

import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[Any]    	=     UnCLIPImageVariationPipeline
      _A						:		Optional[int]    	=     IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _A						:		Union[str, Any]    	=     IMAGE_VARIATION_BATCH_PARAMS

      _A						:		List[Any]    	=     [
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _A						:		int    	=     False
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return 32
      @property
      def A_			(			self ):
          return self.time_input_dim
      @property
      def A_			(			self ):
          return self.time_input_dim * 4
      @property
      def A_			(			self ):
          return 1_00
      @property
      def A_			(			self ):
          snake_case__						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
          return tokenizer
      @property
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				CLIPTextConfig(
              bos_token_id=0		,	eos_token_id=2		,	hidden_size=self.text_embedder_hidden_size		,	projection_dim=self.text_embedder_hidden_size		,	intermediate_size=37		,	layer_norm_eps=1e-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=10_00		,	)
          return CLIPTextModelWithProjection(lowerCamelCase )
      @property
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size		,	projection_dim=self.text_embedder_hidden_size		,	num_hidden_layers=5		,	num_attention_heads=4		,	image_size=32		,	intermediate_size=37		,	patch_size=1		,	)
          return CLIPVisionModelWithProjection(lowerCamelCase )
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				{
              "clip_embeddings_dim": self.text_embedder_hidden_size,
              "time_embed_dim": self.time_embed_dim,
              "cross_attention_dim": self.cross_attention_dim,
          }

          snake_case__						=				UnCLIPTextProjModel(**lowerCamelCase )
          return model
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				{
              "sample_size": 32,
              # RGB in channels
              "in_channels": 3,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 6,
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": "identity",
          }

          snake_case__						=				UNetaDConditionModel(**lowerCamelCase )
          return model
      @property
      def A_			(			self ):
          return {
              "sample_size": 64,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }
      @property
      def A_			(			self ):
          torch.manual_seed(0 )

          snake_case__						=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model
      @property
      def A_			(			self ):
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1 )

          snake_case__						=				UNetaDModel(**self.dummy_super_res_kwargs )
          return model
      def A_			(			self ):
          snake_case__						=				self.dummy_decoder
          snake_case__						=				self.dummy_text_proj
          snake_case__						=				self.dummy_text_encoder
          snake_case__						=				self.dummy_tokenizer
          snake_case__						=				self.dummy_super_res_first
          snake_case__						=				self.dummy_super_res_last

          snake_case__						=				UnCLIPScheduler(
              variance_type="learned_range"		,	prediction_type="epsilon"		,	num_train_timesteps=10_00		,	)

          snake_case__						=				UnCLIPScheduler(
              variance_type="fixed_small_log"		,	prediction_type="epsilon"		,	num_train_timesteps=10_00		,	)

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

          snake_case__						=				self.dummy_image_encoder

          return {
              "decoder": decoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_proj": text_proj,
              "feature_extractor": feature_extractor,
              "image_encoder": image_encoder,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0		,	lowerCamelCase=True ):
          snake_case__						=				floats_tensor((1, 3, 32, 32)		,	rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
          if str(lowerCamelCase ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(lowerCamelCase )
          else:
              snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )

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

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }
      def A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

          snake_case__						=				image[0, -3:, -3:, -1]
          snake_case__						=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          snake_case__						=				np.array(
              [
                  0.9_9_9_7,
                  0.0_0_0_2,
                  0.9_9_9_7,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.0_0_2_3,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.9_9_7_0,
              ] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
      def A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

          snake_case__						=				image[0, -3:, -3:, -1]
          snake_case__						=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          snake_case__						=				np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
      def A_			(			self ):
          snake_case__						=				"cpu"

