


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(						__a, unittest.TestCase   ):
       lowercase_ :	str     =			DiTPipeline
       lowercase_ :	Tuple     =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       lowercase_ :	Tuple     =			PipelineTesterMixin.required_optional_params - {
           """latents""",
           """num_images_per_prompt""",
           """callback""",
           """callback_steps""",
       }
       lowercase_ :	List[Any]     =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       lowercase_ :	Dict     =			False
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Tuple      		=		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=10_00,      norm_type='ada_norm_zero',      norm_elementwise_affine=a_,      )
           _lowercase       :							List[Any]      		=		AutoencoderKL()
           _lowercase       :							Any      		=		DDIMScheduler()
           _lowercase       :							Optional[Any]      		=		{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

           if str(a_).startswith('mps'):
               _lowercase       :							str      		=		torch.manual_seed(a_)
           else:
               _lowercase       :							Dict      		=		torch.Generator(device=a_).manual_seed(a_)
           _lowercase       :							Union[str, Any]      		=		{
               """class_labels""": [1],
               """generator""": generator,
               """num_inference_steps""": 2,
               """output_type""": """numpy""",
           }
           return inputs
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		"""cpu"""

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

           _lowercase       :							int      		=		self.get_dummy_inputs(a_)
           _lowercase       :							Any      		=		pipe(**a_).images
           _lowercase       :							Tuple      		=		image[0, -3:, -3:, -1]

           self.assertEqual(image.shape,      (1, 16, 16, 3))
           _lowercase       :							List[str]      		=		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])
           _lowercase       :							Optional[Any]      		=		np.abs(image_slice.flatten() - expected_slice).max()
           self.assertLessEqual(a_,      1E-3)
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           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 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)






@require_torch_gpu
@slow
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)

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

           _lowercase       :							Optional[Any]      		=		["""vase""", """umbrella""", """white shark""", """white wolf"""]
           _lowercase       :							List[str]      		=		pipe.get_label_ids(a_)

           _lowercase       :							List[str]      		=		pipe(a_,      generator=a_,      num_inference_steps=40,      output_type='np').images

           for word, image in zip(a_,      a_):
               _lowercase       :							Dict      		=		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 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512')
           _lowercase       :							List[Any]      		=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           pipe.to('cuda')

           _lowercase       :							Dict      		=		["""vase""", """umbrella"""]
           _lowercase       :							List[str]      		=		pipe.get_label_ids(a_)

           _lowercase       :							Tuple      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(a_,      generator=a_,      num_inference_steps=25,      output_type='np').images

           for word, image in zip(a_,      a_):
               _lowercase       :							int      		=		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"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


