


from typing import Mapping

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


UpperCAmelCase			=    logging.get_logger(__name__)







class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 __snake_case												=  "mt5"
 __snake_case												=  ["past_key_values"]



 def __init__(	self		,      _UpperCAmelCase=25_01_12		,      _UpperCAmelCase=5_12		,      _UpperCAmelCase=64		,      _UpperCAmelCase=10_24		,      _UpperCAmelCase=8		,      _UpperCAmelCase=None		,      _UpperCAmelCase=6		,      _UpperCAmelCase=32		,      _UpperCAmelCase=1_28		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1E-6		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase="gated-gelu"		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase="T5Tokenizer"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1		,      _UpperCAmelCase=0		,      **_UpperCAmelCase		,      ):
       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		,      )
       snake_case_       =      vocab_size
       snake_case_       =      d_model
       snake_case_       =      d_kv
       snake_case_       =      d_ff
       snake_case_       =      num_layers
       snake_case_       =      (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case_       =      num_heads
       snake_case_       =      relative_attention_num_buckets
       snake_case_       =      relative_attention_max_distance
       snake_case_       =      dropout_rate
       snake_case_       =      layer_norm_epsilon
       snake_case_       =      initializer_factor
       snake_case_       =      feed_forward_proj
       snake_case_       =      use_cache

       snake_case_       =      self.feed_forward_proj.split('''-'''					)
       snake_case_       =      act_info[-1]
       snake_case_       =      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\''''					)

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



 @property
 def 						UpperCamelCase__     (	self					):
       return self.d_model



 @property
 def 						UpperCamelCase__     (	self					):
       return self.num_heads





 @property
 def 						UpperCamelCase__     (	self					):
       return self.num_layers







class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      {
           '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
           '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
       }
       if self.use_past:
             snake_case_       =      '''past_encoder_sequence + sequence'''
             snake_case_       =      {0: '''batch'''}
             snake_case_       =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
             snake_case_       =      {0: '''batch''', 1: '''decoder_sequence'''}
             snake_case_       =      {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 						UpperCamelCase__     (	self					):
       return 13





 @property
 def 						UpperCamelCase__     (	self					):
       return 5E-4



def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')