






'''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_						(			__a	,       unittest.TestCase				):
  lowercase     		=      DiTPipeline
  lowercase     		=      CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  lowercase     		=      PipelineTesterMixin.required_optional_params - {
      '''latents''',
      '''num_images_per_prompt''',
      '''callback''',
      '''callback_steps''',
  }
  lowercase     		=      CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  lowercase     		=      False

  def   A__						(      self      )  ->      Dict:



        """simple docstring"""






        torch.manual_seed(0      )
        UpperCamelCase				  =					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__			,		)
        UpperCamelCase				  =					AutoencoderKL()
        UpperCamelCase				  =					DDIMScheduler()
        UpperCamelCase				  =					{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
        return components

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=0      )  ->      Any:



        """simple docstring"""






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

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



        """simple docstring"""






        UpperCamelCase				  =					"""cpu"""

        UpperCamelCase				  =					self.get_dummy_components()
        UpperCamelCase				  =					self.pipeline_class(**lowerCAmelCase__      )
        pipe.to(lowerCAmelCase__      )
        pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

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

        self.assertEqual(image.shape			,		(1, 16, 16, 3)      )
        UpperCamelCase				  =					np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]      )
        UpperCamelCase				  =					np.abs(image_slice.flatten() - expected_slice      ).max()
        self.assertLessEqual(lowerCAmelCase__			,		1e-3      )

  def   A__						(      self      )  ->      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   A__						(      self      )  ->      Any:



        """simple docstring"""






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )






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

  def   A__						(      self      )  ->      str:



        """simple docstring"""






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

  def   A__						(      self      )  ->      Tuple:



        """simple docstring"""






        UpperCamelCase				  =					torch.manual_seed(0      )

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

        UpperCamelCase				  =					["""vase""", """umbrella""", """white shark""", """white wolf"""]
        UpperCamelCase				  =					pipe.get_label_ids(lowerCAmelCase__      )

        UpperCamelCase				  =					pipe(lowerCAmelCase__			,		generator=lowerCAmelCase__			,		num_inference_steps=40			,		output_type="""np"""      ).images

        for word, image in zip(lowerCAmelCase__			,		lowerCAmelCase__      ):
              UpperCamelCase				  =					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      )  ->      Optional[int]:



        """simple docstring"""






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

        UpperCamelCase				  =					["""vase""", """umbrella"""]
        UpperCamelCase				  =					pipe.get_label_ids(lowerCAmelCase__      )

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

        for word, image in zip(lowerCAmelCase__			,		lowerCAmelCase__      ):
              UpperCamelCase				  =					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 ..utils import DummyObject, requires_backends
class 						SCREAMING_SNAKE_CASE_ (						metaclass=__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		['''keras_nlp''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""keras_nlp"""])




