





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__A									=				logging.get_logger(__name__)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a=False		)     ->		Optional[int]:







  """simple docstring"""





  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
        " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
        " instructions."		)
    raise

  if not is_sharded:
    lowerCamelCase__:      Optional[Any]			  =os.path.abspath(_SCREAMING_SNAKE_CASE		)
    logger.info(F"""Loading PyTorch weights from {pt_path}"""		)

    lowerCamelCase__:      Union[str, Any]			  =torch.load(_SCREAMING_SNAKE_CASE	,    map_location="cpu"		)
    logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()		):,} parameters."""		)

    lowerCamelCase__:      List[str]			  =convert_pytorch_state_dict_to_flax(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    lowerCamelCase__:      Optional[Any]			  =convert_pytorch_sharded_state_dict_to_flax(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)
  return flax_state_dict





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    )     ->		(Tuple[str], np.ndarray):







  """simple docstring"""





  def is_key_or_prefix_key_in_dict(__a		) -> bool:
    return len(set(_SCREAMING_SNAKE_CASE		) & {key, (model_prefix,) + key}		) > 0

  # layer norm
  lowerCamelCase__:      Dict			  =pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  lowerCamelCase__:      Dict			  =pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  lowerCamelCase__:      Optional[Any]			  =pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  lowerCamelCase__:      str			  =pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowerCamelCase__:      Union[str, Any]			  =pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    lowerCamelCase__:      Union[str, Any]			  =pt_tensor.transpose(2	,    3	,    1	,    0		)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCamelCase__:      int			  =pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE		):
    lowerCamelCase__:      Any			  =pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  lowerCamelCase__:      Optional[int]			  =pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  lowerCamelCase__:      List[str]			  =None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    lowerCamelCase__:      Union[str, Any]			  =pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    lowerCamelCase__:      Any			  =pt_tuple_key[-2] + '_v'
  if name is not None:
    lowerCamelCase__:      List[str]			  =pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      str			  ={k: v.numpy() for k, v in pt_state_dict.items()}

  lowerCamelCase__:      Tuple			  =flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    lowerCamelCase__:      Union[str, Any]			  =flax_model.params['params']
  else:
    lowerCamelCase__:      int			  =flax_model.params
  lowerCamelCase__:      str			  =flatten_dict(_SCREAMING_SNAKE_CASE		)

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    lowerCamelCase__:      List[str]			  =flatten_dict(flax_model.params["batch_stats"]		)
    random_flax_state_dict.update(_SCREAMING_SNAKE_CASE		)

  lowerCamelCase__:      Tuple			  ={}

  lowerCamelCase__:      int			  =(model_prefix not in flax_model_params) and (
      model_prefix in {k.split("."		)[0] for k in pt_state_dict.keys()}
  )
  lowerCamelCase__:      Dict			  =(model_prefix in flax_model_params) and (
      model_prefix not in {k.split("."		)[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowerCamelCase__:      str			  =tuple(pt_key.split("."		)		)

    # remove base model prefix if necessary
    lowerCamelCase__:      Union[str, Any]			  =pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCamelCase__:      Any			  =pt_tuple_key[1:]

    # Correctly rename weight parameters
    lowerCamelCase__:      str			  =rename_key_and_reshape_tensor(
        _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)

    # add model prefix if necessary
    lowerCamelCase__:      Dict			  =(model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCamelCase__:      str			  =(model_prefix,) + flax_key

    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}."""		)

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        lowerCamelCase__:      Union[str, Any]			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)
        continue

      # also add unexpected weight so that warning is thrown
      lowerCamelCase__:      Optional[int]			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)

    else:
      # also add unexpected weight so that warning is thrown
      lowerCamelCase__:      Any			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)

  return unflatten_dict(_SCREAMING_SNAKE_CASE		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  import torch

  # Load the index
  lowerCamelCase__:      Optional[Any]			  ={}
  for shard_file in shard_filenames:
    # load using msgpack utils
    lowerCamelCase__:      List[str]			  =torch.load(_SCREAMING_SNAKE_CASE		)
    lowerCamelCase__:      str			  ={k: v.numpy() for k, v in pt_state_dict.items()}

    lowerCamelCase__:      int			  =flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      lowerCamelCase__:      List[str]			  =flax_model.params['params']

      lowerCamelCase__:      str			  =flatten_dict(_SCREAMING_SNAKE_CASE		)
      random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]		)		)
    else:
      lowerCamelCase__:      Any			  =flax_model.params
      lowerCamelCase__:      Optional[int]			  =flatten_dict(_SCREAMING_SNAKE_CASE		)

    lowerCamelCase__:      List[str]			  =(model_prefix not in flax_model_params) and (
        model_prefix in {k.split("."		)[0] for k in pt_state_dict.keys()}
    )
    lowerCamelCase__:      Dict			  =(model_prefix in flax_model_params) and (
        model_prefix not in {k.split("."		)[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      lowerCamelCase__:      Tuple			  =tuple(pt_key.split("."		)		)

