




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 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      KandinskyVaaPriorPipeline
    UpperCAmelCase__							  =      ['''prompt''']
    UpperCAmelCase__							  =      ['''prompt''', '''negative_prompt''']
    UpperCAmelCase__							  =      [
        '''num_images_per_prompt''',
        '''generator''',
        '''num_inference_steps''',
        '''latents''',
        '''negative_prompt''',
        '''guidance_scale''',
        '''output_type''',
        '''return_dict''',
    ]
    UpperCAmelCase__							  =      False


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Optional[int]:





           '''simple docstring'''

           return 32


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           return 32


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





           '''simple docstring'''

           return self.time_input_dim


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           return self.time_input_dim * 4


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[int]:





           '''simple docstring'''

           return 100


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Union[str, Any]:





           '''simple docstring'''

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


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





           '''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-05					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1_000					, )
           return CLIPTextModelWithProjection(UpperCAmelCase__)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[Any]:





           '''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(**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
           A__       =    nn.Parameter(torch.ones(model.clip_std.shape))
           return model


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





           '''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(UpperCAmelCase__)
           return model


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Tuple:





           '''simple docstring'''

           A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Tuple:





           '''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=UpperCAmelCase__					, 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        SCREAMING_SNAKE_CASE							(			self      :	str					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=0)						->str:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           A__       =    '''cpu'''

           A__       =    self.get_dummy_components()

           A__       =    self.pipeline_class(**UpperCAmelCase__)
           A__       =    pipe.to(UpperCAmelCase__)

           pipe.set_progress_bar_config(disable=UpperCAmelCase__)

           A__       =    pipe(**self.get_dummy_inputs(UpperCAmelCase__))
           A__       =    output.image_embeds

           A__       =    pipe(
               **self.get_dummy_inputs(UpperCAmelCase__)					, return_dict=UpperCAmelCase__					, )[0]

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

           assert image.shape == (1, 32)

           A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           A__       =    torch_device == '''cpu'''
           A__       =    True
           A__       =    False

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





    @skip_mps
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    torch_device == '''cpu'''
           A__       =    False

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








from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


