tensor.transpose(2		,   3		,   1		,   0    )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight":
    _snake_case							:		Optional[Any]								=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  _snake_case							:		Dict								=  pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor






def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					Tuple		,   snake_case__							:					Union[str, Any]=42    ):
  """simple docstring"""

  _snake_case							:		Dict								=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  _snake_case							:		List[str]								=  flax_model.init_weights(PRNGKey(snake_case__    )    )

  _snake_case							:		int								=  flatten_dict(snake_case__    )
  _snake_case							:		List[str]								=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _snake_case							:		List[str]								=  rename_key(snake_case__    )
    _snake_case							:		List[Any]								=  tuple(renamed_pt_key.split("""."""    )    )

    # Correctly rename weight parameters
    _snake_case		,					_snake_case							:		int								=  rename_key_and_reshape_tensor(snake_case__		,   snake_case__		,   snake_case__    )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."    )

        # also add unexpected weight so that warning is thrown
    _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__    )

  return unflatten_dict(snake_case__    )








"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


