






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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		):
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= KandinskyVaaInpaintPipeline
    SCREAMING_SNAKE_CASE						:     List[str]						= ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    SCREAMING_SNAKE_CASE						:     str						= [
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    SCREAMING_SNAKE_CASE						:     Tuple						= [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE						:     List[str]						= False






    @property
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		List[str]:
      return 32






    @property
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Dict:
      return 32






    @property
    def    UpperCamelCase       (       self     :							str					)     ->		Optional[Any]:
      return self.time_input_dim






    @property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Union[str, Any]:
      return self.time_input_dim * 4






    @property
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Tuple:
      return 100






    @property
    def    UpperCamelCase       (       self     :							Dict					)     ->		int:
      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': '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': '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(**__SCREAMING_SNAKE_CASE					)
      return model






    @property
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Optional[int]:
      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    UpperCamelCase       (       self     :							Tuple					)     ->		Tuple:
      torch.manual_seed(0					)
      lowerCamelCase_         =						VQModel(**self.dummy_movq_kwargs					)
      return model






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						self.dummy_unet
      lowerCamelCase_         =						self.dummy_movq

      lowerCamelCase_         =						DDIMScheduler(
          num_train_timesteps=1000       ,						beta_schedule='linear'       ,						beta_start=0.00_085       ,						beta_end=0.012       ,						clip_sample=__SCREAMING_SNAKE_CASE       ,						set_alpha_to_one=__SCREAMING_SNAKE_CASE       ,						steps_offset=1       ,						prediction_type='epsilon'       ,						thresholding=__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						{
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=0					)     ->		Dict:
      lowerCamelCase_         =						floats_tensor((1, self.text_embedder_hidden_size)       ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						floats_tensor((1, self.text_embedder_hidden_size)       ,						rng=random.Random(seed + 1					)					).to(
          __SCREAMING_SNAKE_CASE					)
      # create init_image
      lowerCamelCase_         =						floats_tensor((1, 3, 64, 64)       ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						image.cpu().permute(0       ,						2       ,						3       ,						1					)[0]
      lowerCamelCase_         =						Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE					)					).convert('RGB'					).resize((256, 256)					)
      # create mask
      lowerCamelCase_         =						np.ones((64, 64)       ,						dtype=np.floataa					)
      lowerCamelCase_         =						0

      if str(__SCREAMING_SNAKE_CASE					).startswith('mps'					):
        lowerCamelCase_         =						torch.manual_seed(__SCREAMING_SNAKE_CASE					)
      else:
        lowerCamelCase_         =						torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						{
          '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    UpperCamelCase       (       self     :							List[Any]					)     ->		int:
      lowerCamelCase_         =						'cpu'

      lowerCamelCase_         =						self.get_dummy_components()

      lowerCamelCase_         =						self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						pipe.to(__SCREAMING_SNAKE_CASE					)

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						output.images

      lowerCamelCase_         =						pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						)[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, 64, 64, 3)

      lowerCamelCase_         =						np.array(
          [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]					)

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






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






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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Optional[int]:
      lowerCamelCase_         =						load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_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((768, 768)       ,						dtype=np.floataa					)
      lowerCamelCase_         =						0

      lowerCamelCase_         =						'a hat'

      lowerCamelCase_         =						KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'       ,						torch_dtype=torch.floataa					)
      pipe_prior.to(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						KandinskyVaaInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,						torch_dtype=torch.floataa					)
      lowerCamelCase_         =						pipeline.to(__SCREAMING_SNAKE_CASE					)
      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						torch.Generator(device='cpu'					).manual_seed(0					)
      lowerCamelCase_     ,	lowerCamelCase_         =						pipe_prior(
          __SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=5       ,						negative_prompt=''       ,						).to_tuple()

      lowerCamelCase_         =						pipeline(
          image=__SCREAMING_SNAKE_CASE       ,						mask_image=__SCREAMING_SNAKE_CASE       ,						image_embeds=__SCREAMING_SNAKE_CASE       ,						negative_image_embeds=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=100       ,						height=768       ,						width=768       ,						output_type='np'       ,						)

      lowerCamelCase_         =						output.images[0]

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

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)












"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class      a						(					__snake_case		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str = "▁"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<unk>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "</s>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<pad>"       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						{
          'pad': {'id': 0, 'token': pad_token},
          'eos': {'id': 1, 'token': eos_token},
          'unk': {'id': 2, 'token': unk_token},
      }

      lowerCamelCase_         =						[None] * len(self.special_tokens					)
      for token_dict in self.special_tokens.values():
        lowerCamelCase_         =						token_dict['token']

      lowerCamelCase_         =						Tokenizer(Unigram()					)

      lowerCamelCase_         =						normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(' {2,}'					)       ,						' '					),
              normalizers.Lowercase(),
          ]					)
      lowerCamelCase_         =						pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Punctuation(),
          ]					)
      lowerCamelCase_         =						decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						TemplateProcessing(
          single=F'''$A {self.special_tokens["eos"]["token"]}'''       ,						special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]       ,						)

      lowerCamelCase_         =						{
          'model': 'SentencePieceUnigram',
          'replacement': replacement,
          'add_prefix_space': add_prefix_space,
      }

      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		Tuple:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						[files]
      self._tokenizer.train(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[Iterator[str], Iterator[Iterator[str]]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		str:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Dict:
      lowerCamelCase_         =						json.loads(self._tokenizer.to_str()					)

      lowerCamelCase_         =						self.special_tokens['unk']['id']

      lowerCamelCase_         =						Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE					)					)




