


from typing import Mapping

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


_snake_case  :	Tuple			      =       logging.get_logger(__name__)

_snake_case  :	Union[str, 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        a	(A_   ):




       """simple docstring"""


       __UpperCAmelCase					:							Tuple  						=						'''umt5'''
       __UpperCAmelCase					:							List[Any]  						=						['''past_key_values''']

       def __init__(							self :						Any					,			lowerCamelCase :						Tuple=250112					,			lowerCamelCase :						Dict=512					,			lowerCamelCase :						int=64					,			lowerCamelCase :						str=1024					,			lowerCamelCase :						List[str]=8					,			lowerCamelCase :						Union[str, Any]=None					,			lowerCamelCase :						str=6					,			lowerCamelCase :						List[Any]=32					,			lowerCamelCase :						List[str]=128					,			lowerCamelCase :						Union[str, Any]=0.1					,			lowerCamelCase :						List[Any]=1E-6					,			lowerCamelCase :						List[Any]=1.0					,			lowerCamelCase :						Optional[int]="gated-gelu"					,			lowerCamelCase :						Tuple=True					,			lowerCamelCase :						Tuple=True					,			lowerCamelCase :						int="T5Tokenizer"					,			lowerCamelCase :						Optional[int]=True					,			lowerCamelCase :						Tuple=0					,			lowerCamelCase :						int=1					,			lowerCamelCase :						List[Any]=0					,			**lowerCamelCase :						str					,			)			->					Union[str, Any]:
              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					,			)
              __snake_case	:    Optional[int]											=     vocab_size
              __snake_case	:    Dict											=     d_model
              __snake_case	:    Any											=     d_kv
              __snake_case	:    Optional[int]											=     d_ff
              __snake_case	:    List[str]											=     num_layers
              __snake_case	:    Any											=     (
                  num_decoder_layers if num_decoder_layers is not None else self.num_layers
              )  # default = symmetry
              __snake_case	:    List[str]											=     num_heads
              __snake_case	:    Tuple											=     relative_attention_num_buckets
              __snake_case	:    Union[str, Any]											=     relative_attention_max_distance
              __snake_case	:    str											=     dropout_rate
              __snake_case	:    List[str]											=     layer_norm_epsilon
              __snake_case	:    Tuple											=     initializer_factor
              __snake_case	:    Tuple											=     feed_forward_proj
              __snake_case	:    Dict											=     use_cache

              __snake_case	:    Dict											=     self.feed_forward_proj.split("-"       )
              __snake_case	:    List[str]											=     act_info[-1]
              __snake_case	:    Any											=     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":
                     __snake_case	:    Optional[int]											=     """gelu_new"""

       @property
       def 					__snake_case      (							self :						Any       )			->					Any:
              return self.d_model

       @property
       def 					__snake_case      (							self :						Optional[Any]       )			->					Dict:
              return self.num_heads






       @property
       def 					__snake_case      (							self :						Optional[Any]       )			->					int:
              return self.num_layers

class        a	(A_   ):




       """simple docstring"""


       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 					__snake_case      (							self :						str       )			->					Dict:
              __snake_case	:    int											=     {
                  """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
                  """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
              }
              if self.use_past:
                     __snake_case	:    Optional[int]											=     """past_encoder_sequence + sequence"""
                     __snake_case	:    Optional[int]											=     {0: """batch"""}
                     __snake_case	:    Tuple											=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                     __snake_case	:    Any											=     {0: """batch""", 1: """decoder_sequence"""}
                     __snake_case	:    List[str]											=     {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 					__snake_case      (							self :						Union[str, Any]       )			->					Optional[int]:
              return 13






       @property
       def 					__snake_case      (							self :						Optional[Any]       )			->					Optional[int]:
              return 5E-4





from maths.prime_check import is_prime







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[str]											=     F'Input value of [number={number}] must be an integer'
														raise TypeError(__lowerCamelCase			)
							if is_prime(__lowerCamelCase			) and is_prime(number + 2			):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

