






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  snake_case__      (							_lowerCAmelCase   ):
	lowercase__		:  torch.FloatTensor





class  snake_case__      (							_lowerCAmelCase		,						_lowerCAmelCase   ):
	@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__ = (2_24, 4_48, 6_72, 8_96)    ,     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    ,     )							->   int:
							super().__init__()

							__magic_name__    :     Tuple     		=      sample_size
							__magic_name__    :     List[str]     		=      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
							__magic_name__    :     Tuple     		=      nn.Convad(lowerCAmelCase__    ,     block_out_channels[0]    ,     kernel_size=3    ,     padding=(1, 1)				)

							# time
							if time_embedding_type == "fourier":
													__magic_name__    :     Any     		=      GaussianFourierProjection(embedding_size=block_out_channels[0]    ,     scale=16				)
													__magic_name__    :     List[Any]     		=      2 * block_out_channels[0]
							elif time_embedding_type == "positional":
													__magic_name__    :     Tuple     		=      Timesteps(block_out_channels[0]    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
													__magic_name__    :     Optional[Any]     		=      block_out_channels[0]

							__magic_name__    :     Union[str, Any]     		=      TimestepEmbedding(lowerCAmelCase__    ,     lowerCAmelCase__				)

							# class embedding
							if class_embed_type is None and num_class_embeds is not None:
													__magic_name__    :     List[Any]     		=      nn.Embedding(lowerCAmelCase__    ,     lowerCAmelCase__				)
							elif class_embed_type == "timestep":
													__magic_name__    :     str     		=      TimestepEmbedding(lowerCAmelCase__    ,     lowerCAmelCase__				)
							elif class_embed_type == "identity":
													__magic_name__    :     Optional[int]     		=      nn.Identity(lowerCAmelCase__    ,     lowerCAmelCase__				)
							else:
													__magic_name__    :     Tuple     		=      None

							__magic_name__    :     Union[str, Any]     		=      nn.ModuleList([]				)
							__magic_name__    :     int     		=      None
							__magic_name__    :     List[Any]     		=      nn.ModuleList([]				)

							# down
							__magic_name__    :     Optional[Any]     		=      block_out_channels[0]
							for i, down_block_type in enumerate(lowerCAmelCase__				):
													__magic_name__    :     Any     		=      output_channel
													__magic_name__    :     Dict     		=      block_out_channels[i]
													__magic_name__    :     List[str]     		=      i == len(lowerCAmelCase__				) - 1

													__magic_name__    :     Union[str, Any]     		=      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
							__magic_name__    :     List[Any]     		=      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
							__magic_name__    :     Union[str, Any]     		=      list(reversed(lowerCAmelCase__				)				)
							__magic_name__    :     Optional[Any]     		=      reversed_block_out_channels[0]
							for i, up_block_type in enumerate(lowerCAmelCase__				):
													__magic_name__    :     Optional[Any]     		=      output_channel
													__magic_name__    :     Dict     		=      reversed_block_out_channels[i]
													__magic_name__    :     List[Any]     		=      reversed_block_out_channels[min(i + 1    ,     len(lowerCAmelCase__				) - 1				)]

													__magic_name__    :     Tuple     		=      i == len(lowerCAmelCase__				) - 1

													__magic_name__    :     str     		=      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__				)
													__magic_name__    :     Tuple     		=      output_channel

							# out
							__magic_name__    :     Optional[int]     		=      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,     32				)
							__magic_name__    :     List[Any]     		=      nn.GroupNorm(num_channels=block_out_channels[0]    ,     num_groups=lowerCAmelCase__    ,     eps=lowerCAmelCase__				)
							__magic_name__    :     Tuple     		=      nn.SiLU()
							__magic_name__    :     Dict     		=      nn.Convad(block_out_channels[0]    ,     lowerCAmelCase__    ,     kernel_size=3    ,     padding=1				)







	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__ = None    ,     lowerCAmelCase__ = True    ,     )							->   Union[UNetaDOutput, Tuple]:
							# 0. center input if necessary
							if self.config.center_input_sample:
													__magic_name__    :     Union[str, Any]     		=      2 * sample - 1.0

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

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

							__magic_name__    :     Tuple     		=      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.
							__magic_name__    :     str     		=      t_emb.to(dtype=self.dtype				)
							__magic_name__    :     List[Any]     		=      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":
																			__magic_name__    :     Union[str, Any]     		=      self.time_proj(lowerCAmelCase__				)

													__magic_name__    :     Tuple     		=      self.class_embedding(lowerCAmelCase__				).to(dtype=self.dtype				)
													__magic_name__    :     Union[str, Any]     		=      emb + class_emb

							# 2. pre-process
							__magic_name__    :     Union[str, Any]     		=      sample
							__magic_name__    :     Optional[Any]     		=      self.conv_in(lowerCAmelCase__				)