      # remove base model prefix if necessary
      lowerCamelCase__:      List[str]			  =pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        lowerCamelCase__:      Optional[int]			  =pt_tuple_key[1:]

      # Correctly rename weight parameters
      lowerCamelCase__:      Dict			  =rename_key_and_reshape_tensor(
          _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)
      # add model prefix if necessary
      lowerCamelCase__:      Dict			  =(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        lowerCamelCase__:      List[str]			  =(model_prefix,) + flax_key

      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}."""		)

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          lowerCamelCase__:      Any			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)
          continue
        if "var" in flax_key[-1]:
          lowerCamelCase__:      Tuple			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)
          continue

        # also add unexpected weight so that warning is thrown
        lowerCamelCase__:      int			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)

      else:
        # also add unexpected weight so that warning is thrown
        lowerCamelCase__:      Optional[Any]			  =jnp.asarray(_SCREAMING_SNAKE_CASE		)
  return unflatten_dict(_SCREAMING_SNAKE_CASE		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =os.path.abspath(_SCREAMING_SNAKE_CASE		)
  logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""		)

  # import correct flax class
  lowerCamelCase__:      List[Any]			  =getattr(_SCREAMING_SNAKE_CASE	,    "Flax" + model.__class__.__name__		)

  # load flax weight dict
  with open(_SCREAMING_SNAKE_CASE	,    "rb"		) as state_f:
    try:
      lowerCamelCase__:      str			  =from_bytes(_SCREAMING_SNAKE_CASE	,    state_f.read()		)
    except UnpicklingError:
      raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """		)

  return load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE	,    _SCREAMING_SNAKE_CASE		)






def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
        " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
        " instructions."		)
    raise

  # check if we have bf16 weights
  lowerCamelCase__:      str			  =flatten_dict(jax.tree_util.tree_map(lambda __a		: x.dtype == jnp.bfloataa	,    _SCREAMING_SNAKE_CASE		)		).values()
  if any(_SCREAMING_SNAKE_CASE		):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
        "before loading those in PyTorch model."		)
    lowerCamelCase__:      Optional[int]			  =jax.tree_util.tree_map(
        lambda __a		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params	,    _SCREAMING_SNAKE_CASE		)

  lowerCamelCase__:      Dict			  =flatten_dict(_SCREAMING_SNAKE_CASE		)
  lowerCamelCase__:      int			  =pt_model.state_dict()

  lowerCamelCase__:      Union[str, Any]			  =(pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split("."		)[0] for k in pt_model_dict.keys()}
  )
  lowerCamelCase__:      List[str]			  =(pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split("."		)[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  lowerCamelCase__:      Any			  =[]
  lowerCamelCase__:      Union[str, Any]			  =set(pt_model_dict.keys()		)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    lowerCamelCase__:      Tuple			  =flax_key_tuple[0] == pt_model.base_model_prefix
    lowerCamelCase__:      Tuple			  ='.'.join((pt_model.base_model_prefix,) + flax_key_tuple		) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      lowerCamelCase__:      Tuple			  =flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      lowerCamelCase__:      Union[str, Any]			  =(pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_SCREAMING_SNAKE_CASE		) not in pt_model_dict:
      # conv layer
      lowerCamelCase__:      List[Any]			  =flax_key_tuple[:-1] + ('weight',)
      lowerCamelCase__:      int			  =jnp.transpose(_SCREAMING_SNAKE_CASE	,    (3, 2, 0, 1)		)
    elif flax_key_tuple[-1] == "kernel" and ".".join(_SCREAMING_SNAKE_CASE		) not in pt_model_dict:
      # linear layer
      lowerCamelCase__:      List[str]			  =flax_key_tuple[:-1] + ('weight',)
      lowerCamelCase__:      int			  =flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      lowerCamelCase__:      int			  =flax_key_tuple[:-1] + ('weight',)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      lowerCamelCase__:      Any			  =flax_key_tuple[:-1] + ('running_mean',)
    elif "var" in flax_key_tuple[-1]:
      lowerCamelCase__:      str			  =flax_key_tuple[:-1] + ('running_var',)

    if "batch_stats" in flax_state:
      lowerCamelCase__:      Optional[int]			  ='.'.join(flax_key_tuple[1:]		)  # Remove the params/batch_stats header
    else:
      lowerCamelCase__:      Tuple			  ='.'.join(_SCREAMING_SNAKE_CASE		)

