
'''simple docstring'''
from copy import deepcopy


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  )					->       None:

														if arr is None and size is not None:
																					_snake_case       =  size
																					_snake_case       =  [0] * size
														elif arr is not None:
																					self.init(UpperCAmelCase  )
														else:
																					raise ValueError("""Either arr or size must be specified"""  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       None:
														_snake_case       =  len(UpperCAmelCase  )
														_snake_case       =  deepcopy(UpperCAmelCase  )
														for i in range(1  ,  self.size  ):
																					_snake_case       =  self.next_(UpperCAmelCase  )
																					if j < self.size:
																												self.tree[j] += self.tree[i]

							def 	lowercase							(self  )					->       list[int]:
														_snake_case       =  self.tree[:]
														for i in range(self.size - 1  ,  0  ,  -1  ):
																					_snake_case       =  self.next_(UpperCAmelCase  )
																					if j < self.size:
																												arr[j] -= arr[i]
														return arr

							@staticmethod
							def 	lowercase							(UpperCAmelCase  )					->       int:
														return index + (index & (-index))

							@staticmethod
							def 	lowercase							(UpperCAmelCase  )					->       int:
														return index - (index & (-index))

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       None:
														if index == 0:
																					self.tree[0] += value
																					return
														while index < self.size:
																					self.tree[index] += value
																					_snake_case       =  self.next_(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       None:
														self.add(UpperCAmelCase  ,  value - self.get(UpperCAmelCase  )  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														if right == 0:
																					return 0
														_snake_case       =  self.tree[0]
														right -= 1  # make right inclusive
														while right > 0:
																					result += self.tree[right]
																					_snake_case       =  self.prev(UpperCAmelCase  )
														return result

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														return self.prefix(UpperCAmelCase  ) - self.prefix(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return self.query(UpperCAmelCase  ,  index + 1  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														value -= self.tree[0]
														if value < 0:
																					return -1

														_snake_case       =  1  # Largest power of 2 <= size
														while j * 2 < self.size:
																					j *= 2

														_snake_case       =  0

														while j > 0:
																					if i + j < self.size and self.tree[i + j] <= value:
																												value -= self.tree[i + j]
																												i += j
																					j //= 2
														return i


if __name__ == "__main__":
				import doctest

				doctest.testmod()

'''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				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  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=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  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 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  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 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  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