


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__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  DiTPipeline
    UpperCAmelCase__						:		int      =  CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    UpperCAmelCase__						:		Any      =  PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    UpperCAmelCase__						:		int      =  CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    UpperCAmelCase__						:		List[Any]      =  False






    def _a					(			self    )  ->       Optional[Any]:
         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=A_					,   activation_fn='gelu-approximate'					,   num_embeds_ada_norm=1000					,   norm_type='ada_norm_zero'					,   norm_elementwise_affine=A_					,   )
         __UpperCamelCase    							=AutoencoderKL()
         __UpperCamelCase    							=DDIMScheduler()
         __UpperCamelCase    							={'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
         return components






    def _a					(			self					,   A_					,   A_=0    )  ->       Union[str, Any]:
         if str(A_    ).startswith('mps'    ):
              __UpperCamelCase    							=torch.manual_seed(A_    )
         else:
              __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(A_    )
         __UpperCamelCase    							={
             'class_labels': [1],
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							='cpu'

         __UpperCamelCase    							=self.get_dummy_components()
         __UpperCamelCase    							=self.pipeline_class(**A_    )
         pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )

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

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






    def _a					(			self    )  ->       Tuple:
         self._test_inference_batch_single_identical(relax_max_difference=A_					,   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    )  ->       Union[str, Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )






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



    """simple docstring"""






    def _a					(			self    )  ->       Optional[int]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def _a					(			self    )  ->       List[str]:
         __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(A_    )

         __UpperCamelCase    							=pipe(A_					,   generator=A_					,   num_inference_steps=40					,   output_type='np'    ).images

         for word, image in zip(A_					,   A_    ):
              __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    )  ->       Any:
         __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(A_    )

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

         for word, image in zip(A_					,   A_    ):
              __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









from numpy import exp, pi, sqrt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					float = 0.0			,     SCREAMING_SNAKE_CASE__			:					float = 1.0				):
     return 1 / sqrt(2 * pi * sigma**2				) * exp(-((x - mu) ** 2) / (2 * sigma**2)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





