



'''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 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[str]     =       KandinskyVaaPriorPipeline
       lowerCAmelCase__	:Dict     =       ["prompt"]
       lowerCAmelCase__	:Dict     =       ["prompt", "negative_prompt"]
       lowerCAmelCase__	:Optional[Any]     =       [
           "num_images_per_prompt",
           "generator",
           "num_inference_steps",
           "latents",
           "negative_prompt",
           "guidance_scale",
           "output_type",
           "return_dict",
       ]
       lowerCAmelCase__	:int     =       False



       @property
       def 				_a   (					self   )	->				Any:
           return 32



       @property
       def 				_a   (					self   )	->				Any:
           return 32



       @property
       def 				_a   (					self   )	->				Any:
           return self.time_input_dim



       @property
       def 				_a   (					self   )	->				Optional[int]:
           return self.time_input_dim * 4



       @property
       def 				_a   (					self   )	->				List[str]:
           return 100



       @property
       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__									=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
           return tokenizer



       @property
       def 				_a   (					self   )	->				Union[str, Any]:
           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-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_000  ,)
           return CLIPTextModelWithProjection(UpperCAmelCase_   )



       @property
       def 				_a   (					self   )	->				Union[str, Any]:
           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(**UpperCAmelCase_   )
           # 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   )	->				Union[str, Any]:
           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(UpperCAmelCase_   )
           return model



       @property
       def 				_a   (					self   )	->				List[str]:
           lowercase__									=    CLIPImageProcessor(
               crop_size=224  ,do_center_crop=UpperCAmelCase_  ,do_normalize=UpperCAmelCase_  ,do_resize=UpperCAmelCase_  ,image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,resample=3  ,size=224  ,)

           return image_processor



       def 				_a   (					self   )	->				Any:
           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=UpperCAmelCase_  ,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  ,UpperCAmelCase_  ,UpperCAmelCase_=0   )	->				Tuple:
           if str(UpperCAmelCase_   ).startswith("mps"   ):
               lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           else:
               lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    {
               "prompt": "horse",
               "generator": generator,
               "guidance_scale": 4.0,
               "num_inference_steps": 2,
               "output_type": "np",
           }
           return inputs



       def 				_a   (					self   )	->				str:
           lowercase__									=    "cpu"

           lowercase__									=    self.get_dummy_components()

           lowercase__									=    self.pipeline_class(**UpperCAmelCase_   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )

           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    pipe(**self.get_dummy_inputs(UpperCAmelCase_   )   )
           lowercase__									=    output.image_embeds

           lowercase__									=    pipe(
               **self.get_dummy_inputs(UpperCAmelCase_   )  ,return_dict=UpperCAmelCase_  ,)[0]

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

           assert image.shape == (1, 32)

           lowercase__									=    np.array(
               [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]   )

           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   )	->				Union[str, Any]:
           lowercase__									=    torch_device == "cpu"
           lowercase__									=    True
           lowercase__									=    False

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







       @skip_mps
       def 				_a   (					self   )	->				Union[str, Any]:
           lowercase__									=    torch_device == "cpu"
           lowercase__									=    False

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









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




