


from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class     UpperCamelCase(  _a   ):
					snake_case_		:				"DiagonalGaussianDistribution"






class     UpperCamelCase(  _a     ,							_a   ):
					snake_case_		:				List[str]										=	True


					@register_to_config
					def __init__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		int = 3     ,    SCREAMING_SNAKE_CASE	:		int = 3     ,    SCREAMING_SNAKE_CASE	:		Tuple[str] = ("DownEncoderBlock2D",)     ,    SCREAMING_SNAKE_CASE	:		Tuple[str] = ("UpDecoderBlock2D",)     ,    SCREAMING_SNAKE_CASE	:		Tuple[int] = (6_4,)     ,    SCREAMING_SNAKE_CASE	:		int = 1     ,    SCREAMING_SNAKE_CASE	:		str = "silu"     ,    SCREAMING_SNAKE_CASE	:		int = 4     ,    SCREAMING_SNAKE_CASE	:		int = 3_2     ,    SCREAMING_SNAKE_CASE	:		int = 3_2     ,    SCREAMING_SNAKE_CASE	:		float = 0.18215     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__()

											# pass init params to Encoder
											__snake_case				=		Encoder(
											    in_channels=SCREAMING_SNAKE_CASE     ,    out_channels=SCREAMING_SNAKE_CASE     ,    down_block_types=SCREAMING_SNAKE_CASE     ,    block_out_channels=SCREAMING_SNAKE_CASE     ,    layers_per_block=SCREAMING_SNAKE_CASE     ,    act_fn=SCREAMING_SNAKE_CASE     ,    norm_num_groups=SCREAMING_SNAKE_CASE     ,    double_z=SCREAMING_SNAKE_CASE     ,    )

											# pass init params to Decoder
											__snake_case				=		Decoder(
											    in_channels=SCREAMING_SNAKE_CASE     ,    out_channels=SCREAMING_SNAKE_CASE     ,    up_block_types=SCREAMING_SNAKE_CASE     ,    block_out_channels=SCREAMING_SNAKE_CASE     ,    layers_per_block=SCREAMING_SNAKE_CASE     ,    norm_num_groups=SCREAMING_SNAKE_CASE     ,    act_fn=SCREAMING_SNAKE_CASE     ,    )

											__snake_case				=		nn.Convad(2 * latent_channels     ,    2 * latent_channels     ,    1						)
											__snake_case				=		nn.Convad(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1						)

											__snake_case				=		False
											__snake_case				=		False

											# only relevant if vae tiling is enabled
											__snake_case				=		self.config.sample_size
											__snake_case				=		(
											    self.config.sample_size[0]
											    if isinstance(self.config.sample_size     ,    (list, tuple)						)
											    else self.config.sample_size
											)
											__snake_case				=		int(sample_size / (2 ** (len(self.config.block_out_channels						) - 1))						)
											__snake_case				=		0.25


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Optional[Any]:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    (Encoder, Decoder)						):
																	__snake_case				=		value


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	int:




											'''simple docstring'''

											__snake_case				=		use_tiling


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	int:




											'''simple docstring'''

											self.enable_tiling(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		False


					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Dict[str, AttentionProcessor]:




											'''simple docstring'''

											__snake_case				=		{}

											def fn_recursive_add_processors(SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		torch.nn.Module     ,    SCREAMING_SNAKE_CASE	:		Dict[str, AttentionProcessor]						):
																	if hasattr(SCREAMING_SNAKE_CASE     ,    "set_processor"						):
																							__snake_case				=		module.processor

																	for sub_name, child in module.named_children():
																							fn_recursive_add_processors(f'''{name}.{sub_name}'''     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																	return processors

											for name, module in self.named_children():
																	fn_recursive_add_processors(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											return processors


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]						)			->	str:




											'''simple docstring'''

											__snake_case				=		len(self.attn_processors.keys()						)

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) and len(SCREAMING_SNAKE_CASE						) != count:
																	raise ValueError(
																	    f'''A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE						)} does not match the'''
																	    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''						)

											def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		torch.nn.Module     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						):
																	if hasattr(SCREAMING_SNAKE_CASE     ,    "set_processor"						):
																							if not isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																													module.set_processor(SCREAMING_SNAKE_CASE						)
																							else:
																													module.set_processor(processor.pop(f'''{name}.processor'''						)						)

																	for sub_name, child in module.named_children():
																							fn_recursive_attn_processor(f'''{name}.{sub_name}'''     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

											for name, module in self.named_children():
																	fn_recursive_attn_processor(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Optional[int]:




											'''simple docstring'''

											self.set_attn_processor(AttnProcessor()						)


					@apply_forward_hook
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	AutoencoderKLOutput:




											'''simple docstring'''

											if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																	return self.tiled_encode(SCREAMING_SNAKE_CASE     ,    return_dict=SCREAMING_SNAKE_CASE						)

											if self.use_slicing and x.shape[0] > 1:
																	__snake_case				=		[self.encoder(SCREAMING_SNAKE_CASE						) for x_slice in x.split(1						)]
																	__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		self.encoder(SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.quant_conv(SCREAMING_SNAKE_CASE						)
											__snake_case				=		DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE						)

											if not return_dict:
																	return (posterior,)

											return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	Union[DecoderOutput, torch.FloatTensor]:




