




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       a__   ( snake_case__			,    unittest.TestCase				):
      _a							:   Optional[Any] =   KandinskyInpaintPipeline
      _a							:   Optional[Any] =   ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      _a							:   Optional[int] =   [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      _a							:   List[Any] =   [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _a							:   str =   False






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           return 3_2






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           return 3_2






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           return self.time_input_dim






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           return self.time_input_dim * 4






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           return 1_0_0






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           __lowerCAmelCase           =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
           return tokenizer






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           torch.manual_seed(0      )
           __lowerCAmelCase           =	MCLIPConfig(
               numDims=self.cross_attention_dim		,      transformerDimensions=self.text_embedder_hidden_size		,      hidden_size=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      num_attention_heads=4		,      num_hidden_layers=5		,      vocab_size=1_0_0_5		,      )

           __lowerCAmelCase           =	MultilingualCLIP(_A      )
           __lowerCAmelCase           =	text_encoder.eval()

           return text_encoder






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           torch.manual_seed(0      )

           __lowerCAmelCase           =	{
               "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           =	UNetaDConditionModel(**_A      )
           return model






      @property
      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           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      ):




           """simple docstring"""



           torch.manual_seed(0      )
           __lowerCAmelCase           =	VQModel(**self.dummy_movq_kwargs      )
           return model






      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           __lowerCAmelCase           =	self.dummy_text_encoder
           __lowerCAmelCase           =	self.dummy_tokenizer
           __lowerCAmelCase           =	self.dummy_unet
           __lowerCAmelCase           =	self.dummy_movq

           __lowerCAmelCase           =	DDIMScheduler(
               num_train_timesteps=1_0_0_0		,      beta_schedule="linear"		,      beta_start=0.0_00_85		,      beta_end=0.0_12		,      clip_sample=_A		,      set_alpha_to_one=_A		,      steps_offset=1		,      prediction_type="epsilon"		,      thresholding=_A		,      )

           __lowerCAmelCase           =	{
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "unet": unet,
               "scheduler": scheduler,
               "movq": movq,
           }

           return components






      def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A=0      ):




           """simple docstring"""



           __lowerCAmelCase           =	floats_tensor((1, self.cross_attention_dim)		,      rng=random.Random(_A      )      ).to(_A      )
           __lowerCAmelCase           =	floats_tensor((1, self.cross_attention_dim)		,      rng=random.Random(seed + 1      )      ).to(_A      )
           # create init_image
           __lowerCAmelCase           =	floats_tensor((1, 3, 6_4, 6_4)		,      rng=random.Random(_A      )      ).to(_A      )
           __lowerCAmelCase           =	image.cpu().permute(0		,      2		,      3		,      1      )[0]
           __lowerCAmelCase           =	Image.fromarray(np.uinta(_A      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )
           # create mask
           __lowerCAmelCase           =	np.ones((6_4, 6_4)		,      dtype=np.floataa      )
           __lowerCAmelCase           =	0

           if str(_A      ).startswith("mps"      ):
                __lowerCAmelCase           =	torch.manual_seed(_A      )
           else:
                __lowerCAmelCase           =	torch.Generator(device=_A      ).manual_seed(_A      )
           __lowerCAmelCase           =	{
               "prompt": "horse",
               "image": init_image,
               "mask_image": mask,
               "image_embeds": image_embeds,
               "negative_image_embeds": negative_image_embeds,
               "generator": generator,
               "height": 6_4,
               "width": 6_4,
               "num_inference_steps": 2,
               "guidance_scale": 4.0,
               "output_type": "np",
           }
           return inputs






      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



           __lowerCAmelCase           =	"cpu"

           __lowerCAmelCase           =	self.get_dummy_components()

           __lowerCAmelCase           =	self.pipeline_class(**_A      )
           __lowerCAmelCase           =	pipe.to(_A      )

           pipe.set_progress_bar_config(disable=_A      )

           __lowerCAmelCase           =	pipe(**self.get_dummy_inputs(_A      )      )
           __lowerCAmelCase           =	output.images

           __lowerCAmelCase           =	pipe(
               **self.get_dummy_inputs(_A      )		,      return_dict=_A		,      )[0]

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

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

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

           __lowerCAmelCase           =	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      ):




           """simple docstring"""



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






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






      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



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






      def     __SCREAMING_SNAKE_CASE(       self      ):




           """simple docstring"""



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

           __lowerCAmelCase           =	load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
           __lowerCAmelCase           =	np.ones((7_6_8, 7_6_8)		,      dtype=np.floataa      )
           __lowerCAmelCase           =	0

           __lowerCAmelCase           =	"a hat"

           __lowerCAmelCase           =	KandinskyPriorPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-1-prior"		,      torch_dtype=torch.floataa      )
           pipe_prior.to(_A      )

           __lowerCAmelCase           =	KandinskyInpaintPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-1-inpaint"		,      torch_dtype=torch.floataa      )
           __lowerCAmelCase           =	pipeline.to(_A      )
           pipeline.set_progress_bar_config(disable=_A      )

           __lowerCAmelCase           =	torch.Generator(device="cpu"      ).manual_seed(0      )
           __lowerCAmelCase       ,							__lowerCAmelCase           =	pipe_prior(
               _A		,      generator=_A		,      num_inference_steps=5		,      negative_prompt=""		,      ).to_tuple()

           __lowerCAmelCase           =	pipeline(
               _A		,      image=_A		,      mask_image=_A		,      image_embeds=_A		,      negative_image_embeds=_A		,      generator=_A		,      num_inference_steps=1_0_0		,      height=7_6_8		,      width=7_6_8		,      output_type="np"		,      )

           __lowerCAmelCase           =	output.images[0]

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

           assert_mean_pixel_difference(_A		,      _A      )










import enum
import shutil
import sys


UpperCamelCase__      ,     UpperCamelCase__         =      shutil.get_terminal_size()

UpperCamelCase__         =      {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class       a__   ( enum.Enum				):
						_a							:   Any =   0
						_a							:   Dict =   1







def      _a				(						SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Dict=""					):
					sys.stdout.write(str(SCREAMING_SNAKE_CASE_					) + end					)
					sys.stdout.flush()







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    str=""					):
					forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					forceWrite("\r"					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    str					):
					forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""					)







def      _a				(						):
					forceWrite(" " * TERMINAL_WIDTH					)
					reset_cursor()







def      _a				(						):
					reset_cursor()
					forceWrite("-" * TERMINAL_WIDTH					)




