

'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      lowercase_      (lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    int							     =     KandinskyVaaImgaImgPipeline
  SCREAMING_SNAKE_CASE       :    List[str]							     =     ['image_embeds', 'negative_image_embeds', 'image']
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     [
      'image_embeds',
      'negative_image_embeds',
      'image',
  ]
  SCREAMING_SNAKE_CASE       :    int							     =     [
      'generator',
      'height',
      'width',
      'strength',
      'guidance_scale',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE       :    int							     =     False






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   return 3_2






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   return 3_2






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   return self.time_input_dim






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   return self.time_input_dim * 4






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   return 1_0_0






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   torch.manual_seed(0     )

   __lowercase				    =					{
       '''in_channels''': 4,
       # Out channels is double in channels because predicts mean and variance
       '''out_channels''': 8,
       '''addition_embed_type''': '''image''',
       '''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,
       '''encoder_hid_dim''': self.text_embedder_hidden_size,
       '''encoder_hid_dim_type''': '''image_proj''',
       '''cross_attention_dim''': self.cross_attention_dim,
       '''attention_head_dim''': 4,
       '''resnet_time_scale_shift''': '''scale_shift''',
       '''class_embed_type''': None,
   }

   __lowercase				    =					UNetaDConditionModel(**lowercase__     )
   return model






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   return {
       "block_out_channels": [3_2, 6_4],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 1_2,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   torch.manual_seed(0     )
   __lowercase				    =					VQModel(**self.dummy_movq_kwargs     )
   return model






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   __lowercase				    =					self.dummy_unet
   __lowercase				    =					self.dummy_movq

   __lowercase				    =					{
       '''num_train_timesteps''': 1_0_0_0,
       '''beta_schedule''': '''linear''',
       '''beta_start''': 0.0_0_0_8_5,
       '''beta_end''': 0.0_1_2,
       '''clip_sample''': False,
       '''set_alpha_to_one''': False,
       '''steps_offset''': 0,
       '''prediction_type''': '''epsilon''',
       '''thresholding''': False,
   }

   __lowercase				    =					DDIMScheduler(**lowercase__     )

   __lowercase				    =					{
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''movq''': movq,
   }

   return components






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	str   ,lowercase__  :	List[str]=0     ):
   __lowercase				    =					floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(lowercase__     )     ).to(lowercase__     )
   __lowercase				    =					floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1     )     ).to(
       lowercase__     )
   # create init_image
   __lowercase				    =					floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(lowercase__     )     ).to(lowercase__     )
   __lowercase				    =					image.cpu().permute(0   ,2   ,3   ,1     )[0]
   __lowercase				    =					Image.fromarray(np.uinta(lowercase__     )     ).convert('''RGB'''     ).resize((2_5_6, 2_5_6)     )

   if str(lowercase__     ).startswith('''mps'''     ):
    __lowercase				    =					torch.manual_seed(lowercase__     )
   else:
    __lowercase				    =					torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
   __lowercase				    =					{
       '''image''': init_image,
       '''image_embeds''': image_embeds,
       '''negative_image_embeds''': negative_image_embeds,
       '''generator''': generator,
       '''height''': 6_4,
       '''width''': 6_4,
       '''num_inference_steps''': 1_0,
       '''guidance_scale''': 7.0,
       '''strength''': 0.2,
       '''output_type''': '''np''',
   }
   return inputs






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					'''cpu'''

   __lowercase				    =					self.get_dummy_components()

   __lowercase				    =					self.pipeline_class(**lowercase__     )
   __lowercase				    =					pipe.to(lowercase__     )

   pipe.set_progress_bar_config(disable=lowercase__     )

   __lowercase				    =					pipe(**self.get_dummy_inputs(lowercase__     )     )
   __lowercase				    =					output.images

   __lowercase				    =					pipe(
       **self.get_dummy_inputs(lowercase__     )   ,return_dict=lowercase__   ,)[0]

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

   assert image.shape == (1, 6_4, 6_4, 3)

   __lowercase				    =					np.array(
       [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]     )
   assert (
       np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
   ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
   ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''     )

   __lowercase				    =					load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''     )
   __lowercase				    =					'''A red cartoon frog, 4k'''

   __lowercase				    =					KandinskyVaaPriorPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-2-prior'''   ,torch_dtype=torch.floataa     )
   pipe_prior.to(lowercase__     )

   __lowercase				    =					KandinskyVaaImgaImgPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-2-decoder'''   ,torch_dtype=torch.floataa     )
   __lowercase				    =					pipeline.to(lowercase__     )

   pipeline.set_progress_bar_config(disable=lowercase__     )

   __lowercase				    =					torch.Generator(device='''cpu'''     ).manual_seed(0     )
   __lowercase    ,      __lowercase				    =					pipe_prior(
       lowercase__   ,generator=lowercase__   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

   __lowercase				    =					pipeline(
       image=lowercase__   ,image_embeds=lowercase__   ,negative_image_embeds=lowercase__   ,generator=lowercase__   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,strength=0.2   ,output_type='''np'''   ,)

   __lowercase				    =					output.images[0]

   assert image.shape == (7_6_8, 7_6_8, 3)

   assert_mean_pixel_difference(lowercase__   ,lowercase__     )





"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					int   =		len(self.polyA)

      while self.polyB[-1] == 0:
         self.polyB.pop()
      _lowerCamelCase	:					Union[str, Any]   =		len(self.polyB)

      # Add 0 to make lengths equal a power of 2
      _lowerCamelCase	:					List[Any]   =		int(
          2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

      while len(self.polyA) < self.c_max_length:
         self.polyA.append(0)
      while len(self.polyB) < self.c_max_length:
         self.polyB.append(0)
      # A complex root used for the fourier transform
      _lowerCamelCase	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

      # The product
      _lowerCamelCase	:					int   =		self.__multiply()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
      # Corner case
      if len(SCREAMING_SNAKE_CASE) <= 1:
         return dft[0]
      #
      _lowerCamelCase	:					str   =		self.c_max_length // 2
      while next_ncol > 0:
         _lowerCamelCase	:					Dict   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					Tuple   =		self.root**next_ncol

         # First half of next step
         _lowerCamelCase	:					int   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
            current_root *= root
         # Second half of next step
         _lowerCamelCase	:					Optional[int]   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
            current_root *= root
         # Update
         _lowerCamelCase	:					Union[str, Any]   =		new_dft
         _lowerCamelCase	:					List[str]   =		next_ncol // 2
      return dft[0]





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[Any]   =		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]) <= 1:
         return inverce_c[0]
      # Inverse DFT
      _lowerCamelCase	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		1
         # First half of next step
         for j in range(self.c_max_length // next_ncol):
            for i in range(next_ncol // 2):
               # Even positions
               new_inverse_c[i].append(
                   (
                       inverce_c[i][j]
                       + inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / 2)
               # Odd positions
               new_inverse_c[i + next_ncol // 2].append(
                   (
                       inverce_c[i][j]
                       - inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / (2 * current_root))
            current_root *= root
         # Update
         _lowerCamelCase	:					Any   =		new_inverse_c
         next_ncol *= 2
      # Unpack
      _lowerCamelCase	:					Optional[Any]   =		[round(x[0].real      ,   8) + round(x[0].imag      ,   8) * 1j for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
         inverce_c.pop()
      return inverce_c





   def __str__(							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
      _lowerCamelCase	:					int   =		"""A*B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.product))

      return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()

