




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 							lowerCamelCase		( a__				):
   lowercase       :  List[Any]      =				4_2







class 							lowerCamelCase		( a__	,  a__				):







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

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

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

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

       UpperCamelCase				:   Optional[Any]          =							TimestepEmbedding(__A    ,      __A )

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

       UpperCamelCase				:   Dict          =							nn.ModuleList([] )
       UpperCamelCase				:   Union[str, Any]          =							None
       UpperCamelCase				:   str          =							nn.ModuleList([] )

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

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

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

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

           UpperCamelCase				:   List[Any]          =							i == len(__A ) - 1

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

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







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      ):







       if self.config.center_input_sample:
           UpperCamelCase				:   List[str]          =							2 * sample - 1.0

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

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

       UpperCamelCase				:   Tuple          =							self.time_proj(__A )

       # 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.
       UpperCamelCase				:   List[Any]          =							t_emb.to(dtype=self.dtype )
       UpperCamelCase				:   List[str]          =							self.time_embedding(__A )

       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":
               UpperCamelCase				:   Tuple          =							self.time_proj(__A )

           UpperCamelCase				:   int          =							self.class_embedding(__A ).to(dtype=self.dtype )
           UpperCamelCase				:   Optional[Any]          =							emb + class_emb

       # 2. pre-process
       UpperCamelCase				:   str          =							sample
       UpperCamelCase				:   int          =							self.conv_in(__A )

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

           down_block_res_samples += res_samples

       # 4. mid
       UpperCamelCase				:   List[str]          =							self.mid_block(__A    ,      __A )

       # 5. up
       UpperCamelCase				:   str          =							None
       for upsample_block in self.up_blocks:
           UpperCamelCase				:   List[Any]          =							down_block_res_samples[-len(upsample_block.resnets ) :]
           UpperCamelCase				:   Dict          =							down_block_res_samples[: -len(upsample_block.resnets )]

           if hasattr(__A    ,      """skip_conv""" ):
               UpperCamelCase				:   Optional[Any]          =							upsample_block(__A    ,      __A    ,      __A    ,      __A )
           else:
               UpperCamelCase				:   Tuple          =							upsample_block(__A    ,      __A    ,      __A )

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

       if skip_sample is not None:
           sample += skip_sample

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

       if not return_dict:
           return (sample,)

       return UNetaDOutput(sample=__A )










"""simple docstring"""




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

    if s < target_image_size:
        raise ValueError(f'min dim for image {s} < {target_image_size}'    )

    UpperCamelCase				:   Dict          =							target_image_size / s
    UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
    UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
    UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
    UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
    return img
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




