
from typing import Mapping

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


lowerCamelCase__     :		Any        = logging.get_logger(__name__)

lowerCamelCase__     :		List[Any]        = {
    """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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[str]	   =      't5'
 __lowerCAmelCase	:  List[str]	   =      ['past_key_values']
 __lowerCAmelCase	:  List[str]	   =      {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3_21_28       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=64       ,	SCREAMING_SNAKE_CASE_=20_48       ,	SCREAMING_SNAKE_CASE_=6       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=8       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=1_28       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=1E-6       ,	SCREAMING_SNAKE_CASE_=1.0       ,	SCREAMING_SNAKE_CASE_="relu"       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=1       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   vocab_size
   lowercase__	: Union[str, Any]     		=   d_model
   lowercase__	: Union[str, Any]     		=   d_kv
   lowercase__	: List[Any]     		=   d_ff
   lowercase__	: Optional[Any]     		=   num_layers
   lowercase__	: Union[str, Any]     		=   (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   lowercase__	: Any     		=   num_heads
   lowercase__	: Tuple     		=   relative_attention_num_buckets
   lowercase__	: Union[str, Any]     		=   relative_attention_max_distance
   lowercase__	: str     		=   dropout_rate
   lowercase__	: Dict     		=   layer_norm_epsilon
   lowercase__	: Optional[int]     		=   initializer_factor
   lowercase__	: str     		=   feed_forward_proj
   lowercase__	: Optional[int]     		=   use_cache

   lowercase__	: str     		=   self.feed_forward_proj.split("""-""")
   lowercase__	: Tuple     		=   act_info[-1]
   lowercase__	: Optional[int]     		=   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":
     lowercase__	: str     		=   """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    _snake_case (						UpperCAmelCase_				):


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




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


   '''simple docstring'''




   return 13









UpperCAmelCase_							:					dict[tuple[int, int, int], int]		 =   {}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   int       )					->				int:

	# if we are absent twice, or late 3 consecutive days,
	# no further prize strings are possible
	if late == 3 or absent == 2:
		return 0

	# if we have no days left, and have not failed any other rules,
	# we have a prize string
	if days == 0:
		return 1

	# No easy solution, so now we need to do the recursive calculation

	# First, check if the combination is already in the cache, and
	# if yes, return the stored value from there since we already
	# know the number of possible prize strings from this point on
	__A :			List[Any]		    =			(days, absent, late)
	if key in cache:
		return cache[key]

	# now we calculate the three possible ways that can unfold from
	# this point on, depending on our attendance today

	# 1) if we are late (but not absent), the "absent" counter stays as
	# it is, but the "late" counter increases by one
	__A :			Dict		    =			_calculate(days - 1			,a__			,late + 1       )

	# 2) if we are absent, the "absent" counter increases by 1, and the
	# "late" counter resets to 0
	__A :			List[str]		    =			_calculate(days - 1			,absent + 1			,0       )

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	__A :			int		    =			_calculate(days - 1			,a__			,0       )

	__A :			Optional[int]		    =			state_late + state_absent + state_ontime

	__A :			Tuple		    =			prizestrings
	return prizestrings
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 30       )					->				int:
	return _calculate(a__			,absent=0			,late=0       )


if __name__ == "__main__":
			print(solution())





