



'''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


lowercase_	 =						{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 __A    (						nn.Module		):



 '''simple docstring'''







 def __init__(self				,     A )					->  str:


      """simple docstring"""



      super().__init__()
      _a											=						torchvision.models.resnetaaa(pretrained=A )
      _a											=						list(model.children() )[:-2]
      _a											=						nn.Sequential(*A )
      _a											=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )



 def       a__						(self				,     A )					->  Dict:


      """simple docstring"""



      _a											=						self.pool(self.model(A ) )
      _a											=						torch.flatten(A				,     start_dim=2 )
      _a											=						out.transpose(1				,     2 ).contiguous()
      return out  # BxNx2048






class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     A				,     A				,     A				,     A				,     A )					->  List[str]:


      """simple docstring"""



      _a											=						[json.loads(A ) for l in open(A )]
      _a											=						os.path.dirname(A )
      _a											=						tokenizer
      _a											=						labels
      _a											=						len(A )
      _a											=						max_seq_length

      _a											=						transforms



 def __len__(self )					->  Optional[int]:


      """simple docstring"""



      return len(self.data )



 def __getitem__(self				,     A )					->  Dict:


      """simple docstring"""



      _a											=						torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,     add_special_tokens=A ) )
      _a						,					_a						,					_a											=						sentence[0], sentence[1:-1], sentence[-1]
      _a											=						sentence[: self.max_seq_length]

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

      _a											=						Image.open(os.path.join(self.data_dir				,     self.data[index]['''img'''] ) ).convert('''RGB''' )
      _a											=						self.transforms(A )

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



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


      """simple docstring"""



      _a											=						Counter()
      for row in self.data:
           label_freqs.update(row['''label'''] )
      return label_freqs





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						[len(row['''sentence''']) for row in batch]
     _a						,					_a											=						len(__A), max(__A)

     _a											=						torch.zeros(__A						,			__A						,			dtype=torch.long)
     _a											=						torch.zeros(__A						,			__A						,			dtype=torch.long)

     for i_batch, (input_row, length) in enumerate(zip(__A						,			__A)):
          _a											=						input_row['''sentence''']
          _a											=						1

     _a											=						torch.stack([row['''image'''] for row in batch])
     _a											=						torch.stack([row['''label'''] for row in batch])
     _a											=						torch.stack([row['''image_start_token'''] for row in batch])
     _a											=						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 lowerCAmelCase						():



     """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 lowerCAmelCase						():



     """simple docstring"""



     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]						,			),
         ])








'''simple docstring'''





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = KandinskyVaaPriorPipeline
 __lowerCamelCase					: List[str]    = ['prompt']
 __lowerCamelCase					: str    = ['prompt', 'negative_prompt']
 __lowerCamelCase					: Union[str, Any]    = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowerCamelCase					: Union[str, Any]    = False



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



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


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
      return tokenizer



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPTextConfig(
          bos_token_id=0				,     eos_token_id=2				,     hidden_size=self.text_embedder_hidden_size				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
      return CLIPTextModelWithProjection(A )



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


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _a											=						PriorTransformer(**A )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _a											=						nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

      _a											=						CLIPVisionModelWithProjection(A )
      return model



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


      """simple docstring"""



      _a											=						CLIPImageProcessor(
          crop_size=224				,     do_center_crop=A				,     do_normalize=A				,     do_resize=A				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

      return image_processor



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


      """simple docstring"""



      _a											=						self.dummy_prior
      _a											=						self.dummy_image_encoder
      _a											=						self.dummy_text_encoder
      _a											=						self.dummy_tokenizer
      _a											=						self.dummy_image_processor

      _a											=						UnCLIPScheduler(
          variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1_000				,     clip_sample=A				,     clip_sample_range=10.0				,     )

      _a											=						{
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components



 def       a__						(self				,     A				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      if str(A ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A )
      else:
           _a											=						torch.Generator(device=A ).manual_seed(A )
      _a											=						{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



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


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A )
      _a											=						pipe.to(A )

      pipe.set_progress_bar_config(disable=A )

      _a											=						pipe(**self.get_dummy_inputs(A ) )
      _a											=						output.image_embeds

      _a											=						pipe(
          **self.get_dummy_inputs(A )				,     return_dict=A				,     )[0]

      _a											=						image[0, -10:]
      _a											=						image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _a											=						np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )

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



 @skip_mps
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						True
      _a											=						False

      self._test_inference_batch_single_identical(
          test_max_difference=A				,     relax_max_difference=A				,     test_mean_pixel_difference=A				,     )



 @skip_mps
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						False

      self._test_attention_slicing_forward_pass(
          test_max_difference=A				,     test_mean_pixel_difference=A				,     )



