

'''simple docstring'''






import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,unittest.TestCase      ):
    _A			    =							KandinskyVaaPriorEmbaEmbPipeline
    _A			    =							["prompt", "image"]
    _A			    =							["prompt", "image"]
    _A			    =							[
        "num_images_per_prompt",
        "strength",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    _A			    =							False




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 32




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 32




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.time_input_dim




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return self.time_input_dim * 4




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 100




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
          return tokenizer




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       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=1000				,     )
          return CLIPTextModelWithProjection(lowercase__			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Any     =       {
              "num_attention_heads": 2,
              "attention_head_dim": 12,
              "embedding_dim": self.text_embedder_hidden_size,
              "num_layers": 1,
          }

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       PriorTransformer(**lowercase__			)
          # 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
          SCREAMING_SNAKE_CASE_      :			Dict     =       nn.Parameter(torch.ones(model.clip_std.shape			)			)
          return model




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          torch.manual_seed(0			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       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				,     )

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       CLIPVisionModelWithProjection(lowercase__			)
          return model




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       CLIPImageProcessor(
              crop_size=224				,     do_center_crop=lowercase__				,     do_normalize=lowercase__				,     do_resize=lowercase__				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

          return image_processor




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Dict     =       self.dummy_prior
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.dummy_image_encoder
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.dummy_tokenizer
          SCREAMING_SNAKE_CASE_      :			int     =       self.dummy_image_processor

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       UnCLIPScheduler(
              variance_type="fixed_small_log"				,     prediction_type="sample"				,     num_train_timesteps=1000				,     clip_sample=lowercase__				,     clip_sample_range=10.0				,     )

          SCREAMING_SNAKE_CASE_      :			Tuple     =       {
              "prior": prior,
              "image_encoder": image_encoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "scheduler": scheduler,
              "image_processor": image_processor,
          }

          return components




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__=0			):






          """simple docstring"""






          if str(lowercase__			).startswith("mps"			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.manual_seed(lowercase__			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.Generator(device=lowercase__			).manual_seed(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       floats_tensor((1, 3, 64, 64)				,     rng=random.Random(lowercase__			)			).to(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			str     =       image.cpu().permute(0				,     2				,     3				,     1			)[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       Image.fromarray(np.uinta(lowercase__			)			).convert("RGB"			).resize((256, 256)			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       {
              "prompt": "horse",
              "image": init_image,
              "strength": 0.5,
              "generator": generator,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       "cpu"

          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_dummy_components()

          SCREAMING_SNAKE_CASE_      :			str     =       self.pipeline_class(**lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       pipe.to(lowercase__			)

          pipe.set_progress_bar_config(disable=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       pipe(**self.get_dummy_inputs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			str     =       output.image_embeds

          SCREAMING_SNAKE_CASE_      :			Dict     =       pipe(
              **self.get_dummy_inputs(lowercase__			)				,     return_dict=lowercase__				,     )[0]

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       image[0, -10:]
          SCREAMING_SNAKE_CASE_      :			Any     =       image_from_tuple[0, -10:]

          assert image.shape == (1, 32)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       np.array(
              [
                  0.1071284,
                  1.3330271,
                  0.61260223,
                  -0.6691065,
                  -0.3846852,
                  -1.0303661,
                  0.22716111,
                  0.03348901,
                  0.30040675,
                  -0.24805029,
              ]			)

          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   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Any     =       torch_device == "cpu"
          SCREAMING_SNAKE_CASE_      :			str     =       True
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       False

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







    @skip_mps
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch_device == "cpu"
          SCREAMING_SNAKE_CASE_      :			List[str]     =       False

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








"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