          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          snake_case__						=				[
              pipeline_inputs["image"],
              pipeline_inputs["image"],
          ]

          snake_case__						=				pipe(**lowerCamelCase )
          snake_case__						=				output.images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          snake_case__						=				[
              tuple_pipeline_inputs["image"],
              tuple_pipeline_inputs["image"],
          ]

          snake_case__						=				pipe(
              **lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

          snake_case__						=				image[0, -3:, -3:, -1]
          snake_case__						=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (2, 64, 64, 3)

          snake_case__						=				np.array(
              [
                  0.9_9_9_7,
                  0.9_9_8_9,
                  0.0_0_0_8,
                  0.0_0_2_1,
                  0.9_9_6_0,
                  0.0_0_1_8,
                  0.0_0_1_4,
                  0.0_0_0_2,
                  0.9_9_3_3,
              ] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
      def A_			(			self ):
          snake_case__						=				torch.device("cpu" )




          class 					_SCREAMING_SNAKE_CASE  :
                _A						:		Dict    	=     1





          snake_case__						=				self.get_dummy_components()

          snake_case__						=				self.pipeline_class(**lowerCamelCase )
          snake_case__						=				pipe.to(lowerCamelCase )

          pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(0 )
          snake_case__						=				pipe.decoder.dtype
          snake_case__						=				1

          snake_case__						=				(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          snake_case__						=				pipe.prepare_latents(
              lowerCamelCase		,	dtype=lowerCamelCase		,	device=lowerCamelCase		,	generator=lowerCamelCase		,	latents=lowerCamelCase		,	scheduler=DummyScheduler() )

          snake_case__						=				(
              batch_size,
              pipe.super_res_first.config.in_channels // 2,
              pipe.super_res_first.config.sample_size,
              pipe.super_res_first.config.sample_size,
          )
          snake_case__						=				pipe.prepare_latents(
              lowerCamelCase		,	dtype=lowerCamelCase		,	device=lowerCamelCase		,	generator=lowerCamelCase		,	latents=lowerCamelCase		,	scheduler=DummyScheduler() )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )

          snake_case__						=				pipe(
              **lowerCamelCase		,	decoder_latents=lowerCamelCase		,	super_res_latents=lowerCamelCase ).images

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase		,	pil_image=lowerCamelCase )
          # Don't pass image, instead pass embedding
          snake_case__						=				pipeline_inputs.pop("image" )
          snake_case__						=				pipe.image_encoder(lowerCamelCase ).image_embeds

          snake_case__						=				pipe(
              **lowerCamelCase		,	decoder_latents=lowerCamelCase		,	super_res_latents=lowerCamelCase		,	image_embeddings=lowerCamelCase		,	).images

          # make sure passing text embeddings manually is identical
          assert np.abs(img_out_a - img_out_a ).max() < 1e-4
      @skip_mps
      def A_			(			self ):
          snake_case__						=				torch_device == "cpu"

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          snake_case__						=				1e-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=lowerCamelCase		,	expected_max_diff=lowerCamelCase )
      @skip_mps
      def A_			(			self ):
          snake_case__						=				torch_device == "cpu"
          snake_case__						=				True
          snake_case__						=				[
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          self._test_inference_batch_single_identical(
              test_max_difference=lowerCamelCase		,	relax_max_difference=lowerCamelCase		,	additional_params_copy_to_batched_inputs=lowerCamelCase		,	)
      def A_			(			self ):
          snake_case__						=				[
              "decoder_num_inference_steps",
              "super_res_num_inference_steps",
          ]

          if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case__						=				[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=lowerCamelCase		,	additional_params_copy_to_batched_inputs=lowerCamelCase		,	)
          else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=lowerCamelCase )
      @skip_mps
      def A_			(			self ):
          return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def A_			(			self ):
          return super().test_save_load_local()



      @skip_mps
      def A_			(			self ):
          return super().test_save_load_optional_components()





@slow
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def A_			(			self ):
          snake_case__						=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
          snake_case__						=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )

          snake_case__						=				UnCLIPImageVariationPipeline.from_pretrained(
              "kakaobrain/karlo-v1-alpha-image-variations"		,	torch_dtype=torch.floataa )
          snake_case__						=				pipeline.to(lowerCamelCase )
          pipeline.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				torch.Generator(device="cpu" ).manual_seed(0 )
          snake_case__						=				pipeline(
              lowerCamelCase		,	generator=lowerCamelCase		,	output_type="np"		,	)

          snake_case__						=				output.images[0]

          assert image.shape == (2_56, 2_56, 3)

          assert_mean_pixel_difference(lowerCamelCase		,	lowerCamelCase		,	15 )





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



