





"""simple docstring"""





from typing import Mapping

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


_lowerCamelCase       =	logging.get_logger(__name__)





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''mt5'''
      lowerCamelCase__						= ['''past_key_values''']

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

       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.feed_forward_proj.split('''-'''						)
       __SCREAMING_SNAKE_CASE :			str												=       act_info[-1]
       __SCREAMING_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":
        __SCREAMING_SNAKE_CASE :			Tuple												=       '''gelu_new'''

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       return self.d_model

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       return self.num_heads



      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       return self.num_layers







class 			snake_case		(   __UpperCAmelCase   ):

      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			str												=       {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       '''past_encoder_sequence + sequence'''
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       {0: '''batch'''}
        __SCREAMING_SNAKE_CASE :			Dict												=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        __SCREAMING_SNAKE_CASE :			List[str]												=       {0: '''batch''', 1: '''decoder_sequence'''}
        __SCREAMING_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 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       return 1_3



      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       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}')

