



'''simple docstring'''





from typing import Mapping

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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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			(  __lowerCamelCase						):



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       'umt5'
 __lowerCamelCase    :       Optional[int]          =       ['past_key_values']



 def __init__(self ,   A=250_112 ,   A=512 ,   A=64 ,   A=1_024 ,   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 ,   )    ->     int:


      """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_ ,   )
      _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(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":
           _a      =  '''gelu_new'''



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self.d_model



 @property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      return self.num_heads



 @property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      return self.num_layers






class __A			(  __lowerCamelCase						):



 '''simple docstring'''







 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _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_(UpperCamelCase_ ,   direction='''inputs'''       )

      return common_inputs



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      return 13



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return 5E-4









from __future__ import annotations




def 					lowerCamelCase__       (						A__       :					list[int | float]  ,			A__       :					int  ,			A__       :					int	):





       '''simple docstring'''



       if len(A__	) == 0:
              raise ValueError("""find_max() arg is an empty sequence"""	)
       if (
           left >= len(A__	)
           or left < -len(A__	)
           or right >= len(A__	)
           or right < -len(A__	)
       ):
              raise IndexError("""list index out of range"""	)
       if left == right:
              return nums[left]
       __lowerCamelCase   =						(left + right) >> 1  # the middle
       __lowerCamelCase   =						find_max(A__  ,			A__  ,			A__	)  # find max in range[left, mid]
       __lowerCamelCase   =						find_max(A__  ,			mid + 1  ,			A__	)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)

