
"""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  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Union[str, Any]				   =	DiTPipeline
		A_						:       Union[str, Any]				   =	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		A_						:       List[Any]				   =	PipelineTesterMixin.required_optional_params - {
		    'latents',
		    'num_images_per_prompt',
		    'callback',
		    'callback_steps',
		}
		A_						:       Optional[Any]				   =	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		A_						:       Tuple				   =	False

		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						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=_SCREAMING_SNAKE_CASE       ,		activation_fn='gelu-approximate'       ,		num_embeds_ada_norm=10_00       ,		norm_type='ada_norm_zero'       ,		norm_elementwise_affine=_SCREAMING_SNAKE_CASE       ,		)
			__lowerCAmelCase			:						str									=      AutoencoderKL()
			__lowerCAmelCase			:						Union[str, Any]									=      DDIMScheduler()
			__lowerCAmelCase			:						Dict									=      {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
			return components

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0 ):
			if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
				__lowerCAmelCase			:						List[str]									=      torch.manual_seed(_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						List[str]									=      torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      {
			    'class_labels': [1],
			    'generator': generator,
			    'num_inference_steps': 2,
			    'output_type': 'numpy',
			}
			return inputs

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      'cpu'

			__lowerCAmelCase			:						Any									=      self.get_dummy_components()
			__lowerCAmelCase			:						Union[str, Any]									=      self.pipeline_class(**_SCREAMING_SNAKE_CASE )
			pipe.to(_SCREAMING_SNAKE_CASE )
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Tuple									=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      pipe(**_SCREAMING_SNAKE_CASE ).images
			__lowerCAmelCase			:						List[str]									=      image[0, -3:, -3:, -1]

			self.assertEqual(image.shape       ,		(1, 16, 16, 3) )
			__lowerCAmelCase			:						Optional[int]									=      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
			__lowerCAmelCase			:						List[Any]									=      np.abs(image_slice.flatten() - expected_slice ).max()
			self.assertLessEqual(_SCREAMING_SNAKE_CASE       ,		1E-3 )

		def 							__lowerCamelCase							(      self ):
			self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_SNAKE_CASE       ,		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 							__lowerCamelCase							(      self ):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )




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

		def 							__lowerCamelCase							(      self ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      torch.manual_seed(0 )

			__lowerCAmelCase			:						int									=      DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
			pipe.to('cuda' )

			__lowerCAmelCase			:						Optional[Any]									=      ['vase', 'umbrella', 'white shark', 'white wolf']
			__lowerCAmelCase			:						Optional[Any]									=      pipe.get_label_ids(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[Any]									=      pipe(_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=40       ,		output_type='np' ).images

			for word, image in zip(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Tuple									=      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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
			__lowerCAmelCase			:						Union[str, Any]									=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
			pipe.to('cuda' )

			__lowerCAmelCase			:						Dict									=      ['vase', 'umbrella']
			__lowerCAmelCase			:						List[str]									=      pipe.get_label_ids(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Any									=      torch.manual_seed(0 )
			__lowerCAmelCase			:						Optional[Any]									=      pipe(_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=25       ,		output_type='np' ).images

			for word, image in zip(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Dict									=      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
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					str:







  """simple docstring"""





  if a < 0 or b < 0:
    raise ValueError("""the value of both inputs must be positive"""	)

  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"
  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"

  SCREAMING_SNAKE_CASE__										=      max(len(__UpperCamelCase	) ,     len(__UpperCamelCase	)	)

  return "0b" + "".join(
      str(int(char_a != char_b	)	)
      for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase	) ,     b_binary.zfill(__UpperCamelCase	)	)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
