




from typing import Mapping

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


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''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       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Dict			  =		'longt5'
	__magic_name__      :					Optional[int]			  =		['past_key_values']
	__magic_name__      :					Any			  =		{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}






	def __init__(				self		:	Optional[Any]				,    lowerCAmelCase		:	Optional[Any]=32128				,    lowerCAmelCase		:	Any=512				,    lowerCAmelCase		:	int=64				,    lowerCAmelCase		:	str=2048				,    lowerCAmelCase		:	Any=6				,    lowerCAmelCase		:	int=None				,    lowerCAmelCase		:	int=8				,    lowerCAmelCase		:	Optional[Any]=127				,    lowerCAmelCase		:	Union[str, Any]=16				,    lowerCAmelCase		:	int=32				,    lowerCAmelCase		:	Optional[Any]=128				,    lowerCAmelCase		:	List[str]=0.1				,    lowerCAmelCase		:	List[str]=1E-6				,    lowerCAmelCase		:	str=1.0				,    lowerCAmelCase		:	Optional[int]="relu"				,    lowerCAmelCase		:	str=True				,    lowerCAmelCase		:	Union[str, Any]="local"				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	int=0				,    lowerCAmelCase		:	Optional[Any]=1				,    **lowerCAmelCase		:	List[str]				,    )  ->					Optional[int]:







						"""simple docstring"""







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

						__UpperCamelCase				:  Union[str, Any]								=			self.feed_forward_proj.split("""-"""  )
						__UpperCamelCase				:  str								=			act_info[-1]
						__UpperCamelCase				:  Tuple								=			act_info[0] == """gated"""

						if len(lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase  ) > 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				:  Any								=			"""gelu_new"""

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






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	@property
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







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

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

						return common_inputs






	@property
	def 				lowerCamelCase__    (				self		:	int  )  ->					int:







						"""simple docstring"""







						return 13












a__		    =    {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

a__		    =    {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}





def 							A__   (snake_case      :		str		,  snake_case      :		str		,  snake_case      :		float							)    ->	float:
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										__UpperCamelCase				:  Any								=			(
										    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
										    F'''Supported values are: {", ".join(snake_case							)}'''
										)
										raise ValueError(snake_case							)
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






