


from typing import Mapping

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


__UpperCAmelCase			:			Union[str, Any]			      =					logging.get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  str				=       "mt5"
       __UpperCamelCase   :  Dict				=       ["past_key_values"]






       def __init__(  self			,       __SCREAMING_SNAKE_CASE=250_112			,       __SCREAMING_SNAKE_CASE=512			,       __SCREAMING_SNAKE_CASE=64			,       __SCREAMING_SNAKE_CASE=1_024			,       __SCREAMING_SNAKE_CASE=8			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=6			,       __SCREAMING_SNAKE_CASE=32			,       __SCREAMING_SNAKE_CASE=128			,       __SCREAMING_SNAKE_CASE=0.1			,       __SCREAMING_SNAKE_CASE=1e-6			,       __SCREAMING_SNAKE_CASE=1.0			,       __SCREAMING_SNAKE_CASE="gated-gelu"			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE="T5Tokenizer"			,       __SCREAMING_SNAKE_CASE=False			,       __SCREAMING_SNAKE_CASE=0			,       __SCREAMING_SNAKE_CASE=1			,       __SCREAMING_SNAKE_CASE=0			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




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

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

              if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 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":
                     UpperCamelCase     :						Dict 						=		'''gelu_new'''






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.d_model






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.num_heads






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.num_layers




class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 			_lowercase (  self    ):




              """simple docstring"""




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

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

              return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 			_lowercase (  self    ):




              """simple docstring"""




              return 13






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return 5e-4





def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		len(SCREAMING_SNAKE_CASE_							)
       for i in range(length - 1							):
              UpperCamelCase     :						Optional[int] 						=		False
              for j in range(length - 1 - i							):
                     if collection[j] > collection[j + 1]:
                            UpperCamelCase     :						Optional[int] 						=		True
                            UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		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()

  __UpperCAmelCase			:			Any			      =					input("Enter numbers separated by a comma:").strip()
  __UpperCAmelCase			:			List[str]			      =					[int(item) for item in user_input.split(",")]
  __UpperCAmelCase			:			List[Any]			      =					time.process_time()
  print(*bubble_sort(unsorted), sep=",")
  print(f'''Processing time: {(time.process_time() - start)%1E9 + 7}''')

