



from typing import Mapping

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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       """longt5"""
							UpperCamelCase__					:   Optional[int]       =       ["""past_key_values"""]
							UpperCamelCase__					:   Any       =       {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


							def __init__(				self	,	a_=32_128	,	a_=512	,	a_=64	,	a_=2_048	,	a_=6	,	a_=None	,	a_=8	,	a_=127	,	a_=16	,	a_=32	,	a_=128	,	a_=0.1	,	a_=1E-6	,	a_=1.0	,	a_="relu"	,	a_=True	,	a_="local"	,	a_=True	,	a_=0	,	a_=1	,	**a_	,	):
											a__					   =		vocab_size
											a__					   =		d_model
											a__					   =		d_kv
											a__					   =		d_ff
											a__					   =		num_layers
											# default = symmetry
											a__					   =		num_decoder_layers if num_decoder_layers is not None else self.num_layers
											a__					   =		num_heads
											a__					   =		local_radius
											a__					   =		global_block_size
											a__					   =		relative_attention_num_buckets
											a__					   =		relative_attention_max_distance
											a__					   =		dropout_rate
											a__					   =		layer_norm_epsilon
											a__					   =		initializer_factor
											a__					   =		feed_forward_proj
											a__					   =		encoder_attention_type
											a__					   =		use_cache

											a__					   =		self.feed_forward_proj.split("""-"""		)
											a__					   =		act_info[-1]
											a__					   =		act_info[0] == """gated"""

											if len(a_		) > 1 and act_info[0] != "gated" or len(a_		) > 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__					   =		"""gelu_new"""

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




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@property
							def _a   (				self		):
											a__					   =		{
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
															a__					   =		"""past_encoder_sequence + sequence"""
															a__					   =		{0: """batch"""}
															a__					   =		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
															a__					   =		{0: """batch""", 1: """decoder_sequence"""}
															a__					   =		{0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
															self.fill_with_past_key_values_(a_	,	direction="""inputs"""		)

											return common_inputs







							@property
							def _a   (				self		):
											return 13





def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     if n < 0:
          return 0
     # table[i] represents the number of ways to get to amount i
     __UpperCamelCase       				=[0] * (n + 1)

     # There is exactly 1 way to get to zero(You pick no coins).
     __UpperCamelCase       				=1

     # Pick all coins one by one and update table[] values
     # after the index greater than or equal to the value of the
     # picked coin
     for coin_val in s:
          for j in range(SCREAMING_SNAKE_CASE__     ,	n + 1							):
               table[j] += table[j - coin_val]

     return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





