from typing import Mapping

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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE_		(			lowerCamelCase_		):
   __lowerCAmelCase											=							"""mt5"""
   __lowerCAmelCase											=							["""past_key_values"""]







   def __init__(    self	:	Dict      ,      lowerCamelCase_	:	Any=25_0112      ,      lowerCamelCase_	:	Optional[Any]=512      ,      lowerCamelCase_	:	List[Any]=64      ,      lowerCamelCase_	:	str=1024      ,      lowerCamelCase_	:	Optional[Any]=8      ,      lowerCamelCase_	:	str=None      ,      lowerCamelCase_	:	int=6      ,      lowerCamelCase_	:	List[Any]=32      ,      lowerCamelCase_	:	Tuple=128      ,      lowerCamelCase_	:	Any=0.1      ,      lowerCamelCase_	:	List[Any]=1E-6      ,      lowerCamelCase_	:	List[Any]=1.0      ,      lowerCamelCase_	:	List[str]="gated-gelu"      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	List[Any]=True      ,      lowerCamelCase_	:	List[str]="T5Tokenizer"      ,      lowerCamelCase_	:	int=False      ,      lowerCamelCase_	:	Any=0      ,      lowerCamelCase_	:	Optional[int]=1      ,      lowerCamelCase_	:	Optional[int]=0      ,      **lowerCamelCase_	:	int      ,      ):





    """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_      ,      )
    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			d_model
    UpperCamelCase      =			d_kv
    UpperCamelCase      =			d_ff
    UpperCamelCase      =			num_layers
    UpperCamelCase      =			(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    UpperCamelCase      =			num_heads
    UpperCamelCase      =			relative_attention_num_buckets
    UpperCamelCase      =			relative_attention_max_distance
    UpperCamelCase      =			dropout_rate
    UpperCamelCase      =			layer_norm_epsilon
    UpperCamelCase      =			initializer_factor
    UpperCamelCase      =			feed_forward_proj
    UpperCamelCase      =			use_cache

    UpperCamelCase      =			self.feed_forward_proj.split("""-"""							)
    UpperCamelCase      =			act_info[-1]
    UpperCamelCase      =			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":
     UpperCamelCase      =			"gelu_new"







   @property
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return self.d_model







   @property
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    return self.num_heads







   @property
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return self.num_layers






class SCREAMING_SNAKE_CASE_		(			lowerCamelCase_		):







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





    """simple docstring"""


    UpperCamelCase      =			{
        "input_ids": {0: "batch", 1: "encoder_sequence"},
        "attention_mask": {0: "batch", 1: "encoder_sequence"},
    }
    if self.use_past:
     UpperCamelCase      =			"past_encoder_sequence + sequence"
     UpperCamelCase      =			{0: "batch"}
     UpperCamelCase      =			{0: "batch", 1: "past_decoder_sequence + sequence"}
    else:
     UpperCamelCase      =			{0: "batch", 1: "decoder_sequence"}
     UpperCamelCase      =			{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 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return 13







   @property
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    return 5E-4

def  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			len(UpperCamelCase_					)
 for i in range(length - 1					):
  UpperCamelCase      =			False
  for j in range(length - 1 - i					):
   if collection[j] > collection[j + 1]:
    UpperCamelCase      =			True
    UpperCamelCase     ,    UpperCamelCase      =			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()

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