


"""simple docstring"""



from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__snake_case       =       Lock()







def     __lowerCAmelCase		(		lowercase    :    Any		,				lowercase    :    Union[str, Any]		,				lowercase    :    List[Any]		,				lowercase    :    List[str]		,				lowercase    :    List[str]		,				lowercase    :    str		,				lowercase    :    Union[str, Any]					)      ->		Union[str, 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(lowercase					)
            process_lock.release()

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

            # take the lower value since you are on the left
            snake_case       : int    =       min(lowercase		,				lowercase					)
        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(lowercase					)
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            snake_case       : Union[str, Any]    =       lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            snake_case       : Optional[Any]    =       max(lowercase		,				lowercase					)
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(lowercase					)







def     __lowerCAmelCase		(		lowercase    :    Dict					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Union[str, Any]    =       []
    snake_case       : 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
    snake_case       : Optional[Any]    =       Pipe()
    snake_case       : Dict    =       Pipe()
    process_array_.append(
        Process(
            target=lowercase		,				args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])		,				)					)
    snake_case       : Optional[int]    =       temp_rs
    snake_case       : Union[str, Any]    =       temp_rr

    for i in range(1		,				len(lowercase					) - 1					):
        snake_case       : Tuple    =       Pipe()
        snake_case       : Dict    =       Pipe()
        process_array_.append(
            Process(
                target=lowercase		,				args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])		,				)					)
        snake_case       : str    =       temp_rs
        snake_case       : str    =       temp_rr

    process_array_.append(
        Process(
            target=lowercase		,				args=(
                len(lowercase					) - 1,
                arr[len(lowercase					) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(lowercase					) - 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(lowercase					)					):
        snake_case       : Any    =       result_pipe[p][0].recv()
        process_array_[p].join()
    return arr







def     __lowerCAmelCase		(		)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Any    =       list(range(10		,				0		,				-1					)					)
    print("Initial List"					)
    print(*lowercase					)
    snake_case       : Optional[Any]    =       odd_even_transposition(lowercase					)
    print("Sorted List\n"					)
    print(*lowercase					)


if __name__ == "__main__":
  main()









"""simple docstring"""



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




       super().__init__(
           is_encoder_decoder=UpperCamelCase__		,      tokenizer_class=UpperCamelCase__		,      tie_word_embeddings=UpperCamelCase__		,      pad_token_id=UpperCamelCase__		,      eos_token_id=UpperCamelCase__		,      decoder_start_token_id=UpperCamelCase__		,      **UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       d_model
       snake_case       : Optional[Any]    =       d_kv
       snake_case       : Any    =       d_ff
       snake_case       : Optional[Any]    =       num_layers
       snake_case       : Optional[Any]    =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case       : Optional[Any]    =       num_heads
       snake_case       : Optional[Any]    =       relative_attention_num_buckets
       snake_case       : Dict    =       relative_attention_max_distance
       snake_case       : Tuple    =       dropout_rate
       snake_case       : List[Any]    =       layer_norm_epsilon
       snake_case       : Any    =       initializer_factor
       snake_case       : Optional[Any]    =       feed_forward_proj
       snake_case       : Tuple    =       use_cache

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

       if len(UpperCamelCase__		) > 1 and act_info[0] != "gated" or len(UpperCamelCase__		) > 2:
           raise ValueError(
               F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"		)

       if feed_forward_proj == "gated-gelu":
           snake_case       : List[str]    =       "gelu_new"







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
           snake_case       : Tuple    =       "past_encoder_sequence + sequence"
           snake_case       : int    =       {0: "batch"}
           snake_case       : int    =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
           snake_case       : List[Any]    =       {0: "batch", 1: "decoder_sequence"}
           snake_case       : Any    =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
           self.fill_with_past_key_values_(UpperCamelCase__		,      direction="inputs"		)

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





