





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     KandinskyInpaintPipeline
 lowercase_						=     ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_						=     [
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_						=     [
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_						=     False

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->List[Any]:



   '''simple docstring'''



   return 32

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->str:



   '''simple docstring'''



   return 32

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->Dict:



   '''simple docstring'''



   return self.time_input_dim

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->str:



   '''simple docstring'''



   return self.time_input_dim * 4

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->int:



   '''simple docstring'''



   return 100

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
   return tokenizer

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->List[str]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__: Optional[int]         =MCLIPConfig(
       numDims=self.cross_attention_dim			,					transformerDimensions=self.text_embedder_hidden_size			,					hidden_size=self.text_embedder_hidden_size			,					intermediate_size=37			,					num_attention_heads=4			,					num_hidden_layers=5			,					vocab_size=1_005			,					)

   lowerCamelCase__: Optional[int]         =MultilingualCLIP(UpperCAmelCase_)
   lowerCamelCase__: str         =text_encoder.eval()

   return text_encoder

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Union[str, Any]:



   '''simple docstring'''



   torch.manual_seed(0)

   lowerCamelCase__: Union[str, Any]         ={
       "in_channels": 9,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "text_image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "text_image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   lowerCamelCase__: Dict         =UNetaDConditionModel(**UpperCAmelCase_)
   return model

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Dict:



   '''simple docstring'''



   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->str:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__: Optional[Any]         =VQModel(**self.dummy_movq_kwargs)
   return model

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.dummy_text_encoder
   lowerCamelCase__: Dict         =self.dummy_tokenizer
   lowerCamelCase__: Tuple         =self.dummy_unet
   lowerCamelCase__: List[Any]         =self.dummy_movq

   lowerCamelCase__: Tuple         =DDIMScheduler(
       num_train_timesteps=1_000			,					beta_schedule="linear"			,					beta_start=0.0_0085			,					beta_end=0.012			,					clip_sample=UpperCAmelCase_			,					set_alpha_to_one=UpperCAmelCase_			,					steps_offset=1			,					prediction_type="epsilon"			,					thresholding=UpperCAmelCase_			,					)

   lowerCamelCase__: List[str]         ={
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  str=0)			->int:



   '''simple docstring'''



   lowerCamelCase__: Dict         =floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__: str         =floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(seed + 1)).to(UpperCAmelCase_)
   # create init_image
   lowerCamelCase__: Dict         =floats_tensor((1, 3, 64, 64)			,					rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)
   lowerCamelCase__: Dict         =image.cpu().permute(0			,					2			,					3			,					1)[0]
   lowerCamelCase__: Any         =Image.fromarray(np.uinta(UpperCAmelCase_)).convert("RGB").resize((256, 256))
   # create mask
   lowerCamelCase__: Tuple         =np.ones((64, 64)			,					dtype=np.floataa)
   lowerCamelCase__: int         =0

   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__: Union[str, Any]         =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__: Optional[Any]         =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__: Dict         ={
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Any:



   '''simple docstring'''



   lowerCamelCase__: int         ="cpu"

   lowerCamelCase__: Any         =self.get_dummy_components()

   lowerCamelCase__: Optional[Any]         =self.pipeline_class(**UpperCAmelCase_)
   lowerCamelCase__: List[str]         =pipe.to(UpperCAmelCase_)

   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__: int         =pipe(**self.get_dummy_inputs(UpperCAmelCase_))
   lowerCamelCase__: List[Any]         =output.images

   lowerCamelCase__: Optional[Any]         =pipe(
       **self.get_dummy_inputs(UpperCAmelCase_)			,					return_dict=UpperCAmelCase_			,					)[0]

   lowerCamelCase__: Tuple         =image[0, -3:, -3:, -1]
   lowerCamelCase__: Union[str, Any]         =image_from_tuple[0, -3:, -3:, -1]

   print(F"""image.shape {image.shape}""")

   assert image.shape == (1, 64, 64, 3)

   lowerCamelCase__: List[str]         =np.array(
       [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786])

   assert (
       np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
   ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->List[str]:



   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=3E-3)



@slow
@require_torch_gpu
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->List[str]:



   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy")

   lowerCamelCase__: Union[str, Any]         =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
   lowerCamelCase__: Optional[int]         =np.ones((768, 768)			,					dtype=np.floataa)
   lowerCamelCase__: Tuple         =0

   lowerCamelCase__: int         ="a hat"

   lowerCamelCase__: Optional[Any]         =KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,					torch_dtype=torch.floataa)
   pipe_prior.to(UpperCAmelCase_)

   lowerCamelCase__: Any         =KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,					torch_dtype=torch.floataa)
   lowerCamelCase__: Optional[Any]         =pipeline.to(UpperCAmelCase_)
   pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__: Any         =torch.Generator(device="cpu").manual_seed(0)
   lowerCamelCase__    ,   lowerCamelCase__: Dict         =pipe_prior(
       UpperCAmelCase_			,					generator=UpperCAmelCase_			,					num_inference_steps=5			,					negative_prompt=""			,					).to_tuple()

   lowerCamelCase__: List[str]         =pipeline(
       UpperCAmelCase_			,					image=UpperCAmelCase_			,					mask_image=UpperCAmelCase_			,					image_embeds=UpperCAmelCase_			,					negative_image_embeds=UpperCAmelCase_			,					generator=UpperCAmelCase_			,					num_inference_steps=100			,					height=768			,					width=768			,					output_type="np"			,					)

   lowerCamelCase__: str         =output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(UpperCAmelCase_			,					UpperCAmelCase_)







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   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







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






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







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

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

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

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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 				A__ ()     ->						Optional[Any]:
					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 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






