





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   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     KandinskyInpaintPipeline
 lowercase_						=     ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_						=     [
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_						=     [
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_						=     False

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->List[Any]:



   '''simple docstring'''



   return 32

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->str:



   '''simple docstring'''



   return 32

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->Dict:



   '''simple docstring'''



   return self.time_input_dim

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->str:



   '''simple docstring'''



   return self.time_input_dim * 4

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->int:



   '''simple docstring'''



   return 100

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
   return tokenizer

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->List[str]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__: Optional[int]         =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=1_005			,					)

   lowerCamelCase__: Optional[int]         =MultilingualCLIP(UpperCAmelCase_)
   lowerCamelCase__: str         =text_encoder.eval()

   return text_encoder

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Union[str, Any]:



   '''simple docstring'''



   torch.manual_seed(0)

   lowerCamelCase__: Union[str, 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,
   }

   lowerCamelCase__: Dict         =UNetaDConditionModel(**UpperCAmelCase_)
   return model

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Dict:



   '''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							:  Optional[int])			->str:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__: Optional[Any]         =VQModel(**self.dummy_movq_kwargs)
   return model

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.dummy_text_encoder
   lowerCamelCase__: Dict         =self.dummy_tokenizer
   lowerCamelCase__: Tuple         =self.dummy_unet
   lowerCamelCase__: List[Any]         =self.dummy_movq

   lowerCamelCase__: Tuple         =DDIMScheduler(
       num_train_timesteps=1_000			,					beta_schedule="linear"			,					beta_start=0.0_0085			,					beta_end=0.012			,					clip_sample=UpperCAmelCase_			,					set_alpha_to_one=UpperCAmelCase_			,					steps_offset=1			,					prediction_type="epsilon"			,					thresholding=UpperCAmelCase_			,					)

   lowerCamelCase__: List[str]         ={
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  str=0)			->int:



   '''simple docstring'''



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

   if str(UpperCAmelCase_).startswith("mps"):
     lowerCamelCase__: Union[str, Any]         =torch.manual_seed(UpperCAmelCase_)
   else:
     lowerCamelCase__: Optional[Any]         =torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
   lowerCamelCase__: 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							:  Dict)			->Any:



   '''simple docstring'''



   lowerCamelCase__: int         ="cpu"

   lowerCamelCase__: Any         =self.get_dummy_components()

   lowerCamelCase__: Optional[Any]         =self.pipeline_class(**UpperCAmelCase_)
   lowerCamelCase__: List[str]         =pipe.to(UpperCAmelCase_)

   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__: int         =pipe(**self.get_dummy_inputs(UpperCAmelCase_))
   lowerCamelCase__: List[Any]         =output.images

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

   lowerCamelCase__: Tuple         =image[0, -3:, -3:, -1]
   lowerCamelCase__: Union[str, Any]         =image_from_tuple[0, -3:, -3:, -1]

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

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

   lowerCamelCase__: List[str]         =np.array(
       [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786])

   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							:  List[str])			->List[str]:



   '''simple docstring'''



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



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







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->List[str]:



   '''simple docstring'''



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







 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy")

   lowerCamelCase__: Union[str, Any]         =load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
   lowerCamelCase__: Optional[int]         =np.ones((768, 768)			,					dtype=np.floataa)
   lowerCamelCase__: Tuple         =0

   lowerCamelCase__: int         ="a hat"

   lowerCamelCase__: Optional[Any]         =KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,					torch_dtype=torch.floataa)
   pipe_prior.to(UpperCAmelCase_)

   lowerCamelCase__: Any         =KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,					torch_dtype=torch.floataa)
   lowerCamelCase__: Optional[Any]         =pipeline.to(UpperCAmelCase_)
   pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__: Any         =torch.Generator(device="cpu").manual_seed(0)
   lowerCamelCase__    ,   lowerCamelCase__: Dict         =pipe_prior(
       UpperCAmelCase_			,					generator=UpperCAmelCase_			,					num_inference_steps=5			,					negative_prompt=""			,					).to_tuple()

   lowerCamelCase__: List[str]         =pipeline(
       UpperCAmelCase_			,					image=UpperCAmelCase_			,					mask_image=UpperCAmelCase_			,					image_embeds=UpperCAmelCase_			,					negative_image_embeds=UpperCAmelCase_			,					generator=UpperCAmelCase_			,					num_inference_steps=100			,					height=768			,					width=768			,					output_type="np"			,					)

   lowerCamelCase__: str         =output.images[0]

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

   assert_mean_pixel_difference(UpperCAmelCase_			,					UpperCAmelCase_)




__lowercase				      =						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						{
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      assert len(str(SCREAMING_SNAKE_CASE   )   ) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      A_							       =		year // 100
      A_							       =		(5 * (century % 4) + 2) % 7
      A_							       =		year % 100
      A_							       =		centurian % 12
      A_							       =		(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      A_							       =		(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      A_							       =		(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()





