"""simple docstring"""



from typing import Mapping

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


SCREAMING_SNAKE_CASE__:Any			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Dict			=	{
    """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__     (   snake_case_   ):
 _snake_case   :			int           =		"""umt5"""
 _snake_case   :			str           =		["""past_key_values"""]


 def __init__(				self  ,						lowerCamelCase=250112  ,						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  ,						)
        __a     				=  vocab_size
        __a     				=  d_model
        __a     				=  d_kv
        __a     				=  d_ff
        __a     				=  num_layers
        __a     				=  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __a     				=  num_heads
        __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     				=  use_cache

        __a     				=  self.feed_forward_proj.split("-"		)
        __a     				=  act_info[-1]
        __a     				=  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":
               __a     				=  "gelu_new"


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


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






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






class 				snake_case__     (   snake_case_   ):


 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 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_(lowerCamelCase  ,						direction="inputs"		)

        return common_inputs


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






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

"""simple docstring"""



from ..utils import DummyObject, requires_backends






class 				snake_case__     (   metaclass=snake_case_   ):
 _snake_case   :			Union[str, Any]           =		["""onnx"""]


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(self  ,						["onnx"]		)


 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)






 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)
