






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class 				_A			(     UpperCamelCase					,					UpperCamelCase					,					UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Dict				= StableUnCLIPImgaImgPipeline
    lowerCamelCase	:							Dict				= TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    lowerCamelCase	:							Union[str, Any]				= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowerCamelCase	:							Union[str, Any]				= frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCamelCase	:							List[Any]				= frozenset([]				)



    def _a							(				self      :     Any      )      ->							Optional[int]:
           __UpperCAmelCase     				=32
           __UpperCAmelCase     				=embedder_hidden_size

           # image encoding components

           __UpperCAmelCase     				=CLIPImageProcessor(crop_size=32 ,     size=32      )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=CLIPVisionModelWithProjection(
               CLIPVisionConfig(
                   hidden_size=__SCREAMING_SNAKE_CASE ,     projection_dim=__SCREAMING_SNAKE_CASE ,     num_hidden_layers=5 ,     num_attention_heads=4 ,     image_size=32 ,     intermediate_size=37 ,     patch_size=1 ,     )      )

           # regular denoising components

           torch.manual_seed(0      )
           __UpperCAmelCase     				=StableUnCLIPImageNormalizer(embedding_dim=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""      )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=CLIPTextModel(
               CLIPTextConfig(
                   bos_token_id=0 ,     eos_token_id=2 ,     hidden_size=__SCREAMING_SNAKE_CASE ,     projection_dim=32 ,     intermediate_size=37 ,     layer_norm_eps=1e-05 ,     num_attention_heads=4 ,     num_hidden_layers=5 ,     pad_token_id=1 ,     vocab_size=1000 ,     )      )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=UNetaDConditionModel(
               sample_size=32 ,     in_channels=4 ,     out_channels=4 ,     down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") ,     up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") ,     block_out_channels=(32, 64) ,     attention_head_dim=(2, 4) ,     class_embed_type="""projection""" ,     projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,     cross_attention_dim=__SCREAMING_SNAKE_CASE ,     layers_per_block=1 ,     upcast_attention=__SCREAMING_SNAKE_CASE ,     use_linear_projection=__SCREAMING_SNAKE_CASE ,     )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=DDIMScheduler(
               beta_schedule="""scaled_linear""" ,     beta_start=0.00_085 ,     beta_end=0.012 ,     prediction_type="""v_prediction""" ,     set_alpha_to_one=__SCREAMING_SNAKE_CASE ,     steps_offset=1 ,     )

           torch.manual_seed(0      )
           __UpperCAmelCase     				=AutoencoderKL()

           __UpperCAmelCase     				={
               # image encoding components
               """feature_extractor""": feature_extractor,
               """image_encoder""": image_encoder.eval(),
               # image noising components
               """image_normalizer""": image_normalizer.eval(),
               """image_noising_scheduler""": image_noising_scheduler,
               # regular denoising components
               """tokenizer""": tokenizer,
               """text_encoder""": text_encoder.eval(),
               """unet""": unet.eval(),
               """scheduler""": scheduler,
               """vae""": vae.eval(),
           }

           return components



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Any=0 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=True      )      ->							Dict:
           if str(__SCREAMING_SNAKE_CASE      ).startswith("""mps"""      ):
                  __UpperCAmelCase     				=torch.manual_seed(__SCREAMING_SNAKE_CASE      )
           else:
                  __UpperCAmelCase     				=torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=floats_tensor((1, 3, 32, 32) ,     rng=random.Random(__SCREAMING_SNAKE_CASE      )      ).to(__SCREAMING_SNAKE_CASE      )

           if pil_image:
                  __UpperCAmelCase     				=input_image * 0.5 + 0.5
                  __UpperCAmelCase     				=input_image.clamp(0 ,     1      )
                  __UpperCAmelCase     				=input_image.cpu().permute(0 ,     2 ,     3 ,     1      ).float().numpy()
                  __UpperCAmelCase     				=DiffusionPipeline.numpy_to_pil(__SCREAMING_SNAKE_CASE      )[0]

           return {
               "prompt": "An anime racoon running a marathon",
               "image": input_image,
               "generator": generator,
               "num_inference_steps": 2,
               "output_type": "np",
           }



    @skip_mps
    def _a							(				self      :     Tuple      )      ->							str:
           __UpperCAmelCase     				="""cpu"""  # ensure determinism for the device-dependent torch.Generator
           __UpperCAmelCase     				=self.get_dummy_components()
           __UpperCAmelCase     				=StableUnCLIPImgaImgPipeline(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=sd_pipe.to(__SCREAMING_SNAKE_CASE      )
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs(__SCREAMING_SNAKE_CASE      )
           inputs.update({"""image_embeds""": None}      )
           __UpperCAmelCase     				=sd_pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           __UpperCAmelCase     				=np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3



    def _a							(				self      :     int      )      ->							str:
           __UpperCAmelCase     				=torch_device in ["""cpu""", """mps"""]

           self._test_attention_slicing_forward_pass(test_max_difference=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Any:
           __UpperCAmelCase     				=torch_device in ["""cpu""", """mps"""]

           self._test_inference_batch_single_identical(test_max_difference=__SCREAMING_SNAKE_CASE      )



