



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	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			KandinskyInpaintPipeline
     lowerCAmelCase					=			["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
     lowerCAmelCase					=			[
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
         """mask_image""",
     ]
     lowerCAmelCase					=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     lowerCAmelCase					=			False





     @property
     def 	__snake_case    (       self :							int				)->int:
      return 3_2





     @property
     def 	__snake_case    (       self :							Tuple				)->str:
      return 3_2





     @property
     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      return self.time_input_dim





     @property
     def 	__snake_case    (       self :							List[str]				)->int:
      return self.time_input_dim * 4





     @property
     def 	__snake_case    (       self :							Dict				)->Any:
      return 1_0_0





     @property
     def 	__snake_case    (       self :							Tuple				)->Dict:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"				)
      return tokenizer





     @property
     def 	__snake_case    (       self :							Tuple				)->Any:
      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=3_7     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  vocab_size=1_0_0_5     ,  )

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MultilingualCLIP(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	text_encoder.eval()

      return text_encoder





     @property
     def 	__snake_case    (       self :							List[str]				)->int:
      torch.manual_seed(0				)

      __SCREAMING_SNAKE_CASE       :    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       :    Dict       =	UNetaDConditionModel(**UpperCamelCase				)
      return model





     @property
     def 	__snake_case    (       self :							Any				)->Any:
      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 	__snake_case    (       self :							Any				)->Tuple:
      torch.manual_seed(0				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	VQModel(**self.dummy_movq_kwargs				)
      return model





     def 	__snake_case    (       self :							Any				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.dummy_tokenizer
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.dummy_unet
      __SCREAMING_SNAKE_CASE       :    str       =	self.dummy_movq

      __SCREAMING_SNAKE_CASE       :    str       =	DDIMScheduler(
          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=UpperCamelCase     ,  set_alpha_to_one=UpperCamelCase     ,  steps_offset=1     ,  prediction_type="epsilon"     ,  thresholding=UpperCamelCase     ,  )

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	{
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							List[Any]=0				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	floats_tensor((1, self.cross_attention_dim)     ,  rng=random.Random(UpperCamelCase				)				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Any       =	floats_tensor((1, self.cross_attention_dim)     ,  rng=random.Random(seed + 1				)				).to(UpperCamelCase				)
      # create init_image
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	floats_tensor((1, 3, 6_4, 6_4)     ,  rng=random.Random(UpperCamelCase				)				).to(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	image.cpu().permute(0     ,  2     ,  3     ,  1				)[0]
      __SCREAMING_SNAKE_CASE       :    int       =	Image.fromarray(np.uinta(UpperCamelCase				)				).convert("RGB"				).resize((2_5_6, 2_5_6)				)
      # create mask
      __SCREAMING_SNAKE_CASE       :    str       =	np.ones((6_4, 6_4)     ,  dtype=np.floataa				)
      __SCREAMING_SNAKE_CASE       :    Any       =	0

      if str(UpperCamelCase				).startswith("mps"				):
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.manual_seed(UpperCamelCase				)
      else:
       __SCREAMING_SNAKE_CASE       :    int       =	torch.Generator(device=UpperCamelCase				).manual_seed(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{
          "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 	__snake_case    (       self :							List[str]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	"cpu"

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_dummy_components()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.pipeline_class(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	pipe.to(UpperCamelCase				)

      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pipe(**self.get_dummy_inputs(UpperCamelCase				)				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	output.images

      __SCREAMING_SNAKE_CASE       :    str       =	pipe(
          **self.get_dummy_inputs(UpperCamelCase				)     ,  return_dict=UpperCamelCase     ,  )[0]

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_from_tuple[0, -3:, -3:, -1]

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

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

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]				)

      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 	__snake_case    (       self :							Any				)->List[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3				)






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





     def 	__snake_case    (       self :							Tuple				)->str:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






     def 	__snake_case    (       self :							Any				)->Optional[int]:
      __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       :    int       =	load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"				)
      __SCREAMING_SNAKE_CASE       :    str       =	np.ones((7_6_8, 7_6_8)     ,  dtype=np.floataa				)
      __SCREAMING_SNAKE_CASE       :    int       =	0

      __SCREAMING_SNAKE_CASE       :    Any       =	"a hat"

      __SCREAMING_SNAKE_CASE       :    Dict       =	KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"     ,  torch_dtype=torch.floataa				)
      pipe_prior.to(UpperCamelCase				)

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

      __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.Generator(device="cpu"				).manual_seed(0				)
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	pipe_prior(
          UpperCamelCase     ,  generator=UpperCamelCase     ,  num_inference_steps=5     ,  negative_prompt=""     ,  ).to_tuple()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	pipeline(
          UpperCamelCase     ,  image=UpperCamelCase     ,  mask_image=UpperCamelCase     ,  image_embeds=UpperCamelCase     ,  negative_image_embeds=UpperCamelCase     ,  generator=UpperCamelCase     ,  num_inference_steps=1_0_0     ,  height=7_6_8     ,  width=7_6_8     ,  output_type="np"     ,  )

      __SCREAMING_SNAKE_CASE       :    Tuple       =	output.images[0]

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

      assert_mean_pixel_difference(UpperCamelCase     ,  UpperCamelCase				)











from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