							# 3. down
							__magic_name__    :     Any     		=      (sample,)
							for downsample_block in self.down_blocks:
													if hasattr(lowerCAmelCase__    ,     """skip_conv"""				):
																			__magic_name__		,__magic_name__		,__magic_name__    :     Optional[int]     		=      downsample_block(
																			    hidden_states=lowerCAmelCase__    ,     temb=lowerCAmelCase__    ,     skip_sample=lowerCAmelCase__				)
													else:
																			__magic_name__		,__magic_name__    :     Dict     		=      downsample_block(hidden_states=lowerCAmelCase__    ,     temb=lowerCAmelCase__				)

													down_block_res_samples += res_samples

							# 4. mid
							__magic_name__    :     Optional[int]     		=      self.mid_block(lowerCAmelCase__    ,     lowerCAmelCase__				)

							# 5. up
							__magic_name__    :     Dict     		=      None
							for upsample_block in self.up_blocks:
													__magic_name__    :     Optional[Any]     		=      down_block_res_samples[-len(upsample_block.resnets				) :]
													__magic_name__    :     Union[str, Any]     		=      down_block_res_samples[: -len(upsample_block.resnets				)]

													if hasattr(lowerCAmelCase__    ,     """skip_conv"""				):
																			__magic_name__		,__magic_name__    :     List[str]     		=      upsample_block(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
													else:
																			__magic_name__    :     Tuple     		=      upsample_block(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)

        # 6. post-process
							__magic_name__    :     Optional[Any]     		=      self.conv_norm_out(lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      self.conv_act(lowerCAmelCase__				)
							__magic_name__    :     Any     		=      self.conv_out(lowerCAmelCase__				)

							if skip_sample is not None:
													sample += skip_sample

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

							if not return_dict:
													return (sample,)

							return UNetaDOutput(sample=lowerCAmelCase__				)








class  snake_case__      :
	def __init__(       self				)							->   None:
							# numbermap keys are the number and its values are lists of indexes sorted
							# in ascending order
							__magic_name__    :     dict[int, list[int]]     		=      {}
							# indexmap keys are an index and it's values are the number at that index
							__magic_name__    :     dict[int, int]     		=      {}
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   list[int]:
							if isinstance(lowerCAmelCase__    ,     (range, str)				):
													__magic_name__    :     int     		=      list(lowerCAmelCase__				)
							elif not isinstance(lowerCAmelCase__    ,     lowerCAmelCase__				):
													raise TypeError(
													    """binary_search_delete() only accepts either a list, range or str"""				)

							__magic_name__    :     str     		=      0
							__magic_name__    :     int     		=      len(lowerCAmelCase__				) - 1

							while low <= high:
													__magic_name__    :     List[str]     		=      (low + high) // 2
													if array[mid] == item:
																			array.pop(lowerCAmelCase__				)
																			return array
													elif array[mid] < item:
																			__magic_name__    :     Tuple     		=      mid + 1
													else:
																			__magic_name__    :     Dict     		=      mid - 1
							raise ValueError(
							    """Either the item is not in the array or the array was unsorted"""				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   list[int]:
							if isinstance(lowerCAmelCase__    ,     (range, str)				):
													__magic_name__    :     Tuple     		=      list(lowerCAmelCase__				)
							elif not isinstance(lowerCAmelCase__    ,     lowerCAmelCase__				):
													raise TypeError(
													    """binary_search_insert() only accepts either a list, range or str"""				)

							__magic_name__    :     List[str]     		=      0
							__magic_name__    :     Tuple     		=      len(lowerCAmelCase__				) - 1

							while low <= high:
													__magic_name__    :     Dict     		=      (low + high) // 2
													if array[mid] == index:
																			# If the item already exists in the array,
																			# insert it after the existing item
																			array.insert(mid + 1    ,     lowerCAmelCase__				)
																			return array
													elif array[mid] < index:
																			__magic_name__    :     int     		=      mid + 1
													else:
																			__magic_name__    :     Tuple     		=      mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
							array.insert(lowerCAmelCase__    ,     lowerCAmelCase__				)
							return array
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   None:
							# Remove previous index
							if index in self.indexmap:
													__magic_name__    :     int     		=      self.indexmap[index]
													if len(self.numbermap[n]				) == 1:
																			del self.numbermap[n]
													else:
																			__magic_name__    :     Union[str, Any]     		=      self.binary_search_delete(self.numbermap[n]    ,     lowerCAmelCase__				)

        # Set new index
							__magic_name__    :     List[Any]     		=      number

							# Number not seen before or empty so insert number value
							if number not in self.numbermap:
													__magic_name__    :     Any     		=      [index]

							# Here we need to perform a binary search insertion in order to insert
							# The item in the correct place
							else:
													__magic_name__    :     Optional[Any]     		=      self.binary_search_insert(
													    self.numbermap[number]    ,     lowerCAmelCase__				)







	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   int:
							return self.numbermap.get(lowerCAmelCase__    ,     [-1]				)[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
