





import random
import unittest

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

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 						_lowerCAmelCase(						_a		,    unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__			     =    KandinskyVaaPriorEmbaEmbPipeline
  lowerCamelCase__			     =    ['''prompt''', '''image''']
  lowerCamelCase__			     =    ['''prompt''', '''image''']
  lowerCamelCase__			     =    [
      '''num_images_per_prompt''',
      '''strength''',
      '''generator''',
      '''num_inference_steps''',
      '''latents''',
      '''negative_prompt''',
      '''guidance_scale''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__			     =    False

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					List[str]:
     return 32

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Union[str, Any]:
     return 32

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Optional[int]:
     return self.time_input_dim

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					int:
     return self.time_input_dim * 4

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					str:
     return 1_00

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					List[str]:
     __A      					=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)
     return tokenizer

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Optional[int]:
     torch.manual_seed(0	)
     __A      					=   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(lowercase_	)

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					List[str]:
     torch.manual_seed(0	)

     __A      					=   {
         '''num_attention_heads''': 2,
         '''attention_head_dim''': 12,
         '''embedding_dim''': self.text_embedder_hidden_size,
         '''num_layers''': 1,
     }

     __A      					=   PriorTransformer(**lowercase_	)
     # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
     __A      					=   nn.Parameter(torch.ones(model.clip_std.shape	)	)
     return model

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Union[str, Any]:
     torch.manual_seed(0	)
     __A      					=   CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size  ,				image_size=2_24  ,				projection_dim=self.text_embedder_hidden_size  ,				intermediate_size=37  ,				num_attention_heads=4  ,				num_channels=3  ,				num_hidden_layers=5  ,				patch_size=14  ,				)

     __A      					=   CLIPVisionModelWithProjection(lowercase_	)
     return model

  @property
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Union[str, Any]:
     __A      					=   CLIPImageProcessor(
         crop_size=2_24  ,				do_center_crop=lowercase_  ,				do_normalize=lowercase_  ,				do_resize=lowercase_  ,				image_mean=[0.48145466, 0.4578275, 0.40821073]  ,				image_std=[0.26862954, 0.26130258, 0.27577711]  ,				resample=3  ,				size=2_24  ,				)

     return image_processor

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Optional[Any]:
     __A      					=   self.dummy_prior
     __A      					=   self.dummy_image_encoder
     __A      					=   self.dummy_text_encoder
     __A      					=   self.dummy_tokenizer
     __A      					=   self.dummy_image_processor

     __A      					=   UnCLIPScheduler(
         variance_type='''fixed_small_log'''  ,				prediction_type='''sample'''  ,				num_train_timesteps=10_00  ,				clip_sample=lowercase_  ,				clip_sample_range=10.0  ,				)

     __A      					=   {
         '''prior''': prior,
         '''image_encoder''': image_encoder,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''scheduler''': scheduler,
         '''image_processor''': image_processor,
     }

     return components

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase=0	)->					Tuple:







     if str(lowercase_	).startswith('''mps'''	):
        __A      					=   torch.manual_seed(lowercase_	)
     else:
        __A      					=   torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

     __A      					=   floats_tensor((1, 3, 64, 64)  ,				rng=random.Random(lowercase_	)	).to(lowercase_	)
     __A      					=   image.cpu().permute(0  ,				2  ,				3  ,				1	)[0]
     __A      					=   Image.fromarray(np.uinta(lowercase_	)	).convert('''RGB'''	).resize((2_56, 2_56)	)

     __A      					=   {
         '''prompt''': '''horse''',
         '''image''': init_image,
         '''strength''': 0.5,
         '''generator''': generator,
         '''guidance_scale''': 4.0,
         '''num_inference_steps''': 2,
         '''output_type''': '''np''',
     }
     return inputs

  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Union[str, Any]:
     __A      					=   '''cpu'''

     __A      					=   self.get_dummy_components()

     __A      					=   self.pipeline_class(**lowercase_	)
     __A      					=   pipe.to(lowercase_	)

     pipe.set_progress_bar_config(disable=lowercase_	)

     __A      					=   pipe(**self.get_dummy_inputs(lowercase_	)	)
     __A      					=   output.image_embeds

     __A      					=   pipe(
         **self.get_dummy_inputs(lowercase_	)  ,				return_dict=lowercase_  ,				)[0]

     __A      					=   image[0, -10:]
     __A      					=   image_from_tuple[0, -10:]

     assert image.shape == (1, 32)

     __A      					=   np.array(
         [
             0.1071284,
             1.3330271,
             0.61260223,
             -0.6691065,
             -0.3846852,
             -1.0303661,
             0.22716111,
             0.03348901,
             0.30040675,
             -0.24805029,
         ]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2

  @skip_mps
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Optional[int]:
     __A      					=   torch_device == '''cpu'''
     __A      					=   True
     __A      					=   False

     self._test_inference_batch_single_identical(
         test_max_difference=lowercase_  ,				relax_max_difference=lowercase_  ,				test_mean_pixel_difference=lowercase_  ,				)





  @skip_mps
  def 		SCREAMING_SNAKE_CASE__       (					self	)->					Union[str, Any]:
     __A      					=   torch_device == '''cpu'''
     __A      					=   False

     self._test_attention_slicing_forward_pass(
         test_max_difference=lowercase_  ,				test_mean_pixel_difference=lowercase_  ,				)


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCamelCase__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase				=  k_size // 2
  _UpperCamelCase	,		_UpperCamelCase				=  np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _UpperCamelCase				=  (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      _UpperCamelCase				=  gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row, col - 1]
        _UpperCamelCase				=  sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row + 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col]
        _UpperCamelCase				=  sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _UpperCamelCase				=  sobel_grad[row, col]

  return destination


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      if destination[row, col] >= threshold_high:
        _UpperCamelCase				=  strong
      elif destination[row, col] <= threshold_low:
        _UpperCamelCase				=  0
      else:
        _UpperCamelCase				=  weak


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0]      ):
    for col in range(1  ,			image_shape[1]      ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _UpperCamelCase				=  strong
        else:
          _UpperCamelCase				=  0


def 							lowerCAmelCase__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  return destination


if __name__ == "__main__":
   # read original image in gray mode
   lowerCamelCase__   = cva.imread(R'''../image_data/lena.jpg''', 0)
   # canny edge detection
   lowerCamelCase__   = canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)