											'''simple docstring'''

											if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																	return self.tiled_decode(SCREAMING_SNAKE_CASE     ,    return_dict=SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.post_quant_conv(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.decoder(SCREAMING_SNAKE_CASE						)

											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)


					@apply_forward_hook
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	Union[DecoderOutput, torch.FloatTensor]:




											'''simple docstring'''

											if self.use_slicing and z.shape[0] > 1:
																	__snake_case				=		[self._decode(SCREAMING_SNAKE_CASE						).sample for z_slice in z.split(1						)]
																	__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		self._decode(SCREAMING_SNAKE_CASE						).sample

											if not return_dict:
																	return (decoded,)

											return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Any:




											'''simple docstring'''

											__snake_case				=		min(a.shape[2]     ,    b.shape[2]     ,    SCREAMING_SNAKE_CASE						)
											for y in range(SCREAMING_SNAKE_CASE						):
																	__snake_case				=		a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
											return b


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		min(a.shape[3]     ,    b.shape[3]     ,    SCREAMING_SNAKE_CASE						)
											for x in range(SCREAMING_SNAKE_CASE						):
																	__snake_case				=		a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
											return b


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	AutoencoderKLOutput:




											'''simple docstring'''

											__snake_case				=		int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)						)
											__snake_case				=		int(self.tile_latent_min_size * self.tile_overlap_factor						)
											__snake_case				=		self.tile_latent_min_size - blend_extent

											# Split the image into 512x512 tiles and encode them separately.
											__snake_case				=		[]
											for i in range(0     ,    x.shape[2]     ,    SCREAMING_SNAKE_CASE						):
																	__snake_case				=		[]
																	for j in range(0     ,    x.shape[3]     ,    SCREAMING_SNAKE_CASE						):
																							__snake_case				=		x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																							__snake_case				=		self.encoder(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		self.quant_conv(SCREAMING_SNAKE_CASE						)
																							row.append(SCREAMING_SNAKE_CASE						)
																	rows.append(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[]
											for i, row in enumerate(SCREAMING_SNAKE_CASE						):
																	__snake_case				=		[]
																	for j, tile in enumerate(SCREAMING_SNAKE_CASE						):
																							# blend the above tile and the left tile
																							# to the current tile and add the current tile to the result row
																							if i > 0:
																													__snake_case				=		self.blend_v(rows[i - 1][j]     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							if j > 0:
																													__snake_case				=		self.blend_h(row[j - 1]     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							result_row.append(tile[:, :, :row_limit, :row_limit]						)
																	result_rows.append(torch.cat(SCREAMING_SNAKE_CASE     ,    dim=3						)						)

											__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE     ,    dim=2						)
											__snake_case				=		DiagonalGaussianDistribution(SCREAMING_SNAKE_CASE						)

											if not return_dict:
																	return (posterior,)

											return AutoencoderKLOutput(latent_dist=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = True						)			->	Union[DecoderOutput, torch.FloatTensor]:




											'''simple docstring'''

											__snake_case				=		int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)						)
											__snake_case				=		int(self.tile_sample_min_size * self.tile_overlap_factor						)
											__snake_case				=		self.tile_sample_min_size - blend_extent

											# Split z into overlapping 64x64 tiles and decode them separately.
											# The tiles have an overlap to avoid seams between tiles.
											__snake_case				=		[]
											for i in range(0     ,    z.shape[2]     ,    SCREAMING_SNAKE_CASE						):
																	__snake_case				=		[]
																	for j in range(0     ,    z.shape[3]     ,    SCREAMING_SNAKE_CASE						):
																							__snake_case				=		z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																							__snake_case				=		self.post_quant_conv(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		self.decoder(SCREAMING_SNAKE_CASE						)
																							row.append(SCREAMING_SNAKE_CASE						)
																	rows.append(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[]
											for i, row in enumerate(SCREAMING_SNAKE_CASE						):
																	__snake_case				=		[]
																	for j, tile in enumerate(SCREAMING_SNAKE_CASE						):
																							# blend the above tile and the left tile
																							# to the current tile and add the current tile to the result row
																							if i > 0:
																													__snake_case				=		self.blend_v(rows[i - 1][j]     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							if j > 0:
																													__snake_case				=		self.blend_h(row[j - 1]     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																							result_row.append(tile[:, :, :row_limit, :row_limit]						)
																	result_rows.append(torch.cat(SCREAMING_SNAKE_CASE     ,    dim=3						)						)

											__snake_case				=		torch.cat(SCREAMING_SNAKE_CASE     ,    dim=2						)
											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		torch.FloatTensor     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		bool = True     ,    SCREAMING_SNAKE_CASE	:		Optional[torch.Generator] = None     ,    )			->	Union[DecoderOutput, torch.FloatTensor]:




											'''simple docstring'''

											__snake_case				=		sample
											__snake_case				=		self.encode(SCREAMING_SNAKE_CASE						).latent_dist
											if sample_posterior:
																	__snake_case				=		posterior.sample(generator=SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		posterior.mode()
											__snake_case				=		self.decode(SCREAMING_SNAKE_CASE						).sample

											if not return_dict:
																	return (dec,)

											return DecoderOutput(sample=SCREAMING_SNAKE_CASE						)









from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
	# Find root of polynomial
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





