



from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase_									=							typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase_									=							typing.Union[np.floataa, int, float]  # noqa: UP007
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Vector     ,				_SCREAMING_SNAKE_CASE   :	Vector				)->VectorOut:
      return np.sqrt(np.sum((np.asarray(lowercase_				) - np.asarray(lowercase_				)) ** 2				)				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Vector     ,				_SCREAMING_SNAKE_CASE   :	Vector				)->VectorOut:
      return sum((va - va) ** 2 for va, va in zip(lowercase_     ,				lowercase_				)				) ** (1 / 2)


if __name__ == "__main__":
    def 							UpperCAmelCase__		(				)->None:
          from timeit import timeit

          print('''Without Numpy'''				)
          print(
              timeit(
                  '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''     ,				number=1_0_0_0_0     ,				globals=globals()     ,				)				)
          print('''With Numpy'''				)
          print(
              timeit(
                  '''euclidean_distance([1, 2, 3], [4, 5, 6])'''     ,				number=1_0_0_0_0     ,				globals=globals()     ,				)				)


    benchmark()











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





   def 	__lowerCAmelCase							(	self			):
         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=_lowerCAmelCase       ,     activation_fn='''gelu-approximate'''       ,     num_embeds_ada_norm=1_000       ,     norm_type='''ada_norm_zero'''       ,     norm_elementwise_affine=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  AutoencoderKL()
         _lowerCAmelCase 			=  DDIMScheduler()
         _lowerCAmelCase 			=  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
         if str(_lowerCAmelCase			).startswith('''mps'''			):
               _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
         _lowerCAmelCase 			=  {
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''cpu'''

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

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

         self.assertEqual(image.shape       ,     (1, 16, 16, 3)			)
         _lowerCAmelCase 			=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]			)
         _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
         self.assertLessEqual(_lowerCAmelCase       ,     1E-3			)





   def 	__lowerCAmelCase							(	self			):
         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 	__lowerCAmelCase							(	self			):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)






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





   def 	__lowerCAmelCase							(	self			):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 	__lowerCAmelCase							(	self			):
         _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(_lowerCAmelCase			)

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

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _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 	__lowerCAmelCase							(	self			):
         _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(_lowerCAmelCase			)

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

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _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