






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 	lowercase_      (			a_				,						unittest.TestCase     ):






      '''simple docstring'''




      __snake_case  						=						KandinskyVaaPriorEmbaEmbPipeline
      __snake_case  						=						['prompt', 'image']
      __snake_case  						=						['prompt', 'image']
      __snake_case  						=						[
          'num_images_per_prompt',
          'strength',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      __snake_case  						=						False







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->List[Any]:




       """simple docstring"""


       return 32







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




       """simple docstring"""


       return 32







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->Any:




       """simple docstring"""


       return self.time_input_dim







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




       """simple docstring"""


       return self.time_input_dim * 4







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




       """simple docstring"""


       return 100







      @property
      def    __lowerCAmelCase      (   self   :		List[Any]						)					->Optional[int]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		List[Any]						)					->List[Any]:




       """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-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1_000				,      )
       return CLIPTextModelWithProjection(_snake_case						)







      @property
      def    __lowerCAmelCase      (   self   :		Union[str, Any]						)					->Optional[int]:




       """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(**_snake_case						)
       # 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    __lowerCAmelCase      (   self   :		Optional[Any]						)					->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(_snake_case						)
       return model







      @property
      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->Union[str, Any]:




       """simple docstring"""


       a							=   CLIPImageProcessor(
           crop_size=224				,      do_center_crop=_snake_case				,      do_normalize=_snake_case				,      do_resize=_snake_case				,      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   :		Any						)					->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=_snake_case				,      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    __lowerCAmelCase      (   self   :		Optional[int]				,      __UpperCAmelCase   :		Dict				,      __UpperCAmelCase   :		List[str]=0						)					->Any:




       """simple docstring"""


       if str(_snake_case						).startswith('''mps'''						):
        a							=   torch.manual_seed(_snake_case						)
       else:
        a							=   torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

       a							=   floats_tensor((1, 3, 64, 64)				,      rng=random.Random(_snake_case						)						).to(_snake_case						)
       a							=   image.cpu().permute(0				,      2				,      3				,      1						)[0]
       a							=   Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((256, 256)						)

       a							=   {
           '''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   :		Tuple						)					->List[str]:




       """simple docstring"""


       a							=   '''cpu'''

       a							=   self.get_dummy_components()

       a							=   self.pipeline_class(**_snake_case						)
       a							=   pipe.to(_snake_case						)

       pipe.set_progress_bar_config(disable=_snake_case						)

       a							=   pipe(**self.get_dummy_inputs(_snake_case						)						)
       a							=   output.image_embeds

       a							=   pipe(
           **self.get_dummy_inputs(_snake_case						)				,      return_dict=_snake_case				,      )[0]

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

       assert image.shape == (1, 32)

       a							=   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   :		Optional[int]						)					->Any:




       """simple docstring"""


       a							=   torch_device == '''cpu'''
       a							=   True
       a							=   False

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







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




       """simple docstring"""


       a							=   torch_device == '''cpu'''
       a							=   False

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














import numpy as np

UpperCAmelCase__            =	[
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]






class 	lowercase_      :






      '''simple docstring'''




      def __init__(   self   :		Optional[Any]						)					->None:




       """simple docstring"""


       a							=   np.array(__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		str						)					->np.ndarray:




       """simple docstring"""


       a						,  a							=   np.where(letter == self.SQUARE						)
       a							=   np.concatenate([indexa + 1, indexa + 1]						)
       return indexes







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		int				,      __UpperCAmelCase   :		int						)					->str:




       """simple docstring"""


       return self.SQUARE[indexa - 1, indexa - 1]







      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.lower()
       a							=   message.replace('''j'''				,      '''i'''						)

       a							=   ''''''
       for letter_index in range(len(__UpperCAmelCase						)						):
        if message[letter_index] != " ":
         a							=   self.letter_to_numbers(message[letter_index]						)
         a							=   encoded_message + str(numbers[0]						) + str(numbers[1]						)
        elif message[letter_index] == " ":
         a							=   encoded_message + ''' '''

       return encoded_message







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.replace(''' '''				,      '''  '''						)
       a							=   ''''''
       for numbers_index in range(int(len(__UpperCAmelCase						) / 2						)						):
        if message[numbers_index * 2] != " ":
         a							=   message[numbers_index * 2]
         a							=   message[numbers_index * 2 + 1]

         a							=   self.numbers_to_letter(int(__UpperCAmelCase						)				,      int(__UpperCAmelCase						)						)
         a							=   decoded_message + letter
        elif message[numbers_index * 2] == " ":
         a							=   decoded_message + ''' '''

       return decoded_message