    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available() ,     reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,     )
    def _a							(				self      :     List[Any]      )      ->							Tuple:
           self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__SCREAMING_SNAKE_CASE      )




@slow
@require_torch_gpu
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     str      )      ->							int:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def _a							(				self      :     Tuple      )      ->							List[str]:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""      )

           __UpperCAmelCase     				=load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""      )

           __UpperCAmelCase     				=StableUnCLIPImgaImgPipeline.from_pretrained(
               """fusing/stable-unclip-2-1-l-img2img""" ,     torch_dtype=torch.floataa      )
           pipe.to(__SCREAMING_SNAKE_CASE      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )
           # stable unclip will oom when integration tests are run on a V100,
           # so turn on memory savings
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           __UpperCAmelCase     				=torch.Generator(device="""cpu"""      ).manual_seed(0      )
           __UpperCAmelCase     				=pipe(__SCREAMING_SNAKE_CASE ,     """anime turle""" ,     generator=__SCREAMING_SNAKE_CASE ,     output_type="""np"""      )

           __UpperCAmelCase     				=output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Union[str, Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""      )

           __UpperCAmelCase     				=load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""      )

           __UpperCAmelCase     				=StableUnCLIPImgaImgPipeline.from_pretrained(
               """fusing/stable-unclip-2-1-h-img2img""" ,     torch_dtype=torch.floataa      )
           pipe.to(__SCREAMING_SNAKE_CASE      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )
           # stable unclip will oom when integration tests are run on a V100,
           # so turn on memory savings
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           __UpperCAmelCase     				=torch.Generator(device="""cpu"""      ).manual_seed(0      )
           __UpperCAmelCase     				=pipe(__SCREAMING_SNAKE_CASE ,     """anime turle""" ,     generator=__SCREAMING_SNAKE_CASE ,     output_type="""np"""      )

           __UpperCAmelCase     				=output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Union[str, Any]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""      )

           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           __UpperCAmelCase     				=StableUnCLIPImgaImgPipeline.from_pretrained(
               """fusing/stable-unclip-2-1-h-img2img""" ,     torch_dtype=torch.floataa      )
           __UpperCAmelCase     				=pipe.to(__SCREAMING_SNAKE_CASE      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )
           pipe.enable_attention_slicing()
           pipe.enable_sequential_cpu_offload()

           __UpperCAmelCase     				=pipe(
               __SCREAMING_SNAKE_CASE ,     """anime turtle""" ,     num_inference_steps=2 ,     output_type="""np""" ,     )

           __UpperCAmelCase     				=torch.cuda.max_memory_allocated()
           # make sure that less than 7 GB is allocated
           assert mem_bytes < 7 * 10**9








def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ )

   for i in range(n - 1 ):
      for j in range(i + 1	,    lowerCAmelCase_ ):
         if arr[i] > arr[j]:
            num_inversions += 1

   return num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Any:
   if len(lowerCAmelCase_ ) <= 1:
      return arr, 0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) // 2
   UpperCAmelCase      					=  arr[0:mid]
   UpperCAmelCase      					=  arr[mid:]

   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  _count_cross_inversions(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  inversion_p + inversions_q + cross_inversions
   return c, num_inversions
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  UpperCAmelCase      					=  UpperCAmelCase      					=  0
   while i < len(lowerCAmelCase_ ) and j < len(lowerCAmelCase_ ):
      if p[i] > q[j]:
         # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
         # These are all inversions. The claim emerges from the
         # property that P is sorted.
         num_inversion += len(lowerCAmelCase_ ) - i
         r.append(q[j] )
         j += 1
      else:
         r.append(p[i] )
         i += 1

   if i < len(lowerCAmelCase_ ):
      r.extend(p[i:] )
   else:
      r.extend(q[j:] )

   return r, num_inversion
def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  [1_0, 2, 1, 5, 5, 2, 1_1]

   # this arr has 8 inversions:
   # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 8

   print("""number of inversions = """	,    lowerCAmelCase_ )

   # testing an array with zero inversion (a sorted arr_1)

   arr_a.sort()
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )

   # an empty list should also have zero inversions
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  count_inversions_bf(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  count_inversions_recursive(lowerCAmelCase_ )

   assert num_inversions_bf == num_inversions_recursive == 0
   print("""number of inversions = """	,    lowerCAmelCase_ )


if __name__ == "__main__":
 main()