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    lowerCamelCase__:      Tuple			  ={}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      lowerCamelCase__:      List[str]			  =key.split("."		)
      lowerCamelCase__:      Tuple			  =None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        lowerCamelCase__:      str			  =key_components[-2] + '_g'
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        lowerCamelCase__:      List[str]			  =key_components[-2] + '_v'
      if name is not None:
        lowerCamelCase__:      Optional[int]			  =key_components[:-3] + [name]
        lowerCamelCase__:      Optional[Any]			  ='.'.join(_SCREAMING_SNAKE_CASE		)
        lowerCamelCase__:      Optional[Any]			  =key

    if flax_key in special_pt_names:
      lowerCamelCase__:      Optional[int]			  =special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
            F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""		)
      else:
        # add weight to pytorch dict
        lowerCamelCase__:      List[Any]			  =np.asarray(_SCREAMING_SNAKE_CASE		) if not isinstance(_SCREAMING_SNAKE_CASE	,    np.ndarray		) else flax_tensor
        lowerCamelCase__:      str			  =torch.from_numpy(_SCREAMING_SNAKE_CASE		)
        # remove from missing keys
        missing_keys.remove(_SCREAMING_SNAKE_CASE		)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_SCREAMING_SNAKE_CASE		)

  pt_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  # re-transform missing_keys to list
  lowerCamelCase__:      Optional[int]			  =list(_SCREAMING_SNAKE_CASE		)

  if len(_SCREAMING_SNAKE_CASE		) > 0:
    logger.warning(
        "Some weights of the Flax model were not used when initializing the PyTorch model"
        F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
        F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
        " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
        F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
        " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
        " FlaxBertForSequenceClassification model)."		)
  else:
    logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""		)
  if len(_SCREAMING_SNAKE_CASE		) > 0:
    logger.warning(
        F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
        F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
        " use it for predictions and inference."		)
  else:
    logger.warning(
        F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
        "If your task is similar to the task the model of the checkpoint was trained on, "
        F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""		)

  return pt_model








"""simple docstring"""





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__A											=		Lock()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->List[Any]:





  """simple docstring"""


  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0     ,   10			):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(_SCREAMING_SNAKE_CASE			)
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      lowerCAmelCase__			:Any											=							rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      lowerCAmelCase__			:Tuple											=							min(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(_SCREAMING_SNAKE_CASE			)
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      lowerCAmelCase__			:Optional[int]											=							lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      lowerCAmelCase__			:Optional[int]											=							max(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__			:str											=							[]
  lowerCAmelCase__			:Optional[Any]											=							[]
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()			)
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  lowerCAmelCase__			:List[str]											=							Pipe()
  lowerCAmelCase__			:List[Any]											=							Pipe()
  process_array_.append(
      Process(
          target=_SCREAMING_SNAKE_CASE     ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     ,   )			)
  lowerCAmelCase__			:Dict											=							temp_rs
  lowerCAmelCase__			:Optional[Any]											=							temp_rr

  for i in range(1     ,   len(_SCREAMING_SNAKE_CASE			) - 1			):
    lowerCAmelCase__			:Union[str, Any]											=							Pipe()
    lowerCAmelCase__			:List[str]											=							Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE     ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     ,   )			)
    lowerCAmelCase__			:Union[str, Any]											=							temp_rs
    lowerCAmelCase__			:Any											=							temp_rr

  process_array_.append(
      Process(
          target=_SCREAMING_SNAKE_CASE     ,   args=(
              len(_SCREAMING_SNAKE_CASE			) - 1,
              arr[len(_SCREAMING_SNAKE_CASE			) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(_SCREAMING_SNAKE_CASE			) - 1],
          )     ,   )			)

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0     ,   len(_SCREAMING_SNAKE_CASE			)			):
    lowerCAmelCase__			:str											=							result_pipe[p][0].recv()
    process_array_[p].join()
  return arr
def  __A   ()					->List[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Union[str, Any]											=							list(range(10     ,   0     ,   -1			)			)
  print('Initial List'			)
  print(*_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							odd_even_transposition(_SCREAMING_SNAKE_CASE			)
  print('Sorted List\n'			)
  print(*_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  main()
