
'''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     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''umt5'''
					_lowerCAmelCase			     =	['''past_key_values''']
					def __init__(     self,    A=25_0112,    A=512,    A=64,    A=1024,    A=8,    A=None,    A=6,    A=32,    A=128,    A=0.1,    A=1e-6,    A=1.0,    A="gated-gelu",    A=True,    A=True,    A="T5Tokenizer",    A=True,    A=0,    A=1,    A=0,    **A,    ):

									"""simple docstring"""


									super().__init__(
									    is_encoder_decoder=A,    tokenizer_class=A,    tie_word_embeddings=A,    pad_token_id=A,    eos_token_id=A,    decoder_start_token_id=A,    **A,    )
									lowerCamelCase					:			Dict									= vocab_size
									lowerCamelCase					:			Optional[Any]									= d_model
									lowerCamelCase					:			Union[str, Any]									= d_kv
									lowerCamelCase					:			List[Any]									= d_ff
									lowerCamelCase					:			Tuple									= num_layers
									lowerCamelCase					:			Optional[int]									= (
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									lowerCamelCase					:			int									= num_heads
									lowerCamelCase					:			Optional[Any]									= relative_attention_num_buckets
									lowerCamelCase					:			int									= relative_attention_max_distance
									lowerCamelCase					:			Optional[int]									= dropout_rate
									lowerCamelCase					:			Union[str, Any]									= layer_norm_epsilon
									lowerCamelCase					:			Tuple									= initializer_factor
									lowerCamelCase					:			Optional[int]									= feed_forward_proj
									lowerCamelCase					:			Any									= use_cache

									lowerCamelCase					:			int									= self.feed_forward_proj.split('-'      )
									lowerCamelCase					:			Optional[Any]									= act_info[-1]
									lowerCamelCase					:			Tuple									= 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\''      )

									if feed_forward_proj == "gated-gelu":
													lowerCamelCase					:			Dict									= 'gelu_new'
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									return self.num_heads




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.num_layers





class     __snake_case		(   a__):
					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= {
									    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
									    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
									}
									if self.use_past:
													lowerCamelCase					:			Tuple									= 'past_encoder_sequence + sequence'
													lowerCamelCase					:			int									= {0: 'batch'}
													lowerCamelCase					:			int									= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
									else:
													lowerCamelCase					:			Optional[Any]									= {0: 'batch', 1: 'decoder_sequence'}
													lowerCamelCase					:			Dict									= {0: 'batch', 1: 'decoder_sequence'}

									if self.use_past:
													self.fill_with_past_key_values_(A,    direction='inputs'      )

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

									"""simple docstring"""


									return 13




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 5e-4


'''simple docstring'''






import numpy as np





def    UpperCAmelCase							(  UpperCAmelCase__							:							np.array):
				return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
