






"""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 UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class __a   (   snake_case_				):
  __lowercase    :    torch.FloatTensor






class __a   (   snake_case_ ,snake_case_				):



  @register_to_config
  def __init__(self  :		Optional[Any]				,			lowercase_  :		Optional[Union[int, Tuple[int, int]]] = None				,			lowercase_  :		int = 3				,			lowercase_  :		int = 3				,			lowercase_  :		bool = False				,			lowercase_  :		str = "positional"				,			lowercase_  :		int = 0				,			lowercase_  :		bool = True				,			lowercase_  :		Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")				,			lowercase_  :		Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")				,			lowercase_  :		Tuple[int] = (224, 448, 672, 896)				,			lowercase_  :		int = 2				,			lowercase_  :		float = 1				,			lowercase_  :		int = 1				,			lowercase_  :		str = "conv"				,			lowercase_  :		str = "conv"				,			lowercase_  :		str = "silu"				,			lowercase_  :		Optional[int] = 8				,			lowercase_  :		int = 32				,			lowercase_  :		float = 1E-5				,			lowercase_  :		str = "default"				,			lowercase_  :		bool = True				,			lowercase_  :		Optional[str] = None				,			lowercase_  :		Optional[int] = None				,			)		->							Tuple:
   super().__init__()

   __A	:       Union[str, Any]       =	sample_size
   __A	:       List[str]       =	block_out_channels[0] * 4

   # Check inputs
   if len(lowercase_   ) != len(lowercase_   ):
    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(lowercase_   ) != len(lowercase_   ):
    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
   __A	:       Optional[Any]       =	nn.Convad(lowercase_				,			block_out_channels[0]				,			kernel_size=3				,			padding=(1, 1)   )

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

   __A	:       Optional[int]       =	TimestepEmbedding(lowercase_				,			lowercase_   )

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

   __A	:       Optional[int]       =	nn.ModuleList([]   )
   __A	:       Optional[int]       =	None
   __A	:       List[Any]       =	nn.ModuleList([]   )

   # down
   __A	:       Dict       =	block_out_channels[0]
   for i, down_block_type in enumerate(lowercase_   ):
    __A	:       Optional[Any]       =	output_channel
    __A	:       Dict       =	block_out_channels[i]
    __A	:       Any       =	i == len(lowercase_   ) - 1

    __A	:       Union[str, Any]       =	get_down_block(
        lowercase_				,			num_layers=lowercase_				,			in_channels=lowercase_				,			out_channels=lowercase_				,			temb_channels=lowercase_				,			add_downsample=not is_final_block				,			resnet_eps=lowercase_				,			resnet_act_fn=lowercase_				,			resnet_groups=lowercase_				,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,			downsample_padding=lowercase_				,			resnet_time_scale_shift=lowercase_				,			downsample_type=lowercase_				,			)
    self.down_blocks.append(lowercase_   )

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

   # up
   __A	:       Any       =	list(reversed(lowercase_   )   )
   __A	:       Optional[int]       =	reversed_block_out_channels[0]
   for i, up_block_type in enumerate(lowercase_   ):
    __A	:       Union[str, Any]       =	output_channel
    __A	:       str       =	reversed_block_out_channels[i]
    __A	:       Dict       =	reversed_block_out_channels[min(i + 1				,			len(lowercase_   ) - 1   )]

    __A	:       Any       =	i == len(lowercase_   ) - 1

    __A	:       str       =	get_up_block(
        lowercase_				,			num_layers=layers_per_block + 1				,			in_channels=lowercase_				,			out_channels=lowercase_				,			prev_output_channel=lowercase_				,			temb_channels=lowercase_				,			add_upsample=not is_final_block				,			resnet_eps=lowercase_				,			resnet_act_fn=lowercase_				,			resnet_groups=lowercase_				,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,			resnet_time_scale_shift=lowercase_				,			upsample_type=lowercase_				,			)
    self.up_blocks.append(lowercase_   )
    __A	:       Union[str, Any]       =	output_channel

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



  def 						A_				(self  :		Dict				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		Union[torch.Tensor, float, int]				,			lowercase_  :		Optional[torch.Tensor] = None				,			lowercase_  :		bool = True				,			)		->							Union[UNetaDOutput, Tuple]:
   # 0. center input if necessary
   if self.config.center_input_sample:
    __A	:       str       =	2 * sample - 1.0

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

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

   __A	:       Tuple       =	self.time_proj(lowercase_   )

   # 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.
   __A	:       str       =	t_emb.to(dtype=self.dtype   )
   __A	:       Tuple       =	self.time_embedding(lowercase_   )

   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":
     __A	:       Any       =	self.time_proj(lowercase_   )

    __A	:       Optional[Any]       =	self.class_embedding(lowercase_   ).to(dtype=self.dtype   )
    __A	:       Union[str, Any]       =	emb + class_emb

   # 2. pre-process
   __A	:       str       =	sample
   __A	:       List[str]       =	self.conv_in(lowercase_   )

   # 3. down
   __A	:       Any       =	(sample,)
   for downsample_block in self.down_blocks:
    if hasattr(lowercase_				,			'skip_conv'   ):
     __A    ,					__A    ,					__A	:       str       =	downsample_block(
         hidden_states=lowercase_				,			temb=lowercase_				,			skip_sample=lowercase_   )
    else:
     __A    ,					__A	:       Tuple       =	downsample_block(hidden_states=lowercase_				,			temb=lowercase_   )

    down_block_res_samples += res_samples

   # 4. mid
   __A	:       Optional[int]       =	self.mid_block(lowercase_				,			lowercase_   )

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

    if hasattr(lowercase_				,			'skip_conv'   ):
     __A    ,					__A	:       Union[str, Any]       =	upsample_block(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )
    else:
     __A	:       Tuple       =	upsample_block(lowercase_				,			lowercase_				,			lowercase_   )

        # 6. post-process
   __A	:       Optional[int]       =	self.conv_norm_out(lowercase_   )
   __A	:       List[Any]       =	self.conv_act(lowercase_   )
   __A	:       List[Any]       =	self.conv_out(lowercase_   )

   if skip_sample is not None:
    sample += skip_sample

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

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=lowercase_   )













"""simple docstring"""


def 				a (     UpperCamelCase_							:				list     ,	UpperCamelCase_							:				int		)  ->		int:
 for index, item in enumerate(UpperCamelCase_		):
  if item == target:
   return index
 return -1




def 				a (     UpperCamelCase_							:				list     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				int		)  ->		int:
 if not (0 <= high < len(UpperCamelCase_		) and 0 <= low < len(UpperCamelCase_		)):
  raise Exception('Invalid upper or lower bound!'		)
 if high < low:
  return -1
 if sequence[low] == target:
  return low
 if sequence[high] == target:
  return high
 return rec_linear_search(UpperCamelCase_     ,	low + 1     ,	high - 1     ,	UpperCamelCase_		)


if __name__ == "__main__":
   _UpperCamelCase   :						Any     						= input("""Enter numbers separated by comma:\n""").strip()
   _UpperCamelCase   :						Optional[Any]     						= [int(item.strip()) for item in user_input.split(""",""")]

   _UpperCamelCase   :						List[str]     						= int(input("""Enter a single number to be found in the list:\n""").strip())
   _UpperCamelCase   :						List[Any]     						= linear_search(sequence, target)
   if result != -1:
      print(f'''linear_search({sequence}, {target}) = {result}''')
   else:
      print(f'''{target} was not found in {sequence}''')





