


'''simple docstring'''



from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def       _UpperCamelCase (						UpperCamelCase__   ):

			# A local function to see if a dot lands in the circle.
			def is_in_circle(UpperCamelCase__	, UpperCamelCase__   ) -> bool:
						UpperCAmelCase__					:   Union[str, Any]       				=       sqrt((x**2) + (y**2)   )
						# Our circle has a radius of 1, so a distance
						# greater than 1 would land outside the circle.
						return distance_from_centre <= 1

			# The proportion of guesses that landed in the circle
			UpperCAmelCase__					:   Union[str, Any]       				=       mean(
			    int(is_in_circle(uniform(-1.0	, 1.0   )	, uniform(-1.0	, 1.0   )   )   )
			    for _ in range(UpperCamelCase__   )   )
			# The ratio of the area for circle to square is pi/4.
			UpperCAmelCase__					:   Any       				=       proportion * 4
			print(f'''The estimated value of pi is {pi_estimate}'''   )
			print(f'''The numpy value of pi is {pi}'''   )
			print(f'''The total error is {abs(pi - pi_estimate   )}'''   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = 0.0	, UpperCamelCase__ = 1.0	, ):
			return mean(
			    function_to_integrate(uniform(UpperCamelCase__	, UpperCamelCase__   )   ) for _ in range(UpperCamelCase__   )   ) * (max_value - min_value)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = 0.0	, UpperCamelCase__ = 1.0   ):

			def identity_function(UpperCamelCase__   ) -> float:
						return x

			UpperCAmelCase__					:   Any       				=       area_under_curve_estimator(
			    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       (max_value * max_value - min_value * min_value) / 2

			print("""******************"""   )
			print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''   )
			print(f'''Estimated value is {estimated_value}'''   )
			print(f'''Expected value is {expected_value}'''   )
			print(f'''Total error is {abs(estimated_value - expected_value   )}'''   )
			print("""******************"""   )







def       _UpperCamelCase (						UpperCamelCase__   ):

			def function_to_integrate(UpperCamelCase__   ) -> float:
						return sqrt(4.0 - x * x   )

			UpperCAmelCase__					:   str       				=       area_under_curve_estimator(
			    UpperCamelCase__	, UpperCamelCase__	, 0.0	, 2.0   )

			print("""******************"""   )
			print("""Estimating pi using area_under_curve_estimator"""   )
			print(f'''Estimated value is {estimated_value}'''   )
			print(f'''Expected value is {pi}'''   )
			print(f'''Total error is {abs(estimated_value - pi   )}'''   )
			print("""******************"""   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)