






'''simple docstring'''



from typing import Mapping

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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    '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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''umt5'''
      UpperCamelCase_								=			['''past_key_values''']






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]=25_0112     ,      UpperCAmelCase			:		str=512     ,      UpperCAmelCase			:		Optional[int]=64     ,      UpperCAmelCase			:		List[Any]=1024     ,      UpperCAmelCase			:		Tuple=8     ,      UpperCAmelCase			:		List[Any]=None     ,      UpperCAmelCase			:		int=6     ,      UpperCAmelCase			:		Dict=32     ,      UpperCAmelCase			:		List[str]=128     ,      UpperCAmelCase			:		Dict=0.1     ,      UpperCAmelCase			:		Optional[int]=1e-6     ,      UpperCAmelCase			:		Optional[int]=1.0     ,      UpperCAmelCase			:		Optional[Any]="gated-gelu"     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		List[str]=True     ,      UpperCAmelCase			:		List[str]="T5Tokenizer"     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Tuple=0     ,      UpperCAmelCase			:		Optional[Any]=1     ,      UpperCAmelCase			:		int=0     ,      **UpperCAmelCase			:		Union[str, Any]     ,      )  ->  List[Any]:


       '''simple docstring'''

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

       lowercase					:   List[Any]				  =self.feed_forward_proj.split('''-'''		)
       lowercase					:   Dict				  =act_info[-1]
       lowercase					:   Tuple				  =act_info[0] == '''gated'''

       if len(UpperCAmelCase		) > 1 and act_info[0] != "gated" or len(UpperCAmelCase		) > 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					:   List[Any]				  ='''gelu_new'''






      @property
      def 				A__      ( self			:		Any		)  ->  List[Any]:


       '''simple docstring'''

       return self.d_model






      @property
      def 				A__      ( self			:		List[str]		)  ->  Optional[int]:


       '''simple docstring'''

       return self.num_heads






      @property
      def 				A__      ( self			:		Optional[int]		)  ->  Union[str, Any]:


       '''simple docstring'''

       return self.num_layers





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 				A__      ( self			:		List[Any]		)  ->  Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

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

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

       return common_inputs






      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 				A__      ( self			:		List[Any]		)  ->  int:


       '''simple docstring'''

       return 13






      @property
      def 				A__      ( self			:		Union[str, Any]		)  ->  float:


       '''simple docstring'''

       return 5e-4











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



