code: 
  '''simple docstring'''
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  UpperCamelCase_     			= logging.get_logger(__name__)
  
  
  class   a_  (_a    ):
         __lowerCAmelCase    :				Tuple    =      "mt5"
         __lowerCAmelCase    :				Optional[Any]    =      ["past_key_values"]
  
  
  
  
         def __init__(     self   ,   snake_case_=2_5_0_1_1_2   ,   snake_case_=5_1_2   ,   snake_case_=6_4   ,   snake_case_=1_0_2_4   ,   snake_case_=8   ,   snake_case_=None   ,   snake_case_=6   ,   snake_case_=3_2   ,   snake_case_=1_2_8   ,   snake_case_=0.1   ,   snake_case_=1E-6   ,   snake_case_=1.0   ,   snake_case_="gated-gelu"   ,   snake_case_=True   ,   snake_case_=True   ,   snake_case_="T5Tokenizer"   ,   snake_case_=False   ,   snake_case_=0   ,   snake_case_=1   ,   snake_case_=0   ,   **snake_case_   ,   ):
           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_   ,   )
           _lowerCAmelCase       :  Optional[Any]   				=					vocab_size
           _lowerCAmelCase       :  List[Any]   				=					d_model
           _lowerCAmelCase       :  Tuple   				=					d_kv
           _lowerCAmelCase       :  str   				=					d_ff
           _lowerCAmelCase       :  Dict   				=					num_layers
           _lowerCAmelCase       :  Any   				=					(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           _lowerCAmelCase       :  Any   				=					num_heads
           _lowerCAmelCase       :  int   				=					relative_attention_num_buckets
           _lowerCAmelCase       :  List[Any]   				=					relative_attention_max_distance
           _lowerCAmelCase       :  List[str]   				=					dropout_rate
           _lowerCAmelCase       :  List[str]   				=					layer_norm_epsilon
           _lowerCAmelCase       :  Tuple   				=					initializer_factor
           _lowerCAmelCase       :  Union[str, Any]   				=					feed_forward_proj
           _lowerCAmelCase       :  Optional[Any]   				=					use_cache
  
           _lowerCAmelCase       :  Tuple   				=					self.feed_forward_proj.split("""-"""  )
           _lowerCAmelCase       :  Dict   				=					act_info[-1]
           _lowerCAmelCase       :  Dict   				=					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":
             _lowerCAmelCase       :  Optional[int]   				=					'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   a_  (_a    ):
  
  
  
  
         @property
         # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  str   				=					{
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
             _lowerCAmelCase       :  Optional[Any]   				=					'past_encoder_sequence + sequence'
             _lowerCAmelCase       :  List[str]   				=					{0: 'batch'}
             _lowerCAmelCase       :  Optional[int]   				=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
             _lowerCAmelCase       :  str   				=					{0: 'batch', 1: 'decoder_sequence'}
             _lowerCAmelCase       :  int   				=					{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 1_3
  
  
  
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           return 5E-4
  
  
  
style_context: 
  '''simple docstring'''
  
  import random
  
  
  class   a_  :
  
  
  
  
         def __init__(     self   ,   snake_case_   ,   snake_case_  ):
           _lowerCAmelCase       :  List[str]   				=					x
           _lowerCAmelCase       :  List[str]   				=					y
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           return (self.x**2 + self.y**2) <= 1
  
  
  
  
  
  
  
         @classmethod
         def 							__UpperCamelCase       (     cls  ):
           return cls(x=random.random()   ,   y=random.random()  )
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int		)    ->						float:
    if number_of_simulations < 1:
      raise ValueError("""At least one simulation is necessary to estimate PI."""		)
  
    _lowerCAmelCase       :  Tuple   				=					0
    for _ in range(_lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					Point.random_unit_square()
  
      if random_point.is_in_unit_circle():
        number_in_unit_circle += 1
  
    return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
       # import doctest
  
       # doctest.testmod()
       from math import pi
  
       UpperCamelCase_     			=       """Please enter the desired number of Monte Carlo simulations: """
       UpperCamelCase_     			=       estimate_pi(int(input(prompt).strip()))
       print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')
  
  
  