




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(		UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


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

 def        a__				(       self      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       torch.manual_seed(0						)
       lowerCamelCase_	  =					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_		,							)
       lowerCamelCase_	  =					AutoencoderKL()
       lowerCamelCase_	  =					DDIMScheduler()
       lowerCamelCase_	  =					{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
       return components

 def        a__				(       self      :						Dict		,							A_      :						Optional[Any]		,							A_      :						List[str]=0						)     ->			List[str]:

       """simple docstring"""






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

 def        a__				(       self      :						List[str]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'cpu'

       lowerCamelCase_	  =					self.get_dummy_components()
       lowerCamelCase_	  =					self.pipeline_class(**UpperCamelCase_						)
       pipe.to(UpperCamelCase_						)
       pipe.set_progress_bar_config(disable=UpperCamelCase_						)

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

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

 def        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       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        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







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



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






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

 def        a__				(       self      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					torch.manual_seed(0						)

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

       lowerCamelCase_	  =					['vase', 'umbrella', 'white shark', 'white wolf']
       lowerCamelCase_	  =					pipe.get_label_ids(UpperCamelCase_						)

       lowerCamelCase_	  =					pipe(UpperCamelCase_		,							generator=UpperCamelCase_		,							num_inference_steps=40		,							output_type='np'						).images

       for word, image in zip(UpperCamelCase_		,							UpperCamelCase_						):
             lowerCamelCase_	  =					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[Any]						)     ->			int:

       """simple docstring"""






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

       lowerCamelCase_	  =					['vase', 'umbrella']
       lowerCamelCase_	  =					pipe.get_label_ids(UpperCamelCase_						)

       lowerCamelCase_	  =					torch.manual_seed(0						)
       lowerCamelCase_	  =					pipe(UpperCamelCase_		,							generator=UpperCamelCase_		,							num_inference_steps=25		,							output_type='np'						).images

       for word, image in zip(UpperCamelCase_		,							UpperCamelCase_						):
             lowerCamelCase_	  =					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    (							lowercase			: list[int]			,							lowercase			: list[int]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					len(lowercase  )
      print('The following activities are selected:'  )

      # The first activity is always selected
      lowerCamelCase_	  =					0
      print(lowercase			,							end=','  )

      # Consider rest of the activities
      for j in range(lowercase  ):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase			,							end=','  )
                  lowerCamelCase_	  =					j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase       :	Tuple									=     [1, 3, 0, 5, 8, 5]
 lowerCamelCase       :	int									=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)



