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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42

class 	_a  (							lowerCamelCase_							,		lowerCamelCase_					):



	"""simple docstring"""



	@register_to_config
	def __init__(   self       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = "positional"       ,  lowerCAmelCase_ = 0       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,  lowerCAmelCase_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,  lowerCAmelCase_ = (224, 448, 672, 896)       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = "conv"       ,  lowerCAmelCase_ = "conv"       ,  lowerCAmelCase_ = "silu"       ,  lowerCAmelCase_ = 8       ,  lowerCAmelCase_ = 32       ,  lowerCAmelCase_ = 1e-5       ,  lowerCAmelCase_ = "default"       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  ):
		super().__init__()

		_lowercase   =sample_size
		_lowercase   =block_out_channels[0] * 4

		# Check inputs
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			raise ValueError(
			    F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''       )

		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			raise ValueError(
			    F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''       )

		# input
		_lowercase   =nn.Convad(lowerCAmelCase_       ,  block_out_channels[0]       ,  kernel_size=3       ,  padding=(1, 1)       )

		# time
		if time_embedding_type == "fourier":
			_lowercase   =GaussianFourierProjection(embedding_size=block_out_channels[0]       ,  scale=16       )
			_lowercase   =2 * block_out_channels[0]
		elif time_embedding_type == "positional":
			_lowercase   =Timesteps(block_out_channels[0]       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =block_out_channels[0]

		_lowercase   =TimestepEmbedding(lowerCAmelCase_       ,  lowerCAmelCase_       )

		# class embedding
		if class_embed_type is None and num_class_embeds is not None:
			_lowercase   =nn.Embedding(lowerCAmelCase_       ,  lowerCAmelCase_       )
		elif class_embed_type == "timestep":
			_lowercase   =TimestepEmbedding(lowerCAmelCase_       ,  lowerCAmelCase_       )
		elif class_embed_type == "identity":
			_lowercase   =nn.Identity(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =None

		_lowercase   =nn.ModuleList([]       )
		_lowercase   =None
		_lowercase   =nn.ModuleList([]       )

		# down
		_lowercase   =block_out_channels[0]
		for i, down_block_type in enumerate(lowerCAmelCase_       ):
			_lowercase   =output_channel
			_lowercase   =block_out_channels[i]
			_lowercase   =i == len(lowerCAmelCase_       ) - 1

			_lowercase   =get_down_block(
			    lowerCAmelCase_       ,  num_layers=lowerCAmelCase_       ,  in_channels=lowerCAmelCase_       ,  out_channels=lowerCAmelCase_       ,  temb_channels=lowerCAmelCase_       ,  add_downsample=not is_final_block       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  resnet_groups=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,  downsample_padding=lowerCAmelCase_       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  downsample_type=lowerCAmelCase_       ,  )
			self.down_blocks.append(lowerCAmelCase_       )

		# mid
		_lowercase   =UNetMidBlockaD(
		    in_channels=block_out_channels[-1]       ,  temb_channels=lowerCAmelCase_       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  output_scale_factor=lowerCAmelCase_       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,  resnet_groups=lowerCAmelCase_       ,  add_attention=lowerCAmelCase_       ,  )

		# up
		_lowercase   =list(reversed(lowerCAmelCase_       )       )
		_lowercase   =reversed_block_out_channels[0]
		for i, up_block_type in enumerate(lowerCAmelCase_       ):
			_lowercase   =output_channel
			_lowercase   =reversed_block_out_channels[i]
			_lowercase   =reversed_block_out_channels[min(i + 1       ,  len(lowerCAmelCase_       ) - 1       )]

			_lowercase   =i == len(lowerCAmelCase_       ) - 1

			_lowercase   =get_up_block(
			    lowerCAmelCase_       ,  num_layers=layers_per_block + 1       ,  in_channels=lowerCAmelCase_       ,  out_channels=lowerCAmelCase_       ,  prev_output_channel=lowerCAmelCase_       ,  temb_channels=lowerCAmelCase_       ,  add_upsample=not is_final_block       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  resnet_groups=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  upsample_type=lowerCAmelCase_       ,  )
			self.up_blocks.append(lowerCAmelCase_       )
			_lowercase   =output_channel

		# out
		_lowercase   =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,  32       )
		_lowercase   =nn.GroupNorm(num_channels=block_out_channels[0]       ,  num_groups=lowerCAmelCase_       ,  eps=lowerCAmelCase_       )
		_lowercase   =nn.SiLU()
		_lowercase   =nn.Convad(block_out_channels[0]       ,  lowerCAmelCase_       ,  kernel_size=3       ,  padding=1       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = True       ,  ):
		# 0. center input if necessary
		if self.config.center_input_sample:
			_lowercase   =2 * sample - 1.0

