
'''simple docstring'''






from typing import Mapping

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


UpperCAmelCase_       :   Tuple   =       logging.get_logger(__name__)

UpperCAmelCase_       :   str   =       {
    '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  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'umt5'
    lowerCAmelCase_				    =		['past_key_values']



    def __init__(	self      :       int,__A      :       List[Any]=2_5_0_1_1_2,__A      :       Optional[int]=5_1_2,__A      :       Tuple=6_4,__A      :       Optional[int]=1_0_2_4,__A      :       Optional[Any]=8,__A      :       Tuple=None,__A      :       int=6,__A      :       List[str]=3_2,__A      :       List[Any]=1_2_8,__A      :       Union[str, Any]=0.1,__A      :       Optional[int]=1e-6,__A      :       List[Any]=1.0,__A      :       Dict="gated-gelu",__A      :       List[Any]=True,__A      :       Dict=True,__A      :       List[Any]="T5Tokenizer",__A      :       int=True,__A      :       Optional[Any]=0,__A      :       Any=1,__A      :       Optional[Any]=0,**__A      :       Tuple,):
          super().__init__(
              is_encoder_decoder=__A,tokenizer_class=__A,tie_word_embeddings=__A,pad_token_id=__A,eos_token_id=__A,decoder_start_token_id=__A,**__A,)
          _lowerCamelCase				:   List[Any]								=   vocab_size
          _lowerCamelCase				:   Tuple								=   d_model
          _lowerCamelCase				:   Any								=   d_kv
          _lowerCamelCase				:   Dict								=   d_ff
          _lowerCamelCase				:   Optional[int]								=   num_layers
          _lowerCamelCase				:   Any								=   (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _lowerCamelCase				:   List[Any]								=   num_heads
          _lowerCamelCase				:   Optional[int]								=   relative_attention_num_buckets
          _lowerCamelCase				:   Tuple								=   relative_attention_max_distance
          _lowerCamelCase				:   List[str]								=   dropout_rate
          _lowerCamelCase				:   Union[str, Any]								=   layer_norm_epsilon
          _lowerCamelCase				:   Optional[int]								=   initializer_factor
          _lowerCamelCase				:   Optional[int]								=   feed_forward_proj
          _lowerCamelCase				:   Optional[int]								=   use_cache

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

          if len(__A     ) > 1 and act_info[0] != "gated" or len(__A     ) > 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":
                _lowerCamelCase				:   str								=   "gelu_new"



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return self.d_model



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return self.num_heads



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return self.num_layers


class  UpperCAmelCase__						(       A ):



    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   List[str]								=   {
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
                _lowerCamelCase				:   Optional[Any]								=   "past_encoder_sequence + sequence"
                _lowerCamelCase				:   Union[str, Any]								=   {0: "batch"}
                _lowerCamelCase				:   List[Any]								=   {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
                _lowerCamelCase				:   int								=   {0: "batch", 1: "decoder_sequence"}
                _lowerCamelCase				:   List[Any]								=   {0: "batch", 1: "decoder_sequence"}

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

          return common_inputs



    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def   lowerCamelCase_  (	self      :       str     ):
          return 1_3



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return 5e-4

'''simple docstring'''






from __future__ import annotations







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if (direction == 1 and array[indexa] > array[indexa]) or (
          direction == 0 and array[indexa] < array[indexa]
      ):
            _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   array[indexa], array[indexa]







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[int]								=   int(length / 2							)
            for i in range(_lowerCAmelCase				,	low + middle							):
                  comp_and_swap(_lowerCAmelCase				,	_lowerCAmelCase				,	i + middle				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            bitonic_merge(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    list[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if length > 1:
            _lowerCamelCase				:   Optional[Any]								=   int(length / 2							)
            bitonic_sort(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	1							)
            bitonic_sort(_lowerCAmelCase				,	low + middle				,	_lowerCAmelCase				,	0							)
            bitonic_merge(_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)


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

 bitonic_sort(unsorted, 0, len(unsorted), 1)
 print('\nSorted array in ascending order is: ', end='')
 print(*unsorted, sep=', ')

 bitonic_merge(unsorted, 0, len(unsorted), 0)
 print('Sorted array in descending order is: ', end='')
 print(*unsorted, sep=', ')