






"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 							lowerCAmelCase__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  KandinskyInpaintPipeline
      __UpperCamelCase	 =  ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
      __UpperCamelCase	 =  [
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
          "mask_image",
      ]
      __UpperCamelCase	 =  [
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      __UpperCamelCase	 =  False
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return 32
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          return 32
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          return self.time_input_dim
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return self.time_input_dim * 4
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return 100
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
          return tokenizer
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  str    						=		MCLIPConfig(
              numDims=self.cross_attention_dim						,	transformerDimensions=self.text_embedder_hidden_size						,	hidden_size=self.text_embedder_hidden_size						,	intermediate_size=37						,	num_attention_heads=4						,	num_hidden_layers=5						,	vocab_size=1005						,	)

          SCREAMING_SNAKE_CASE_      :  Any    						=		MultilingualCLIP(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		text_encoder.eval()

          return text_encoder
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          torch.manual_seed(0)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{
              '''in_channels''': 9,
              # Out channels is double in channels because predicts mean and variance
              '''out_channels''': 8,
              '''addition_embed_type''': '''text_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''': '''text_image_proj''',
              '''cross_attention_dim''': self.cross_attention_dim,
              '''attention_head_dim''': 4,
              '''resnet_time_scale_shift''': '''scale_shift''',
              '''class_embed_type''': None,
          }

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		UNetaDConditionModel(**lowercase_)
          return model
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          return {
              "block_out_channels": [32, 64],
              "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": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		VQModel(**self.dummy_movq_kwargs)
          return model
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_tokenizer
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_unet
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.dummy_movq

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		DDIMScheduler(
              num_train_timesteps=1000						,	beta_schedule='''linear'''						,	beta_start=0.0_00_85						,	beta_end=0.0_12						,	clip_sample=lowercase_						,	set_alpha_to_one=lowercase_						,	steps_offset=1						,	prediction_type='''epsilon'''						,	thresholding=lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  int=0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(lowercase_)).to(lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		floats_tensor((1, self.cross_attention_dim)						,	rng=random.Random(seed + 1)).to(lowercase_)
          # create init_image
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		floats_tensor((1, 3, 64, 64)						,	rng=random.Random(lowercase_)).to(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image.cpu().permute(0						,	2						,	3						,	1)[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		Image.fromarray(np.uinta(lowercase_)).convert('''RGB''').resize((256, 256))
          # create mask
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		np.ones((64, 64)						,	dtype=np.floataa)
          SCREAMING_SNAKE_CASE_      :  Any    						=		0

          if str(lowercase_).startswith('''mps'''):
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.manual_seed(lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  int    						=		torch.Generator(device=lowercase_).manual_seed(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		{
              '''prompt''': '''horse''',
              '''image''': init_image,
              '''mask_image''': mask,
              '''image_embeds''': image_embeds,
              '''negative_image_embeds''': negative_image_embeds,
              '''generator''': generator,
              '''height''': 64,
              '''width''': 64,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 4.0,
              '''output_type''': '''np''',
          }
          return inputs
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''cpu'''

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_dummy_components()

          SCREAMING_SNAKE_CASE_      :  int    						=		self.pipeline_class(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		pipe.to(lowercase_)

          pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pipe(**self.get_dummy_inputs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		output.images

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pipe(
              **self.get_dummy_inputs(lowercase_)						,	return_dict=lowercase_						,	)[0]

          SCREAMING_SNAKE_CASE_      :  int    						=		image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_      :  str    						=		image_from_tuple[0, -3:, -3:, -1]

          print(F'image.shape {image.shape}')

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

          SCREAMING_SNAKE_CASE_      :  str    						=		np.array(
              [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86])

          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()}'



      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          super().test_inference_batch_single_identical(expected_max_diff=3e-3)




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''')

          SCREAMING_SNAKE_CASE_      :  str    						=		load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		np.ones((768, 768)						,	dtype=np.floataa)
          SCREAMING_SNAKE_CASE_      :  str    						=		0

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''a hat'''

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		KandinskyPriorPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-prior'''						,	torch_dtype=torch.floataa)
          pipe_prior.to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		KandinskyInpaintPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-inpaint'''						,	torch_dtype=torch.floataa)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		pipeline.to(lowercase_)
          pipeline.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.Generator(device='''cpu''').manual_seed(0)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pipe_prior(
              lowercase_						,	generator=lowercase_						,	num_inference_steps=5						,	negative_prompt=''''''						,	).to_tuple()

          SCREAMING_SNAKE_CASE_      :  str    						=		pipeline(
              lowercase_						,	image=lowercase_						,	mask_image=lowercase_						,	image_embeds=lowercase_						,	negative_image_embeds=lowercase_						,	generator=lowercase_						,	num_inference_steps=100						,	height=768						,	width=768						,	output_type='''np'''						,	)

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		output.images[0]

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

          assert_mean_pixel_difference(lowercase_						,	lowercase_)










"""simple docstring"""






from __future__ import annotations




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  int = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''encrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''decrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


