


from typing import Mapping

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


__snake_case      = logging.get_logger(__name__)

__snake_case      = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class  UpperCAmelCase       (							__snake_case     ):
			lowercase						       =			"t5"
			lowercase						       =			["past_key_values"]
			lowercase						       =			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




			def __init__(    self				:		Any							,							__magic_name__				:		Any=3_2_1_2_8							,							__magic_name__				:		int=5_1_2							,							__magic_name__				:		int=6_4							,							__magic_name__				:		List[str]=2_0_4_8							,							__magic_name__				:		Any=6							,							__magic_name__				:		str=None							,							__magic_name__				:		List[Any]=8							,							__magic_name__				:		Tuple=3_2							,							__magic_name__				:		int=1_2_8							,							__magic_name__				:		List[Any]=0.1							,							__magic_name__				:		Optional[int]=1e-6							,							__magic_name__				:		List[str]=1.0							,							__magic_name__				:		str="relu"							,							__magic_name__				:		List[str]=True							,							__magic_name__				:		Any=True							,							__magic_name__				:		List[str]=0							,							__magic_name__				:		str=1							,							**__magic_name__				:		Dict							,							):







						"""simple docstring"""







						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(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								=       """gelu_new"""

						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__							,							eos_token_id=SCREAMING_SNAKE_CASE__							,							is_encoder_decoder=SCREAMING_SNAKE_CASE__							,							**SCREAMING_SNAKE_CASE__							,							)




class  UpperCAmelCase       (							__snake_case     ):




			@property
			def 	lowerCamelCase_       (    self				:		Any    ):







						"""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_(SCREAMING_SNAKE_CASE__							,							direction="""inputs"""    )

						return common_inputs





			@property
			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						return 1_3






def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


