




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 	UpperCamelCase__			(A_					,       unittest.TestCase  ):

    '''simple docstring'''



    lowerCamelCase_   :			Union[str, Any]			  =   KandinskyVaaPriorEmbaEmbPipeline
    lowerCamelCase_   :			Optional[Any]			  =   ['''prompt''', '''image''']
    lowerCamelCase_   :			Any			  =   ['''prompt''', '''image''']
    lowerCamelCase_   :			Any			  =   [
        '''num_images_per_prompt''',
        '''strength''',
        '''generator''',
        '''num_inference_steps''',
        '''latents''',
        '''negative_prompt''',
        '''guidance_scale''',
        '''output_type''',
        '''return_dict''',
    ]
    lowerCamelCase_   :			List[Any]			  =   False







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return 32







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return 32







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







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







    @property
    def 					_lowercase  (					self  )  ->   Optional[int]:
           return 100







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   Dict    				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
           return tokenizer







    @property
    def 					_lowercase  (					self  )  ->   List[str]:
           torch.manual_seed(0  )
           lowerCamelCase     :   List[str]    				=      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(UpperCamelCase__  )







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           torch.manual_seed(0  )

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

           lowerCamelCase     :   Any    				=      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
           lowerCamelCase     :   Optional[int]    				=      nn.Parameter(torch.ones(model.clip_std.shape  )  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   Union[str, Any]:
           torch.manual_seed(0  )
           lowerCamelCase     :   Dict    				=      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  ,						)

           lowerCamelCase     :   str    				=      CLIPVisionModelWithProjection(UpperCamelCase__  )
           return model







    @property
    def 					_lowercase  (					self  )  ->   List[str]:
           lowerCamelCase     :   Tuple    				=      CLIPImageProcessor(
               crop_size=224  ,						do_center_crop=UpperCamelCase__  ,						do_normalize=UpperCamelCase__  ,						do_resize=UpperCamelCase__  ,						image_mean=[0.48145466, 0.4578275, 0.40821073]  ,						image_std=[0.26862954, 0.26130258, 0.27577711]  ,						resample=3  ,						size=224  ,						)

           return image_processor







    def 					_lowercase  (					self  )  ->   str:
           lowerCamelCase     :   Tuple    				=      self.dummy_prior
           lowerCamelCase     :   str    				=      self.dummy_image_encoder
           lowerCamelCase     :   List[str]    				=      self.dummy_text_encoder
           lowerCamelCase     :   List[str]    				=      self.dummy_tokenizer
           lowerCamelCase     :   Tuple    				=      self.dummy_image_processor

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

           lowerCamelCase     :   str    				=      {
               "prior": prior,
               "image_encoder": image_encoder,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "scheduler": scheduler,
               "image_processor": image_processor,
           }

           return components







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=0  )  ->   Any:
           if str(UpperCamelCase__  ).startswith("mps"  ):
                  lowerCamelCase     :   Any    				=      torch.manual_seed(UpperCamelCase__  )
           else:
                  lowerCamelCase     :   int    				=      torch.Generator(device=UpperCamelCase__  ).manual_seed(UpperCamelCase__  )

           lowerCamelCase     :   int    				=      floats_tensor((1, 3, 64, 64)  ,						rng=random.Random(UpperCamelCase__  )  ).to(UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      image.cpu().permute(0  ,						2  ,						3  ,						1  )[0]
           lowerCamelCase     :   int    				=      Image.fromarray(np.uinta(UpperCamelCase__  )  ).convert("RGB"  ).resize((256, 256)  )

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







    def 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   Optional[int]    				=      "cpu"

           lowerCamelCase     :   int    				=      self.get_dummy_components()

           lowerCamelCase     :   str    				=      self.pipeline_class(**UpperCamelCase__  )
           lowerCamelCase     :   List[str]    				=      pipe.to(UpperCamelCase__  )

           pipe.set_progress_bar_config(disable=UpperCamelCase__  )

           lowerCamelCase     :   int    				=      pipe(**self.get_dummy_inputs(UpperCamelCase__  )  )
           lowerCamelCase     :   Union[str, Any]    				=      output.image_embeds

           lowerCamelCase     :   Tuple    				=      pipe(
               **self.get_dummy_inputs(UpperCamelCase__  )  ,						return_dict=UpperCamelCase__  ,						)[0]

           lowerCamelCase     :   Dict    				=      image[0, -10:]
           lowerCamelCase     :   List[str]    				=      image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           lowerCamelCase     :   List[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 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   List[str]    				=      torch_device == "cpu"
           lowerCamelCase     :   Optional[int]    				=      True
           lowerCamelCase     :   Union[str, Any]    				=      False

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







    @skip_mps
    def 					_lowercase  (					self  )  ->   List[str]:
           lowerCamelCase     :   List[Any]    				=      torch_device == "cpu"
           lowerCamelCase     :   str    				=      False

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








from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				list:
       lowerCamelCase     :   int    				=      [None] * (len(_SCREAMING_SNAKE_CASE					) + len(_SCREAMING_SNAKE_CASE					))

       lowerCamelCase     :   Any    				=      0  # pointer to current index for left Half
       lowerCamelCase     :   int    				=      0  # pointer to current index for the right Half
       lowerCamelCase     :   Optional[int]    				=      0  # pointer to current index for the sorted array Half

       while pointera < len(_SCREAMING_SNAKE_CASE					) and pointera < len(_SCREAMING_SNAKE_CASE					):
              if left_half[pointera] < right_half[pointera]:
                     lowerCamelCase     :   Any    				=      left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     lowerCamelCase     :   Any    				=      right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Optional[Any]    				=      left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   str    				=      right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list:
       if len(_SCREAMING_SNAKE_CASE					) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       lowerCamelCase     :   int    				=      0 + (len(_SCREAMING_SNAKE_CASE					) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       lowerCamelCase     :   Optional[Any]    				=      array[:middle]
       lowerCamelCase     :   Optional[Any]    				=      array[middle:]

       return merge(merge_sort(_SCREAMING_SNAKE_CASE					)				,merge_sort(_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






