






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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:List[Any]   =       DiTPipeline
	__snake_case	:Any   =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	__snake_case	:Tuple   =       PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	__snake_case	:str   =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	__snake_case	:List[Any]   =       False







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		torch.manual_seed(0			)
		__lowercase             =  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=1000       ,				norm_type="""ada_norm_zero"""       ,				norm_elementwise_affine=_lowerCAmelCase       ,				)
		__lowercase             =  AutoencoderKL()
		__lowercase             =  DDIMScheduler()
		__lowercase             =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
		return components







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Tuple=0			)		-> Optional[Any]:
		"""simple docstring"""







		if str(_lowerCAmelCase			).startswith("""mps"""			):
			__lowercase             =  torch.manual_seed(_lowerCAmelCase			)
		else:
			__lowercase             =  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
		__lowercase             =  {
		    """class_labels""": [1],
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """output_type""": """numpy""",
		}
		return inputs







	def  _a   (  self      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  """cpu"""

		__lowercase             =  self.get_dummy_components()
		__lowercase             =  self.pipeline_class(**_lowerCAmelCase			)
		pipe.to(_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		self.assertEqual(image.shape       ,				(1, 16, 16, 3)			)
		__lowercase             =  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]			)
		__lowercase             =  np.abs(image_slice.flatten() - expected_slice			).max()
		self.assertLessEqual(_lowerCAmelCase       ,				1e-3			)







	def  _a   (  self      :							List[str]			)		-> str:
		"""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  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)


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







	def  _a   (  self      :							int			)		-> int:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  torch.manual_seed(0			)

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

		__lowercase             =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
		__lowercase             =  pipe.get_label_ids(_lowerCAmelCase			)

		__lowercase             =  pipe(_lowerCAmelCase       ,				generator=_lowerCAmelCase       ,				num_inference_steps=40       ,				output_type="""np"""			).images

		for word, image in zip(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  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  _a   (  self      :							List[str]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""			)
		__lowercase             =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
		pipe.to("""cuda"""			)

		__lowercase             =  ["""vase""", """umbrella"""]
		__lowercase             =  pipe.get_label_ids(_lowerCAmelCase			)

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

		for word, image in zip(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  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










from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if not nums:
		return 0
	__lowercase             =  nums[0]
	__lowercase             =  0
	for num in nums[1:]:
		__lowercase       ,						__lowercase             =  (
		    max_excluding + num,
		    max(lowerCamelCase				,  lowerCamelCase					),
		)
	return max(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

