
'''simple docstring'''



import math
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   str:




   """simple docstring"""
   if number == 0:
      _SCREAMING_SNAKE_CASE          = """Zero"""
      return words
   else:
      _SCREAMING_SNAKE_CASE          = math.logaa(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = digits + 1
      _SCREAMING_SNAKE_CASE          = {}
      _SCREAMING_SNAKE_CASE          = """"""
      _SCREAMING_SNAKE_CASE          = """One"""
      _SCREAMING_SNAKE_CASE          = """Two"""
      _SCREAMING_SNAKE_CASE          = """Three"""
      _SCREAMING_SNAKE_CASE          = """Four"""
      _SCREAMING_SNAKE_CASE          = """Five"""
      _SCREAMING_SNAKE_CASE          = """Six"""
      _SCREAMING_SNAKE_CASE          = """Seven"""
      _SCREAMING_SNAKE_CASE          = """Eight"""
      _SCREAMING_SNAKE_CASE          = """Nine"""

      _SCREAMING_SNAKE_CASE          = {}
      _SCREAMING_SNAKE_CASE          = """"""
      _SCREAMING_SNAKE_CASE          = """Twenty"""
      _SCREAMING_SNAKE_CASE          = """Thirty"""
      _SCREAMING_SNAKE_CASE          = """Forty"""
      _SCREAMING_SNAKE_CASE          = """Fifty"""
      _SCREAMING_SNAKE_CASE          = """Sixty"""
      _SCREAMING_SNAKE_CASE          = """Seventy"""
      _SCREAMING_SNAKE_CASE          = """Eighty"""
      _SCREAMING_SNAKE_CASE          = """Ninety"""

      _SCREAMING_SNAKE_CASE          = {}
      _SCREAMING_SNAKE_CASE          = """Ten"""
      _SCREAMING_SNAKE_CASE          = """Eleven"""
      _SCREAMING_SNAKE_CASE          = """Twelve"""
      _SCREAMING_SNAKE_CASE          = """Thirteen"""
      _SCREAMING_SNAKE_CASE          = """Fourteen"""
      _SCREAMING_SNAKE_CASE          = """Fifteen"""
      _SCREAMING_SNAKE_CASE          = """Sixteen"""
      _SCREAMING_SNAKE_CASE          = """Seventeen"""
      _SCREAMING_SNAKE_CASE          = """Eighteen"""
      _SCREAMING_SNAKE_CASE          = """Nineteen"""

      _SCREAMING_SNAKE_CASE          = {}
      _SCREAMING_SNAKE_CASE          = """Hundred,"""
      _SCREAMING_SNAKE_CASE          = """Thousand,"""
      _SCREAMING_SNAKE_CASE          = """Lakh,"""
      _SCREAMING_SNAKE_CASE          = """Crore,"""

      _SCREAMING_SNAKE_CASE          = number
      _SCREAMING_SNAKE_CASE          = """"""
      _SCREAMING_SNAKE_CASE          = 0
      _SCREAMING_SNAKE_CASE          = int(SCREAMING_SNAKE_CASE_  )
      while counter < digits:
         _SCREAMING_SNAKE_CASE          = temp_num % 10
         if counter % 2 == 0:
            _SCREAMING_SNAKE_CASE          = """"""
            if counter in placevalue and current != 0:
               _SCREAMING_SNAKE_CASE          = placevalue[counter]
            if counter == 2:
               _SCREAMING_SNAKE_CASE          = singles[current] + addition + words
            elif counter == 0:
               if ((temp_num % 1_00) // 10) == 1:
                  _SCREAMING_SNAKE_CASE          = teens[current] + addition + words
                  _SCREAMING_SNAKE_CASE          = temp_num // 10
                  counter += 1
               else:
                  _SCREAMING_SNAKE_CASE          = singles[current] + addition + words

            else:
               _SCREAMING_SNAKE_CASE          = doubles[current] + addition + words

         else:
            if counter == 1:
               if current == 1:
                  _SCREAMING_SNAKE_CASE          = teens[number % 10] + words
               else:
                  _SCREAMING_SNAKE_CASE          = """"""
                  if counter in placevalue:
                     _SCREAMING_SNAKE_CASE          = placevalue[counter]
                  _SCREAMING_SNAKE_CASE          = doubles[current] + addition + words
            else:
               _SCREAMING_SNAKE_CASE          = """"""
               if counter in placevalue:
                  if current == 0 and ((temp_num % 1_00) // 10) == 0:
                     _SCREAMING_SNAKE_CASE          = """"""
                  else:
                     _SCREAMING_SNAKE_CASE          = placevalue[counter]
               if ((temp_num % 1_00) // 10) == 1:
                  _SCREAMING_SNAKE_CASE          = teens[current] + addition + words
                  _SCREAMING_SNAKE_CASE          = temp_num // 10
                  counter += 1
               else:
                  _SCREAMING_SNAKE_CASE          = singles[current] + addition + words
         counter += 1
         _SCREAMING_SNAKE_CASE          = temp_num // 10
   return words


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



from typing import Mapping

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


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							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__				,							)			->	Union[str, Any]:
          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__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = 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":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






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






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




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






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






