





'''simple docstring'''





from typing import Mapping

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


A_												=     logging.get_logger(__name__)

A_												=     {
    "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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "umt5"
					A__				 =    ["past_key_values"]





					def __init__( self	,  _lowerCAmelCase=25_0112	,  _lowerCAmelCase=512	,  _lowerCAmelCase=64	,  _lowerCAmelCase=1024	,  _lowerCAmelCase=8	,  _lowerCAmelCase=None	,  _lowerCAmelCase=6	,  _lowerCAmelCase=32	,  _lowerCAmelCase=128	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=1E-6	,  _lowerCAmelCase=1.0	,  _lowerCAmelCase="gated-gelu"	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase="T5Tokenizer"	,  _lowerCAmelCase=True	,  _lowerCAmelCase=0	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  **_lowerCAmelCase	,  ):
										super().__init__(
										    is_encoder_decoder=_lowerCAmelCase	,  tokenizer_class=_lowerCAmelCase	,  tie_word_embeddings=_lowerCAmelCase	,  pad_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  decoder_start_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	,  )
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       d_model
										lowerCamelCase__       =       d_kv
										lowerCamelCase__       =       d_ff
										lowerCamelCase__       =       num_layers
										lowerCamelCase__       =       (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowerCamelCase__       =       num_heads
										lowerCamelCase__       =       relative_attention_num_buckets
										lowerCamelCase__       =       relative_attention_max_distance
										lowerCamelCase__       =       dropout_rate
										lowerCamelCase__       =       layer_norm_epsilon
										lowerCamelCase__       =       initializer_factor
										lowerCamelCase__       =       feed_forward_proj
										lowerCamelCase__       =       use_cache

										lowerCamelCase__       =       self.feed_forward_proj.split("-"	)
										lowerCamelCase__       =       act_info[-1]
										lowerCamelCase__       =       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'"	)

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





					@property
					def 					__magic_name__ ( self	):
										return self.d_model





					@property
					def 					__magic_name__ ( self	):
										return self.num_heads






					@property
					def 					__magic_name__ ( self	):
										return self.num_layers

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       {
										    "input_ids": {0: "batch", 1: "encoder_sequence"},
										    "attention_mask": {0: "batch", 1: "encoder_sequence"},
										}
										if self.use_past:
															lowerCamelCase__       =       "past_encoder_sequence + sequence"
															lowerCamelCase__       =       {0: "batch"}
															lowerCamelCase__       =       {0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
															lowerCamelCase__       =       {0: "batch", 1: "decoder_sequence"}
															lowerCamelCase__       =       {0: "batch", 1: "decoder_sequence"}

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

										return common_inputs





					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
					def 					__magic_name__ ( self	):
										return 13






					@property
					def 					__magic_name__ ( self	):
										return 5E-4








def    __UpperCamelCase      (    a,     a,     a=False) ->Dict:

					if isinstance(a,     a) and isinstance(a,     a):
										lowerCamelCase__       =       len(set_a.intersection(a))

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
										else:
															lowerCamelCase__       =       len(set_a.union(a))

										return intersection / union

					if isinstance(a,     (list, tuple)) and isinstance(a,     (list, tuple)):
										lowerCamelCase__       =       [element for element in set_a if element in set_b]

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
															return len(a) / union
										else:
															lowerCamelCase__       =       set_a + [element for element in set_b if element not in set_a]
															return len(a) / len(a)

										return len(a) / len(a)
					return None


if __name__ == "__main__":
						A_												=     {"a", "b", "c", "d", "e"}
						A_												=     {"c", "d", "e", "f", "h", "i"}
						print(jaccard_similarity(set_a, set_b))

