



'''simple docstring'''






from typing import Mapping

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


lowerCAmelCase:					int						       =						logging.get_logger(__name__)

lowerCAmelCase:					Dict						       =						{
    """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 							a__(					__UpperCAmelCase	):
    lowercase__ =    '''t5'''
    lowercase__ =    ['''past_key_values''']
    lowercase__ =    {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







    def __init__(      self :  Tuple						,			__snake_case :  str=3_21_28						,			__snake_case :  Tuple=5_12						,			__snake_case :  Optional[int]=64						,			__snake_case :  Any=20_48						,			__snake_case :  str=6						,			__snake_case :  Optional[Any]=None						,			__snake_case :  int=8						,			__snake_case :  Dict=32						,			__snake_case :  Optional[int]=1_28						,			__snake_case :  str=0.1						,			__snake_case :  Dict=1e-6						,			__snake_case :  List[Any]=1.0						,			__snake_case :  Union[str, Any]="relu"						,			__snake_case :  List[Any]=True						,			__snake_case :  Dict=True						,			__snake_case :  Dict=0						,			__snake_case :  Dict=1						,			**__snake_case :  str						,			):
      a						:					List[Any]        =     vocab_size
      a						:					str        =     d_model
      a						:					str        =     d_kv
      a						:					Any        =     d_ff
      a						:					Optional[Any]        =     num_layers
      a						:					Tuple        =     (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      a						:					Any        =     num_heads
      a						:					Any        =     relative_attention_num_buckets
      a						:					List[str]        =     relative_attention_max_distance
      a						:					str        =     dropout_rate
      a						:					str        =     layer_norm_epsilon
      a						:					Union[str, Any]        =     initializer_factor
      a						:					List[Any]        =     feed_forward_proj
      a						:					str        =     use_cache

      a						:					Optional[int]        =     self.feed_forward_proj.split('-'	)
      a						:					Optional[int]        =     act_info[-1]
      a						:					Tuple        =     act_info[0] == 'gated'

      if len(__snake_case	) > 1 and act_info[0] != "gated" or len(__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":
        a						:					int        =     'gelu_new'

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

class 							a__(					__UpperCAmelCase	):







    @property
    def 			lowercase_       (      self :  Optional[Any]	):
      a						:					str        =     {
          'input_ids': {0: 'batch', 1: 'encoder_sequence'},
          'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
      }
      if self.use_past:
        a						:					int        =     'past_encoder_sequence + sequence'
        a						:					int        =     {0: 'batch'}
        a						:					Dict        =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
        a						:					Any        =     {0: 'batch', 1: 'decoder_sequence'}
        a						:					Optional[Any]        =     {0: 'batch', 1: 'decoder_sequence'}

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

      return common_inputs







    @property
    def 			lowercase_       (      self :  str	):
      return 13






from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


