import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      KandinskyVaaInpaintPipeline
    __a  =      ["image_embeds", "negative_image_embeds", "image", "mask_image"]
    __a  =      [
        "image_embeds",
        "negative_image_embeds",
        "image",
        "mask_image",
    ]
    __a  =      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __a  =      False
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     return 32
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return 32
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     return self.time_input_dim
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return self.time_input_dim * 4
    @property
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     return 100
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   Dict=      {
         '''in_channels''': 9,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 8,
         '''addition_embed_type''': '''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''': '''image_proj''',
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': None,
     }

     SCREAMING_SNAKE_CASE__:   List[Any]=      UNetaDConditionModel(**lowerCAmelCase   )
     return model
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      VQModel(**self.dummy_movq_kwargs   )
     return model
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.dummy_unet
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_movq

     SCREAMING_SNAKE_CASE__:   List[Any]=      DDIMScheduler(
         num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.00085 , beta_end=0.012 , clip_sample=lowerCAmelCase , set_alpha_to_one=lowerCAmelCase , steps_offset=1 , prediction_type='''epsilon''' , thresholding=lowerCAmelCase , )

     SCREAMING_SNAKE_CASE__:   Any=      {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   str=      floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1   )   ).to(
         lowerCAmelCase   )
     # create init_image
     SCREAMING_SNAKE_CASE__:   Dict=      floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      image.cpu().permute(0 , 2 , 3 , 1   )[0]
     SCREAMING_SNAKE_CASE__:   List[str]=      Image.fromarray(np.uinta(lowerCAmelCase   )   ).convert('''RGB'''   ).resize((256, 256)   )
     # create mask
     SCREAMING_SNAKE_CASE__:   Tuple=      np.ones((64, 64) , dtype=np.floataa   )
     SCREAMING_SNAKE_CASE__:   int=      0

     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   int=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      {
         '''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 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Any=      '''cpu'''

     SCREAMING_SNAKE_CASE__:   Dict=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__:   Dict=      self.pipeline_class(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      pipe.to(lowerCAmelCase   )

     pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      pipe(**self.get_dummy_inputs(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Tuple=      output.images

     SCREAMING_SNAKE_CASE__:   List[str]=      pipe(
         **self.get_dummy_inputs(lowerCAmelCase   ) , return_dict=lowerCAmelCase , )[0]

     SCREAMING_SNAKE_CASE__:   Tuple=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__:   List[Any]=      image_from_tuple[0, -3:, -3:, -1]

     print(f'image.shape {image.shape}'   )

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

     SCREAMING_SNAKE_CASE__:   int=      np.array(
         [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]   )

     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 						UpperCamelCase_				(  self   )       ->					str:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3   )






@slow
@require_torch_gpu
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Tuple=      load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
     SCREAMING_SNAKE_CASE__:   int=      np.ones((768, 768) , dtype=np.floataa   )
     SCREAMING_SNAKE_CASE__:   Dict=      0

     SCREAMING_SNAKE_CASE__:   Optional[int]=      '''a hat'''

     SCREAMING_SNAKE_CASE__:   Dict=      KandinskyVaaPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa   )
     pipe_prior.to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      KandinskyVaaInpaintPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-2-decoder-inpaint''' , torch_dtype=torch.floataa   )
     SCREAMING_SNAKE_CASE__:   int=      pipeline.to(lowerCAmelCase   )
     pipeline.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      torch.Generator(device='''cpu'''   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      pipe_prior(
         lowerCAmelCase , generator=lowerCAmelCase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()

     SCREAMING_SNAKE_CASE__:   List[Any]=      pipeline(
         image=lowerCAmelCase , mask_image=lowerCAmelCase , image_embeds=lowerCAmelCase , negative_image_embeds=lowerCAmelCase , generator=lowerCAmelCase , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )

     SCREAMING_SNAKE_CASE__:   Tuple=      output.images[0]

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

     assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase   )


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


lowercase_     :   List[str] 				=				{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 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

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



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      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__      (	):
 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__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

