




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_      						=   logging.get_logger(__name__)

a_      						=   {
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    "gpt2"
      lowerCAmelCase__	:       List[str]			=    ["past_key_values"]
      lowerCAmelCase__	:       str			=    {
          "hidden_size": "n_embd",
          "max_position_embeddings": "n_positions",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       Tuple=50257       ,				snake_case    :       List[Any]=1024       ,				snake_case    :       List[Any]=768       ,				snake_case    :       int=12       ,				snake_case    :       List[Any]=12       ,				snake_case    :       List[Any]=None       ,				snake_case    :       Optional[int]="gelu_new"       ,				snake_case    :       Union[str, Any]=0.1       ,				snake_case    :       Any=0.1       ,				snake_case    :       Any=0.1       ,				snake_case    :       Optional[int]=1E-5       ,				snake_case    :       Union[str, Any]=0.02       ,				snake_case    :       str="cls_index"       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       List[Any]=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       List[str]=0.1       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       Dict=True       ,				snake_case    :       List[Any]=50256       ,				snake_case    :       Tuple=50256       ,				snake_case    :       Union[str, Any]=False       ,				snake_case    :       Union[str, Any]=False       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  n_positions
          __UpperCamelCase     						=  n_embd
          __UpperCamelCase     						=  n_layer
          __UpperCamelCase     						=  n_head
          __UpperCamelCase     						=  n_inner
          __UpperCamelCase     						=  activation_function
          __UpperCamelCase     						=  resid_pdrop
          __UpperCamelCase     						=  embd_pdrop
          __UpperCamelCase     						=  attn_pdrop
          __UpperCamelCase     						=  layer_norm_epsilon
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  summary_type
          __UpperCamelCase     						=  summary_use_proj
          __UpperCamelCase     						=  summary_activation
          __UpperCamelCase     						=  summary_first_dropout
          __UpperCamelCase     						=  summary_proj_to_labels
          __UpperCamelCase     						=  scale_attn_weights
          __UpperCamelCase     						=  use_cache
          __UpperCamelCase     						=  scale_attn_by_inverse_layer_idx
          __UpperCamelCase     						=  reorder_and_upcast_attn

          __UpperCamelCase     						=  bos_token_id
          __UpperCamelCase     						=  eos_token_id

          super().__init__(bos_token_id=snake_case       ,				eos_token_id=snake_case       ,				**snake_case )







class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       str       ,				snake_case    :       PretrainedConfig       ,				snake_case    :       str = "default"       ,				snake_case    :       List[PatchingSpec] = None       ,				snake_case    :       bool = False       ,				):
          super().__init__(snake_case       ,				task=snake_case       ,				patching_specs=snake_case       ,				use_past=snake_case )
          if not getattr(self._config       ,				'''pad_token_id'''       ,				snake_case ):
              # TODO: how to do that better?
              __UpperCamelCase     						=  0







      @property
      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
          if self.use_past:
              self.fill_with_past_key_values_(snake_case       ,				direction='''inputs''' )
              __UpperCamelCase     						=  {0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
              __UpperCamelCase     						=  {0: '''batch''', 1: '''sequence'''}

          return common_inputs







      @property
      def 					snake_case		(   self    :       str ):
          return self._config.n_layer







      @property
      def 					snake_case		(   self    :       Tuple ):
          return self._config.n_head







      def 					snake_case		(   self    :       List[str]       ,				snake_case    :       PreTrainedTokenizer       ,				snake_case    :       int = -1       ,				snake_case    :       int = -1       ,				snake_case    :       bool = False       ,				snake_case    :       Optional[TensorType] = None       ,				):
          __UpperCamelCase     						=  super(snake_case       ,				self ).generate_dummy_inputs(
              snake_case       ,				batch_size=snake_case       ,				seq_length=snake_case       ,				is_pair=snake_case       ,				framework=snake_case )

          # We need to order the input in the way they appears in the forward()
          __UpperCamelCase     						=  OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )

          # Need to add the past_keys
          if self.use_past:
              if not is_torch_available():
                  raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
              else:
                  import torch

                  __UpperCamelCase						,					__UpperCamelCase     						=  common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  __UpperCamelCase     						=  seqlen + 2
                  __UpperCamelCase     						=  (
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  __UpperCamelCase     						=  [
                      (torch.zeros(snake_case ), torch.zeros(snake_case )) for _ in range(self.num_layers )
                  ]

          __UpperCamelCase     						=  common_inputs['''attention_mask''']
          if self.use_past:
              __UpperCamelCase     						=  ordered_inputs['''attention_mask'''].dtype
              __UpperCamelCase     						=  torch.cat(
                  [ordered_inputs['''attention_mask'''], torch.ones(snake_case       ,				snake_case       ,				dtype=snake_case )]       ,				dim=1 )

          return ordered_inputs







      @property
      def 					snake_case		(   self    :       Optional[int] ):
          return 13












import random

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       float       ,				snake_case    :       float ):
          __UpperCamelCase     						=  x
          __UpperCamelCase     						=  y







      def 					snake_case		(   self    :       List[Any] ):
          return (self.x**2 + self.y**2) <= 1







      @classmethod
      def 					snake_case		(   cls    :       List[str] ):
          return cls(x=random.random()       ,				y=random.random() )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		float:

    """simple docstring"""


    if number_of_simulations < 1:
        raise ValueError('''At least one simulation is necessary to estimate PI.'''					)

    __UpperCamelCase     						=  0
    for _ in range(lowercase_					):
        __UpperCamelCase     						=  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

 a_      						=   "Please enter the desired number of Monte Carlo simulations: "
 a_      						=   estimate_pi(int(input(prompt).strip()))
 print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")






