




"""simple docstring"""







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a      =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			str						,						lowerCAmelCase				:			int					):
										super().__init__()
										lowerCAmelCase  =				torchvision.models.resnetaaa(pretrained=__UpperCamelCase					)
										lowerCAmelCase  =				list(model.children()					)[:-2]
										lowerCAmelCase  =				nn.Sequential(*__UpperCamelCase					)
										lowerCAmelCase  =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)




						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				self.pool(self.model(__UpperCamelCase					)					)
										lowerCAmelCase  =				torch.flatten(__UpperCamelCase						,						start_dim=2					)
										lowerCAmelCase  =				out.transpose(1						,						2					).contiguous()
										return out  # BxNx2048






class       SCREAMING_SNAKE_CASE__					(					__snake_case					):


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Dict					):
										lowerCAmelCase  =				[json.loads(__UpperCamelCase					) for l in open(__UpperCamelCase					)]
										lowerCAmelCase  =				os.path.dirname(__UpperCamelCase					)
										lowerCAmelCase  =				tokenizer
										lowerCAmelCase  =				labels
										lowerCAmelCase  =				len(__UpperCamelCase					)
										lowerCAmelCase  =				max_seq_length

										lowerCAmelCase  =				transforms


						def __len__(							self				:			str					):
										return len(self.data					)


						def __getitem__(							self				:			Optional[int]						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						,						add_special_tokens=__UpperCamelCase					)					)
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				sentence[0], sentence[1:-1], sentence[-1]
										lowerCAmelCase  =				sentence[: self.max_seq_length]

										lowerCAmelCase  =				torch.zeros(self.n_classes					)
										lowerCAmelCase  =				1

										lowerCAmelCase  =				Image.open(os.path.join(self.data_dir						,						self.data[index]["""img"""]					)					).convert("""RGB"""					)
										lowerCAmelCase  =				self.transforms(__UpperCamelCase					)

										return {
										    "image_start_token": start_token,
										    "image_end_token": end_token,
										    "sentence": sentence,
										    "image": image,
										    "label": label,
										}




						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				Counter()
										for row in self.data:
														label_freqs.update(row["""label"""]					)
										return label_freqs





def 							lowercase					(snake_case__   : Tuple )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[len(row["""sentence"""] ) for row in batch]
				lowerCAmelCase ,						lowerCAmelCase  =				len(lowercase__ ), max(lowercase__ )

				lowerCAmelCase  =				torch.zeros(lowercase__      ,       lowercase__      ,       dtype=torch.long )
				lowerCAmelCase  =				torch.zeros(lowercase__      ,       lowercase__      ,       dtype=torch.long )

				for i_batch, (input_row, length) in enumerate(zip(lowercase__      ,       lowercase__ ) ):
								lowerCAmelCase  =				input_row["""sentence"""]
								lowerCAmelCase  =				1

				lowerCAmelCase  =				torch.stack([row["""image"""] for row in batch] )
				lowerCAmelCase  =				torch.stack([row["""label"""] for row in batch] )
				lowerCAmelCase  =				torch.stack([row["""image_start_token"""] for row in batch] )
				lowerCAmelCase  =				torch.stack([row["""image_end_token"""] for row in batch] )

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 							lowercase					()     ->  str:


				'''simple docstring'''






				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]






def 							lowercase					()     ->  str:


				'''simple docstring'''






				return transforms.Compose(
				    [
				        transforms.Resize(256 ),
				        transforms.CenterCrop(224 ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,       std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,       ),
				    ] )











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





