


from typing import Mapping

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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '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 					__magic_name__		(			a__     ):
       __A			:      Optional[int]        =   """umt5"""
       __A			:      Optional[Any]        =   ["""past_key_values"""]






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    int=2_5_0_1_1_2  ,     snake_case__  :    List[str]=5_1_2  ,     snake_case__  :    Optional[int]=6_4  ,     snake_case__  :    Optional[int]=1_0_2_4  ,     snake_case__  :    List[Any]=8  ,     snake_case__  :    Tuple=None  ,     snake_case__  :    Optional[Any]=6  ,     snake_case__  :    Optional[Any]=3_2  ,     snake_case__  :    Tuple=1_2_8  ,     snake_case__  :    List[Any]=0.1  ,     snake_case__  :    Optional[int]=1e-6  ,     snake_case__  :    List[Any]=1.0  ,     snake_case__  :    Any="gated-gelu"  ,     snake_case__  :    Optional[int]=True  ,     snake_case__  :    Dict=True  ,     snake_case__  :    Any="T5Tokenizer"  ,     snake_case__  :    Any=True  ,     snake_case__  :    Optional[int]=0  ,     snake_case__  :    Optional[int]=1  ,     snake_case__  :    Any=0  ,     **snake_case__  :    Union[str, Any]  ,     ):

              '''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__  ,     )
              lowercase		:str       							=				vocab_size
              lowercase		:Any       							=				d_model
              lowercase		:Union[str, Any]       							=				d_kv
              lowercase		:List[str]       							=				d_ff
              lowercase		:Dict       							=				num_layers
              lowercase		:Tuple       							=				(
                  num_decoder_layers if num_decoder_layers is not None else self.num_layers
              )  # default = symmetry
              lowercase		:List[Any]       							=				num_heads
              lowercase		:Dict       							=				relative_attention_num_buckets
              lowercase		:List[Any]       							=				relative_attention_max_distance
              lowercase		:int       							=				dropout_rate
              lowercase		:List[Any]       							=				layer_norm_epsilon
              lowercase		:Tuple       							=				initializer_factor
              lowercase		:List[str]       							=				feed_forward_proj
              lowercase		:Optional[Any]       							=				use_cache

              lowercase		:Optional[Any]       							=				self.feed_forward_proj.split('''-'''				)
              lowercase		:str       							=				act_info[-1]
              lowercase		:int       							=				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":
                     lowercase		:Optional[int]       							=				'''gelu_new'''






       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return self.d_model






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              return self.num_heads







       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              return self.num_layers







class 					__magic_name__		(			a__     ):






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

              '''simple docstring'''






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

              '''simple docstring'''






              return 1_3







       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return 5e-4





"""simple docstring"""



def       lowerCamelCase					(a_						:int)     ->							str:
       if number > 0:
              raise ValueError('''input must be a negative integer''')
       lowercase		:Any       							=				len(bin(a_)[3:])
       lowercase		:Any       							=				bin(abs(a_) - (1 << binary_number_length))[3:]
       lowercase		:Tuple       							=				(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(a_))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
 import doctest

 doctest.testmod()

