





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







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        return 32







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        return 32







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






        """simple docstring"""





        return self.time_input_dim







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






        """simple docstring"""





        return self.time_input_dim * 4







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        return 100







  @property
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
        return tokenizer







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






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	Tuple					     =     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(lowercase__		)







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






        """simple docstring"""





        torch.manual_seed(0		)

        _snake_case		:	int					     =     {
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        _snake_case		:	str					     =     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
        _snake_case		:	int					     =     nn.Parameter(torch.ones(model.clip_std.shape		)		)
        return model







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






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	List[str]					     =     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     ,     )

        _snake_case		:	List[str]					     =     CLIPVisionModelWithProjection(lowercase__		)
        return model







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






        """simple docstring"""





        _snake_case		:	int					     =     CLIPImageProcessor(
            crop_size=224     ,     do_center_crop=lowercase__     ,     do_normalize=lowercase__     ,     do_resize=lowercase__     ,     image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]     ,     image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]     ,     resample=3     ,     size=224     ,     )

        return image_processor







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self.dummy_prior
        _snake_case		:	Dict					     =     self.dummy_image_encoder
        _snake_case		:	List[str]					     =     self.dummy_text_encoder
        _snake_case		:	Dict					     =     self.dummy_tokenizer
        _snake_case		:	Dict					     =     self.dummy_image_processor

        _snake_case		:	str					     =     UnCLIPScheduler(
            variance_type='''fixed_small_log'''     ,     prediction_type='''sample'''     ,     num_train_timesteps=1_000     ,     clip_sample=lowercase__     ,     clip_sample_range=10.0     ,     )

        _snake_case		:	Union[str, Any]					     =     {
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''scheduler''': scheduler,
            '''image_processor''': image_processor,
        }

        return components







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=0		)		->						List[str]:






        """simple docstring"""





        if str(lowercase__		).startswith('''mps'''		):
              _snake_case		:	List[str]					     =     torch.manual_seed(lowercase__		)
        else:
              _snake_case		:	Dict					     =     torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
        _snake_case		:	Any					     =     {
            '''prompt''': '''horse''',
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     '''cpu'''

        _snake_case		:	Dict					     =     self.get_dummy_components()

        _snake_case		:	Dict					     =     self.pipeline_class(**lowercase__		)
        _snake_case		:	Tuple					     =     pipe.to(lowercase__		)

        pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	Tuple					     =     pipe(**self.get_dummy_inputs(lowercase__		)		)
        _snake_case		:	Any					     =     output.image_embeds

        _snake_case		:	Union[str, Any]					     =     pipe(
            **self.get_dummy_inputs(lowercase__		)     ,     return_dict=lowercase__     ,     )[0]

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

        assert image.shape == (1, 32)

        _snake_case		:	List[Any]					     =     np.array(
            [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]		)

        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 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     torch_device == '''cpu'''
        _snake_case		:	List[str]					     =     True
        _snake_case		:	List[str]					     =     False

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







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






        """simple docstring"""





        _snake_case		:	int					     =     torch_device == '''cpu'''
        _snake_case		:	List[str]					     =     False

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









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _in_place_partition(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      lowerCAmelCase_			,      p - 1	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      p + 1			,      lowerCAmelCase_	)
      return count




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      _snake_case		:	Optional[int]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     a[end]
      _snake_case		:	Optional[Any]					     =     a[pivot]
      _snake_case		:	Union[str, Any]					     =     temp
      _snake_case		:	Union[str, Any]					     =     start - 1
      for index in range(lowerCAmelCase_			,      lowerCAmelCase_	):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  _snake_case		:	Optional[int]					     =     new_pivot_index + 1
                  _snake_case		:	Optional[Any]					     =     a[new_pivot_index]
                  _snake_case		:	Tuple					     =     a[index]
                  _snake_case		:	str					     =     temp

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


UpperCAmelCase					:		Dict		=							TemporaryFile()
UpperCAmelCase					:		Dict		=							1_0_0  # 1000 elements are to be sorted


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		str		=							_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)


