



'''simple docstring'''





from typing import Mapping

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


lowercase_			=  logging.get_logger(__name__)






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	str										=	'mt5'
 __lowerCamelCase   :	Union[str, Any]										=	['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=False			,       A=0			,       A=1			,       A=0			,       **A			,       )   ->					str:


      """simple docstring"""



      super().__init__(
          is_encoder_decoder=A			,       tokenizer_class=A			,       tie_word_embeddings=A			,       pad_token_id=A			,       eos_token_id=A			,       decoder_start_token_id=A			,       **A			,       )
      _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(A	) > 1 and act_info[0] != "gated" or len(A	) > 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\''''	)

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           _a     =  '''gelu_new'''



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


      """simple docstring"""



      return self.d_model



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


      """simple docstring"""



      return self.num_heads



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


      """simple docstring"""



      return self.num_layers






class __A      (  A						):



 '''simple docstring'''







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


      """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_(A			,       direction='''inputs'''	)

      return common_inputs



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


      """simple docstring"""



      return 13



 @property
 def  a__  (self	)   ->					float:


      """simple docstring"""



      return 5E-4








'''simple docstring'''





from math import gcd




def lowerCAmelCase  (__A = 12_000):



     """simple docstring"""



     _a     =  0
     for d in range(max_d + 1):
          for n in range(d // 3 + 1  ,     (d + 1) // 2):
               if gcd(__A  ,     __A) == 1:
                    fractions_number += 1
     return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")



