





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class lowerCamelCase						(_snake_case						,      unittest.TestCase					):
  _lowercase  :	Union[str, Any]       					=				DiTPipeline
  _lowercase  :	int       					=				CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  _lowercase  :	Any       					=				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  _lowercase  :	List[str]       					=				CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  _lowercase  :	List[str]       					=				False







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	List[str]					     =     TransformeraDModel(
            sample_size=16     ,     num_layers=2     ,     patch_size=4     ,     attention_head_dim=8     ,     num_attention_heads=2     ,     in_channels=4     ,     out_channels=8     ,     attention_bias=lowerCAmelCase__     ,     activation_fn='''gelu-approximate'''     ,     num_embeds_ada_norm=1_000     ,     norm_type='''ada_norm_zero'''     ,     norm_elementwise_affine=lowerCAmelCase__     ,     )
        _snake_case		:	Optional[Any]					     =     AutoencoderKL()
        _snake_case		:	Union[str, Any]					     =     DDIMScheduler()
        _snake_case		:	Dict					     =     {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
        return components







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=0		)		->						Tuple:






        """simple docstring"""





        if str(lowerCAmelCase__		).startswith('''mps'''		):
              _snake_case		:	List[Any]					     =     torch.manual_seed(lowerCAmelCase__		)
        else:
              _snake_case		:	Dict					     =     torch.Generator(device=lowerCAmelCase__		).manual_seed(lowerCAmelCase__		)
        _snake_case		:	Optional[Any]					     =     {
            '''class_labels''': [1],
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





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

        _snake_case		:	str					     =     self.get_dummy_components()
        _snake_case		:	int					     =     self.pipeline_class(**lowerCAmelCase__		)
        pipe.to(lowerCAmelCase__		)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

        _snake_case		:	Dict					     =     self.get_dummy_inputs(lowerCAmelCase__		)
        _snake_case		:	Any					     =     pipe(**lowerCAmelCase__		).images
        _snake_case		:	Tuple					     =     image[0, -3:, -3:, -1]

        self.assertEqual(image.shape     ,     (1, 16, 16, 3)		)
        _snake_case		:	List[Any]					     =     np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]		)
        _snake_case		:	List[str]					     =     np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowerCAmelCase__     ,     1E-3		)







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






        """simple docstring"""





        self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase__     ,     expected_max_diff=1E-3		)







  @unittest.skipIf(
      torch_device != '''cuda''' or not is_xformers_available()     ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,     )
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)





@require_torch_gpu
@slow
class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







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






        """simple docstring"""





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

        _snake_case		:	Optional[int]					     =     DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''		)
        pipe.to('''cuda'''		)

        _snake_case		:	int					     =     ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
        _snake_case		:	int					     =     pipe.get_label_ids(lowerCAmelCase__		)

        _snake_case		:	List[str]					     =     pipe(lowerCAmelCase__     ,     generator=lowerCAmelCase__     ,     num_inference_steps=40     ,     output_type='''np'''		).images

        for word, image in zip(lowerCAmelCase__     ,     lowerCAmelCase__		):
              _snake_case		:	str					     =     load_numpy(
                  F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''		)
              assert np.abs((expected_image - image).max()		) < 1E-2







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






        """simple docstring"""





        _snake_case		:	List[Any]					     =     DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''		)
        _snake_case		:	List[str]					     =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
        pipe.to('''cuda'''		)

        _snake_case		:	List[Any]					     =     ['''vase''', '''umbrella''']
        _snake_case		:	List[Any]					     =     pipe.get_label_ids(lowerCAmelCase__		)

        _snake_case		:	List[str]					     =     torch.manual_seed(0		)
        _snake_case		:	Dict					     =     pipe(lowerCAmelCase__     ,     generator=lowerCAmelCase__     ,     num_inference_steps=25     ,     output_type='''np'''		).images

        for word, image in zip(lowerCAmelCase__     ,     lowerCAmelCase__		):
              _snake_case		:	List[str]					     =     load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  F'''/dit/{word}_512.npy'''		)

              assert np.abs((expected_image - image).max()		) < 1E-1









'''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)


