



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






UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

