




from typing import Mapping

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


lowercase__							:	List[Any]											=		logging.get_logger(__name__)

lowercase__							:	Optional[Any]											=		{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  '''umt5'''
						lowerCAmelCase_							=  ['''past_key_values''']






						def __init__(		self				:	str			,      __lowercase				:	str=25_01_12			,      __lowercase				:	List[str]=5_12			,      __lowercase				:	Tuple=64			,      __lowercase				:	str=10_24			,      __lowercase				:	Any=8			,      __lowercase				:	List[Any]=None			,      __lowercase				:	List[Any]=6			,      __lowercase				:	Any=32			,      __lowercase				:	str=1_28			,      __lowercase				:	Optional[int]=0.1			,      __lowercase				:	Union[str, Any]=1E-6			,      __lowercase				:	Optional[Any]=1.0			,      __lowercase				:	List[Any]="gated-gelu"			,      __lowercase				:	str=True			,      __lowercase				:	int=True			,      __lowercase				:	Any="T5Tokenizer"			,      __lowercase				:	Optional[Any]=True			,      __lowercase				:	Optional[int]=0			,      __lowercase				:	List[Any]=1			,      __lowercase				:	Tuple=0			,      **__lowercase				:	Dict			,      ):






							"""simple docstring"""






							super().__init__(
							    is_encoder_decoder=__lowercase			,      tokenizer_class=__lowercase			,      tie_word_embeddings=__lowercase			,      pad_token_id=__lowercase			,      eos_token_id=__lowercase			,      decoder_start_token_id=__lowercase			,      **__lowercase			,      )
							snake_case_						       =	vocab_size
							snake_case_						       =	d_model
							snake_case_						       =	d_kv
							snake_case_						       =	d_ff
							snake_case_						       =	num_layers
							snake_case_						       =	(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							snake_case_						       =	num_heads
							snake_case_						       =	relative_attention_num_buckets
							snake_case_						       =	relative_attention_max_distance
							snake_case_						       =	dropout_rate
							snake_case_						       =	layer_norm_epsilon
							snake_case_						       =	initializer_factor
							snake_case_						       =	feed_forward_proj
							snake_case_						       =	use_cache

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

							if len(__lowercase		) > 1 and act_info[0] != "gated" or len(__lowercase		) > 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'"		)

							if feed_forward_proj == "gated-gelu":
								snake_case_						       =	"gelu_new"






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							return self.d_model






						@property
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							return self.num_heads






						@property
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							return self.num_layers







class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	{
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
								snake_case_						       =	"past_encoder_sequence + sequence"
								snake_case_						       =	{0: "batch"}
								snake_case_						       =	{0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
								snake_case_						       =	{0: "batch", 1: "decoder_sequence"}
								snake_case_						       =	{0: "batch", 1: "decoder_sequence"}

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

							return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							return 13






						@property
						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							return 5E-4






def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
