



'''simple docstring'''





from typing import Mapping

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


A__  :					Any				= logging.get_logger(__name__)



class       snake_case__		(  __UpperCAmelCase							):
 A__							=      '''mt5'''
 A__							=      ['''past_key_values''']
 def __init__(					self		:				List[Any]	,			__a		:				Dict=250112	,			__a		:				Any=512	,			__a		:				str=64	,			__a		:				str=1024	,			__a		:				Dict=8	,			__a		:				List[str]=None	,			__a		:				Dict=6	,			__a		:				Any=32	,			__a		:				Optional[Any]=128	,			__a		:				Optional[int]=0.1	,			__a		:				Optional[Any]=1e-6	,			__a		:				Tuple=1.0	,			__a		:				List[str]="gated-gelu"	,			__a		:				Optional[int]=True	,			__a		:				List[Any]=True	,			__a		:				int="T5Tokenizer"	,			__a		:				Optional[int]=False	,			__a		:				Union[str, Any]=0	,			__a		:				int=1	,			__a		:				Any=0	,			**__a		:				List[str]	,			)	->					List[str]:







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

       __snake_case      :					List[Any]						=  self.feed_forward_proj.split('-'   )
       __snake_case      :					str						=  act_info[-1]
       __snake_case      :					Optional[int]						=  act_info[0] == '''gated'''

       if len(_lowerCamelCase   ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase   ) > 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\''   )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
             __snake_case      :					Tuple						=  '''gelu_new'''
 @property
 def A_					(					self		:				Optional[int]   )	->					Union[str, Any]:







       '''simple docstring'''
       return self.d_model
 @property
 def A_					(					self		:				Dict   )	->					List[str]:







       '''simple docstring'''
       return self.num_heads





 @property
 def A_					(					self		:				List[str]   )	->					Any:







       '''simple docstring'''
       return self.num_layers



class       snake_case__		(  __UpperCAmelCase							):
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def A_					(					self		:				str   )	->					Tuple:







       '''simple docstring'''
       __snake_case      :					str						=  {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
             __snake_case      :					Optional[Any]						=  '''past_encoder_sequence + sequence'''
             __snake_case      :					Optional[int]						=  {0: '''batch'''}
             __snake_case      :					Dict						=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
             __snake_case      :					List[str]						=  {0: '''batch''', 1: '''decoder_sequence'''}
             __snake_case      :					Tuple						=  {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
             self.fill_with_past_key_values_(_lowerCamelCase	,			direction='inputs'   )

       return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def A_					(					self		:				Any   )	->					Optional[int]:







       '''simple docstring'''
       return 13





 @property
 def A_					(					self		:				int   )	->					Optional[int]:







       '''simple docstring'''
       return 5e-4










"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       len(lowercase_		)
 for i in range(length - 1		):
  __SCREAMING_SNAKE_CASE :			str												=       False
  for j in range(length - 1 - i		):
   if collection[j] > collection[j + 1]:
    __SCREAMING_SNAKE_CASE :			Any												=       True
    __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
    import doctest
    import time

    doctest.testmod()

    _lowerCamelCase       =	input('''Enter numbers separated by a comma:''').strip()
    _lowerCamelCase       =	[int(item) for item in user_input.split(''',''')]
    _lowerCamelCase       =	time.process_time()
    print(*bubble_sort(unsorted), sep=''',''')
    print(f'Processing time: {(time.process_time() - start)%1e9 + 7}')

