from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		Dict      ):
 SCREAMING_SNAKE_CASE__:   Any=      k_size // 2
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
 SCREAMING_SNAKE_CASE__:   List[str]=      1 / (2 * pi * sigma) * exp(-(square(snake_case_      ) + square(snake_case_      )) / (2 * square(snake_case_      ))      )
 return g







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		str       ,				snake_case_ :		Dict      ):
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      image.shape[0], image.shape[1]
 # dst image height and width
 SCREAMING_SNAKE_CASE__:   Any=      height - k_size + 1
 SCREAMING_SNAKE_CASE__:   Optional[int]=      width - k_size + 1

 # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
 SCREAMING_SNAKE_CASE__:   Optional[int]=      zeros((dst_height * dst_width, k_size * k_size)      )
 SCREAMING_SNAKE_CASE__:   Tuple=      0
 for i, j in product(range(snake_case_      )       ,				range(snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Tuple=      ravel(image[i : i + k_size, j : j + k_size]      )
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      window
  row += 1

 #  turn the kernel into shape(k*k, 1)
 SCREAMING_SNAKE_CASE__:   Optional[int]=      gen_gaussian_kernel(snake_case_       ,				snake_case_      )
 SCREAMING_SNAKE_CASE__:   Any=      ravel(snake_case_      )

 # reshape and get the dst image
 SCREAMING_SNAKE_CASE__:   Any=      dot(snake_case_       ,				snake_case_      ).reshape(snake_case_       ,				snake_case_      ).astype(snake_case_      )

 return dst


if __name__ == "__main__":
      # read original image
      lowercase_     :   Optional[int] 				=				imread(R'../image_data/lena.jpg')
      # turn image in gray scale value
      lowercase_     :   Tuple 				=				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      lowercase_     :   Tuple 				=				gaussian_filter(gray, 3, sigma=1)
      lowercase_     :   Optional[Any] 				=				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow('gaussian filter with 3x3 mask', gaussianaxa)
      imshow('gaussian filter with 5x5 mask', gaussianaxa)
      waitKey()


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      AltDiffusionPipeline
    __a  =      TEXT_TO_IMAGE_PARAMS
    __a  =      TEXT_TO_IMAGE_BATCH_PARAMS
    __a  =      TEXT_TO_IMAGE_IMAGE_PARAMS
    __a  =      TEXT_TO_IMAGE_IMAGE_PARAMS
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      UNetaDConditionModel(
         block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
     SCREAMING_SNAKE_CASE__:   Tuple=      DDIMScheduler(
         beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCAmelCase , set_alpha_to_one=lowerCAmelCase , )
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      AutoencoderKL(
         block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )

     # TODO: address the non-deterministic text encoder (fails for save-load tests)
     # torch.manual_seed(0)
     # text_encoder_config = RobertaSeriesConfig(
     #     hidden_size=32,
     #     project_dim=32,
     #     intermediate_size=37,
     #     layer_norm_eps=1e-05,
     #     num_attention_heads=4,
     #     num_hidden_layers=5,
     #     vocab_size=5002,
     # )
     # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      CLIPTextConfig(
         bos_token_id=0 , eos_token_id=2 , hidden_size=32 , 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=5002 , )
     SCREAMING_SNAKE_CASE__:   List[Any]=      CLIPTextModel(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      77

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''safety_checker''': None,
         '''feature_extractor''': None,
     }
     return components
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0   )       ->					List[Any]:
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Any=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 6.0,
         '''output_type''': '''numpy''',
     }
     return inputs
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__:   Tuple=      self.get_dummy_components()
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      RobertaSeriesConfig(
         hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
     # TODO: remove after fixing the non-deterministic text encoder
     SCREAMING_SNAKE_CASE__:   List[str]=      RobertaSeriesModelWithTransformation(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      text_encoder

     SCREAMING_SNAKE_CASE__:   List[Any]=      AltDiffusionPipeline(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      self.get_dummy_inputs(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      '''A photo of an astronaut'''
     SCREAMING_SNAKE_CASE__:   Any=      alt_pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      output.images
     SCREAMING_SNAKE_CASE__:   Tuple=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE__:   Any=      np.array(
         [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]   )

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



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[str]=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__:   Any=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__:   List[str]=      PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      RobertaSeriesConfig(
         hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
     # TODO: remove after fixing the non-deterministic text encoder
     SCREAMING_SNAKE_CASE__:   str=      RobertaSeriesModelWithTransformation(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      text_encoder
     SCREAMING_SNAKE_CASE__:   Any=      AltDiffusionPipeline(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_dummy_inputs(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      alt_pipe(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      output.images
     SCREAMING_SNAKE_CASE__:   Tuple=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE__:   List[Any]=      np.array(
         [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]   )

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






@slow
@require_torch_gpu
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__:   Any=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      '''A painting of a squirrel eating a burger'''
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__:   Tuple=      alt_pipe([prompt] , generator=lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np'''   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      output.images

     SCREAMING_SNAKE_CASE__:   Dict=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE__:   Optional[int]=      np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]   )

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



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler'''   )

     SCREAMING_SNAKE_CASE__:   List[str]=      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=lowerCAmelCase , safety_checker=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      alt_pipe.to(lowerCAmelCase   )
     alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      '''A painting of a squirrel eating a burger'''
     SCREAMING_SNAKE_CASE__:   Any=      torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__:   str=      alt_pipe([prompt] , generator=lowerCAmelCase , num_inference_steps=2 , output_type='''numpy'''   )
     SCREAMING_SNAKE_CASE__:   List[str]=      output.images

     SCREAMING_SNAKE_CASE__:   Dict=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]   )

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