		# 1. time
		_lowercase   =timestep
		if not torch.is_tensor(lowerCAmelCase_       ):
			_lowercase   =torch.tensor([timesteps]       ,  dtype=torch.long       ,  device=sample.device       )
		elif torch.is_tensor(lowerCAmelCase_       ) and len(timesteps.shape       ) == 0:
			_lowercase   =timesteps[None].to(sample.device       )

		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
		_lowercase   =timesteps * torch.ones(sample.shape[0]       ,  dtype=timesteps.dtype       ,  device=timesteps.device       )

		_lowercase   =self.time_proj(lowerCAmelCase_       )

		# timesteps does not contain any weights and will always return f32 tensors
		# but time_embedding might actually be running in fp16. so we need to cast here.
		# there might be better ways to encapsulate this.
		_lowercase   =t_emb.to(dtype=self.dtype       )
		_lowercase   =self.time_embedding(lowerCAmelCase_       )

		if self.class_embedding is not None:
			if class_labels is None:
				raise ValueError("class_labels should be provided when doing class conditioning"       )

			if self.config.class_embed_type == "timestep":
				_lowercase   =self.time_proj(lowerCAmelCase_       )

			_lowercase   =self.class_embedding(lowerCAmelCase_       ).to(dtype=self.dtype       )
			_lowercase   =emb + class_emb

		# 2. pre-process
		_lowercase   =sample
		_lowercase   =self.conv_in(lowerCAmelCase_       )

		# 3. down
		_lowercase   =(sample,)
		for downsample_block in self.down_blocks:
			if hasattr(lowerCAmelCase_       ,  "skip_conv"       ):
				_lowercase   ,  _lowercase   ,  _lowercase   =downsample_block(
				    hidden_states=lowerCAmelCase_       ,  temb=lowerCAmelCase_       ,  skip_sample=lowerCAmelCase_       )
			else:
				_lowercase   ,  _lowercase   =downsample_block(hidden_states=lowerCAmelCase_       ,  temb=lowerCAmelCase_       )

			down_block_res_samples += res_samples

		# 4. mid
		_lowercase   =self.mid_block(lowerCAmelCase_       ,  lowerCAmelCase_       )

		# 5. up
		_lowercase   =None
		for upsample_block in self.up_blocks:
			_lowercase   =down_block_res_samples[-len(upsample_block.resnets       ) :]
			_lowercase   =down_block_res_samples[: -len(upsample_block.resnets       )]

			if hasattr(lowerCAmelCase_       ,  "skip_conv"       ):
				_lowercase   ,  _lowercase   =upsample_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			else:
				_lowercase   =upsample_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

        # 6. post-process
		_lowercase   =self.conv_norm_out(lowerCAmelCase_       )
		_lowercase   =self.conv_act(lowerCAmelCase_       )
		_lowercase   =self.conv_out(lowerCAmelCase_       )

		if skip_sample is not None:
			sample += skip_sample

		if self.config.time_embedding_type == "fourier":
			_lowercase   =timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]       )))       )
			_lowercase   =sample / timesteps

		if not return_dict:
			return (sample,)

		return UNetaDOutput(sample=lowerCAmelCase_       )









'''simple docstring'''






import math as m





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     int ,       snake_case_    :     Optional[Any]=0.0_0_1  )		->Tuple:






							'''simple docstring'''
							return (f(a + h  ) - f(a - h  )) / (2 * h)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[Any]=0 ,       snake_case_    :     Dict=1_00 ,       snake_case_    :     List[str]=0.0_0_0_1 ,       snake_case_    :     str=1E-6 ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Tuple				=   xa  # set the initial guess
							_lowercase							:		List[str]				=   [a]
							_lowercase							:		str				=   abs(f(snake_case_  )  )
							_lowercase							:		Dict				=   lambda snake_case_  : calc_derivative(snake_case_ ,       snake_case_ ,       h=snake_case_  )  # noqa: E731  Derivative of f(x)
							for _ in range(snake_case_  ):
														if fa(snake_case_  ) == 0:
																					raise ValueError('''No converging solution found'''  )
														_lowercase							:		Tuple				=   a - f(snake_case_  ) / fa(snake_case_  )  # Calculate the next estimate
														if logsteps:
																					steps.append(snake_case_  )
														if error < maxerror:
																					break
							else:
														raise ValueError('''Iteration limit reached, no converging solution found'''  )
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
						from matplotlib import pyplot as plt

						lowerCamelCase__	  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__  ,      lowerCamelCase__  ,      lowerCamelCase__	  =		newton_raphson(
						    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
						)
						plt.plot([abs(f(x)) for x in steps])
						plt.xlabel('step')
						plt.ylabel('error')
						plt.show()
						print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')



