

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 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						KandinskyVaaPriorPipeline
  _a		    =						["""prompt"""]
  _a		    =						["""prompt""", """negative_prompt"""]
  _a		    =						[
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  _a		    =						False




  @property
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        return 32




  @property
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        return 32




  @property
  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        return self.time_input_dim




  @property
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        return self.time_input_dim * 4




  @property
  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        return 100




  @property
  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






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




  @property
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        torch.manual_seed(0      )
        _lowercase			 =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(lowerCAmelCase      )




  @property
  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        torch.manual_seed(0      )

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

        _lowercase			 =PriorTransformer(**lowerCAmelCase      )
        # 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
        _lowercase			 =nn.Parameter(torch.ones(model.clip_std.shape      )      )
        return model




  @property
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        torch.manual_seed(0      )
        _lowercase			 =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							,     )

        _lowercase			 =CLIPVisionModelWithProjection(lowerCAmelCase      )
        return model




  @property
  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =CLIPImageProcessor(
            crop_size=224							,     do_center_crop=lowerCAmelCase							,     do_normalize=lowerCAmelCase							,     do_resize=lowerCAmelCase							,     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      )						->				Any:
        '''simple docstring'''






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

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

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

        return components




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=0      )						->				Union[str, Any]:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 ='cpu'

        _lowercase			 =self.get_dummy_components()

        _lowercase			 =self.pipeline_class(**lowerCAmelCase      )
        _lowercase			 =pipe.to(lowerCAmelCase      )

        pipe.set_progress_bar_config(disable=lowerCAmelCase      )

        _lowercase			 =pipe(**self.get_dummy_inputs(lowerCAmelCase      )      )
        _lowercase			 =output.image_embeds

        _lowercase			 =pipe(
            **self.get_dummy_inputs(lowerCAmelCase      )							,     return_dict=lowerCAmelCase							,     )[0]

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

        assert image.shape == (1, 32)

        _lowercase			 =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      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =torch_device == 'cpu'
        _lowercase			 =True
        _lowercase			 =False

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




  @skip_mps
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =torch_device == 'cpu'
        _lowercase			 =False

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









print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))


