



from typing import Mapping

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


__A       =logging.get_logger(__name__)

class 						UpperCAmelCase__     (     _snake_case  ):




			'''simple docstring'''






			UpperCamelCase     		=			"mt5"
			UpperCamelCase     		=			["past_key_values"]




			def __init__(  self				:					Optional[Any]			,			a_				:					Union[str, Any]=25_01_12			,			a_				:					Dict=5_12			,			a_				:					Dict=64			,			a_				:					List[str]=10_24			,			a_				:					Union[str, Any]=8			,			a_				:					List[str]=None			,			a_				:					Optional[Any]=6			,			a_				:					List[str]=32			,			a_				:					Tuple=1_28			,			a_				:					Optional[int]=0.1			,			a_				:					int=1e-6			,			a_				:					Dict=1.0			,			a_				:					str="gated-gelu"			,			a_				:					List[Any]=True			,			a_				:					Any=True			,			a_				:					Any="T5Tokenizer"			,			a_				:					int=False			,			a_				:					List[Any]=0			,			a_				:					List[str]=1			,			a_				:					Dict=0			,			**a_				:					Tuple			,			):
					'''simple docstring'''





					super().__init__(
					    is_encoder_decoder=_lowercase			,			tokenizer_class=_lowercase			,			tie_word_embeddings=_lowercase			,			pad_token_id=_lowercase			,			eos_token_id=_lowercase			,			decoder_start_token_id=_lowercase			,			**_lowercase			,			)
					__UpperCAmelCase       :	List[Any]						=      vocab_size
					__UpperCAmelCase       :	List[str]						=      d_model
					__UpperCAmelCase       :	Dict						=      d_kv
					__UpperCAmelCase       :	Optional[int]						=      d_ff
					__UpperCAmelCase       :	Optional[int]						=      num_layers
					__UpperCAmelCase       :	Optional[Any]						=      (
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__UpperCAmelCase       :	List[Any]						=      num_heads
					__UpperCAmelCase       :	Any						=      relative_attention_num_buckets
					__UpperCAmelCase       :	Optional[int]						=      relative_attention_max_distance
					__UpperCAmelCase       :	Union[str, Any]						=      dropout_rate
					__UpperCAmelCase       :	Tuple						=      layer_norm_epsilon
					__UpperCAmelCase       :	Dict						=      initializer_factor
					__UpperCAmelCase       :	List[Any]						=      feed_forward_proj
					__UpperCAmelCase       :	Any						=      use_cache

					__UpperCAmelCase       :	List[str]						=      self.feed_forward_proj.split('''-'''					)
					__UpperCAmelCase       :	Tuple						=      act_info[-1]
					__UpperCAmelCase       :	Optional[Any]						=      act_info[0] == '''gated'''

					if len(_lowercase					) > 1 and act_info[0] != "gated" or len(_lowercase					) > 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":
							__UpperCAmelCase       :	Tuple						=      '''gelu_new'''




			@property
			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					return self.d_model




			@property
			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					return self.num_heads




			@property
			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					return self.num_layers

class 						UpperCAmelCase__     (     _snake_case  ):




			'''simple docstring'''






			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      {
					    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
					    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
					}
					if self.use_past:
							__UpperCAmelCase       :	Optional[int]						=      '''past_encoder_sequence + sequence'''
							__UpperCAmelCase       :	str						=      {0: '''batch'''}
							__UpperCAmelCase       :	Tuple						=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
							__UpperCAmelCase       :	Optional[Any]						=      {0: '''batch''', 1: '''decoder_sequence'''}
							__UpperCAmelCase       :	List[str]						=      {0: '''batch''', 1: '''decoder_sequence'''}

					if self.use_past:
							self.fill_with_past_key_values_(_lowercase			,			direction='''inputs'''					)

					return common_inputs




			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					return 13




			@property
			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					return 5e-4










import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






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

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												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

							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)}")





