




"""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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						42






class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__				):






				'''simple docstring'''





				@register_to_config
				def __init__(       self		, _a = 65_536		, _a = None		, _a = 2		, _a = 2		, _a = 0		, _a = "fourier"		, _a = True		, _a = False		, _a = 0.0		, _a = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		, _a = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		, _a = "UNetMidBlock1D"		, _a = None		, _a = (32, 32, 64)		, _a = None		, _a = 8		, _a = 1		, _a = False		, ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					sample_size

							# time
							if time_embedding_type == "fourier":
										lowerCamelCase			      =					GaussianFourierProjection(
										    embedding_size=8		, set_W_to_weight=_a		, log=_a		, flip_sin_to_cos=_a )
										lowerCamelCase			      =					2 * block_out_channels[0]
							elif time_embedding_type == "positional":
										lowerCamelCase			      =					Timesteps(
										    block_out_channels[0]		, flip_sin_to_cos=_a		, downscale_freq_shift=_a )
										lowerCamelCase			      =					block_out_channels[0]

							if use_timestep_embedding:
										lowerCamelCase			      =					block_out_channels[0] * 4
										lowerCamelCase			      =					TimestepEmbedding(
										    in_channels=_a		, time_embed_dim=_a		, act_fn=_a		, out_dim=block_out_channels[0]		, )

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

							# down
							lowerCamelCase			      =					in_channels
							for i, down_block_type in enumerate(_a ):
										lowerCamelCase			      =					output_channel
										lowerCamelCase			      =					block_out_channels[i]

										if i == 0:
													input_channel += extra_in_channels

										lowerCamelCase			      =					i == len(_a ) - 1

										lowerCamelCase			      =					get_down_block(
										    _a		, num_layers=_a		, in_channels=_a		, out_channels=_a		, temb_channels=block_out_channels[0]		, add_downsample=not is_final_block or downsample_each_block		, )
										self.down_blocks.append(_a )

							# mid
							lowerCamelCase			      =					get_mid_block(
							    _a		, in_channels=block_out_channels[-1]		, mid_channels=block_out_channels[-1]		, out_channels=block_out_channels[-1]		, embed_dim=block_out_channels[0]		, num_layers=_a		, add_downsample=_a		, )

							# up
							lowerCamelCase			      =					list(reversed(_a ) )
							lowerCamelCase			      =					reversed_block_out_channels[0]
							if out_block_type is None:
										lowerCamelCase			      =					out_channels
							else:
										lowerCamelCase			      =					block_out_channels[0]

							for i, up_block_type in enumerate(_a ):
										lowerCamelCase			      =					output_channel
										lowerCamelCase			      =					(
										    reversed_block_out_channels[i + 1] if i < len(_a ) - 1 else final_upsample_channels
										)

										lowerCamelCase			      =					i == len(_a ) - 1

										lowerCamelCase			      =					get_up_block(
										    _a		, num_layers=_a		, in_channels=_a		, out_channels=_a		, temb_channels=block_out_channels[0]		, add_upsample=not is_final_block		, )
										self.up_blocks.append(_a )
										lowerCamelCase			      =					output_channel

							# out
							lowerCamelCase			      =					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		, 32 )
							lowerCamelCase			      =					get_out_block(
							    out_block_type=_a		, num_groups_out=_a		, embed_dim=block_out_channels[0]		, out_channels=_a		, act_fn=_a		, fc_dim=block_out_channels[-1] // 4		, )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = True		, ):





							"""simple docstring"""
							lowerCamelCase			      =					timestep
							if not torch.is_tensor(_a ):
										lowerCamelCase			      =					torch.tensor([timesteps]		, dtype=torch.long		, device=sample.device )
							elif torch.is_tensor(_a ) and len(timesteps.shape ) == 0:
										lowerCamelCase			      =					timesteps[None].to(sample.device )

							lowerCamelCase			      =					self.time_proj(_a )
							if self.config.use_timestep_embedding:
										lowerCamelCase			      =					self.time_mlp(_a )
							else:
										lowerCamelCase			      =					timestep_embed[..., None]
										lowerCamelCase			      =					timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
										lowerCamelCase			      =					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

							# 2. down
							lowerCamelCase			      =					()
							for downsample_block in self.down_blocks:
										lowerCamelCase      ,   lowerCamelCase			      =					downsample_block(hidden_states=_a		, temb=_a )
										down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
										lowerCamelCase			      =					self.mid_block(_a		, _a )

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks ):
										lowerCamelCase			      =					down_block_res_samples[-1:]
										lowerCamelCase			      =					down_block_res_samples[:-1]
										lowerCamelCase			      =					upsample_block(_a		, res_hidden_states_tuple=_a		, temb=_a )

							# 5. post-process
							if self.out_block:
										lowerCamelCase			      =					self.out_block(_a		, _a )

							if not return_dict:
										return (sample,)

							return UNetaDOutput(sample=_a )









"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



