
'''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       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





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


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							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(__magic_name__							) != len(__magic_name__							):
							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
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

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

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

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

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

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

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

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

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

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

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

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if self.config.center_input_sample:
							lowerCAmelCase__									=							2 * sample - 1.0

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

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

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# 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.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				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":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

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

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

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

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

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

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)







'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 				A       :





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for img in imgs:
							lowerCAmelCase__		,lowerCAmelCase__									=							img.shape[:2]
							# later: provide list and randomly choose index for resize
							lowerCAmelCase__									=							np.random.randint(self.short_edge_length[0] ,							self.short_edge_length[1] + 1							)
							if size == 0:
										return img
							lowerCAmelCase__									=							size * 1.0 / min(__magic_name__ ,							__magic_name__							)
							if h < w:
										lowerCAmelCase__		,lowerCAmelCase__									=							size, scale * w
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							scale * h, size
							if max(__magic_name__ ,							__magic_name__							) > self.max_size:
										lowerCAmelCase__									=							self.max_size * 1.0 / max(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							newh * scale
										lowerCAmelCase__									=							neww * scale
							lowerCAmelCase__									=							int(neww + 0.5							)
							lowerCAmelCase__									=							int(newh + 0.5							)

							if img.dtype == np.uinta:
										lowerCAmelCase__									=							Image.fromarray(__magic_name__							)
										lowerCAmelCase__									=							pil_image.resize((neww, newh) ,							PILImageResampling.BILINEAR							)
										lowerCAmelCase__									=							np.asarray(__magic_name__							)
							else:
										lowerCAmelCase__									=							img.permute(2 ,							0 ,							1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
										lowerCAmelCase__									=							nn.functional.interpolate(
										    __magic_name__ ,							(newh, neww) ,							mode=self.interp_method ,							align_corners=__magic_name__							).squeeze(0							)
							img_augs.append(__magic_name__							)

				return img_augs






class 				A       :





	def __init__(   self     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,							cfg.INPUT.MAX_SIZE_TEST							)
				lowerCAmelCase__									=							cfg.INPUT.FORMAT
				lowerCAmelCase__									=							cfg.SIZE_DIVISIBILITY
				lowerCAmelCase__									=							cfg.PAD_VALUE
				lowerCAmelCase__									=							cfg.INPUT.MAX_SIZE_TEST
				lowerCAmelCase__									=							cfg.MODEL.DEVICE
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							lambda __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(max(__magic_name__							) for s in zip(*[img.shape for img in images]							)							)
				lowerCAmelCase__									=							[im.shape[-2:] for im in images]
				lowerCAmelCase__									=							[
				    nn.functional.pad(
				        __magic_name__ ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,							value=self.pad_value ,							)
				    for size, im in zip(__magic_name__ ,							__magic_name__							)
				]

				return torch.stack(__magic_name__							), torch.tensor(__magic_name__							)







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							lowerCAmelCase__									=							torch.true_divide(__magic_name__ ,							__magic_name__							)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



			assert torch.isfinite(UpperCamelCase_ ).all(), "Box tensor contains infinite or NaN!"
			lowerCAmelCase__		,lowerCAmelCase__									=							box_size
			tensor[:, 0].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 1].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 2].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 3].clamp_(min=0  ,		max=UpperCamelCase_ )





