


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 							lowerCamelCase__						(				_SCREAMING_SNAKE_CASE       ,						unittest.TestCase   ):






			"""simple docstring"""






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




			def 					snake_case__					(				self  ):

						'''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=snake_case   ,    activation_fn="gelu-approximate"   ,    num_embeds_ada_norm=1000   ,    norm_type="ada_norm_zero"   ,    norm_elementwise_affine=snake_case   ,    )
						UpperCamelCase__       =							AutoencoderKL()
						UpperCamelCase__       =							DDIMScheduler()
						UpperCamelCase__       =							{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
						return components




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


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




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"""cpu"""

						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							self.pipeline_class(**snake_case  )
						pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )
						UpperCamelCase__       =							pipe(**snake_case  ).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(snake_case   ,    1E-3  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_inference_batch_single_identical(relax_max_difference=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 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )







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






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


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




			def 					snake_case__					(				self  ):

						'''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(snake_case  )

						UpperCamelCase__       =							pipe(snake_case   ,    generator=snake_case   ,    num_inference_steps=40   ,    output_type="np"  ).images

						for word, image in zip(snake_case   ,    snake_case  ):
									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 					snake_case__					(				self  ):

						'''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(snake_case  )

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

						for word, image in zip(snake_case   ,    snake_case  ):
									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 is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



