
'''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 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							KandinskyVaaInpaintPipeline
   _snake_case		   =							['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _snake_case		   =							[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _snake_case		   =							[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _snake_case		   =							False







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 3_2







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 3_2







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.time_input_dim







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.time_input_dim * 4







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 1_0_0







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      torch.manual_seed(0   )

      UpperCamelCase       =  {
          '''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,
      }

      UpperCamelCase       =  UNetaDConditionModel(**lowerCamelCase__   )
      return model







   @property
   def       UpperCAmelCase				(			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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.dummy_unet
      UpperCamelCase       =  self.dummy_movq

      UpperCamelCase       =  DDIMScheduler(
          num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.00085 ,   beta_end=0.012 ,   clip_sample=lowerCamelCase__ ,   set_alpha_to_one=lowerCamelCase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowerCamelCase__ ,   )

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

      return components







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=0   ):

      '''simple docstring'''







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

      if str(lowerCamelCase__   ).startswith('''mps'''   ):
         UpperCamelCase       =  torch.manual_seed(lowerCamelCase__   )
      else:
         UpperCamelCase       =  torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
      UpperCamelCase       =  {
          '''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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''cpu'''

      UpperCamelCase       =  self.get_dummy_components()

      UpperCamelCase       =  self.pipeline_class(**lowerCamelCase__   )
      UpperCamelCase       =  pipe.to(lowerCamelCase__   )

      pipe.set_progress_bar_config(disable=lowerCamelCase__   )

      UpperCamelCase       =  pipe(**self.get_dummy_inputs(lowerCamelCase__   )   )
      UpperCamelCase       =  output.images

      UpperCamelCase       =  pipe(
          **self.get_dummy_inputs(lowerCamelCase__   ) ,   return_dict=lowerCamelCase__ ,   )[0]

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

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

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

      UpperCamelCase       =  np.array(
          [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]   )

      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   ):

      '''simple docstring'''







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




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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''   )

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

      UpperCamelCase       =  '''a hat'''

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

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

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

      UpperCamelCase       =  pipeline(
          image=lowerCamelCase__ ,   mask_image=lowerCamelCase__ ,   image_embeds=lowerCamelCase__ ,   negative_image_embeds=lowerCamelCase__ ,   generator=lowerCamelCase__ ,   num_inference_steps=1_0_0 ,   height=7_6_8 ,   width=7_6_8 ,   output_type='''np''' ,   )

      UpperCamelCase       =  output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase__ ,   lowerCamelCase__   )




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_ :  Optional[int]     =					logging.get_logger(__name__)

snake_case_ :  int     =					'▁'

snake_case_ :  str     =					{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

snake_case_ :  Optional[Any]     =					{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

snake_case_ :  Dict     =					{'vinai/bartpho-syllable': 1_024}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   sep_token=lowerCamelCase__ ,   cls_token=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   sp_model_kwargs=self.sp_model_kwargs ,   **lowerCamelCase__ ,   )

      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  monolingual_vocab_file
      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )

      # Load the reduced vocab

      # Keep order of special tokens for backward compatibility
      UpperCamelCase       =  {}
      UpperCamelCase       =  0
      for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
            UpperCamelCase       =  cnt
            cnt += 1
      with open(lowerCamelCase__ ,   '''r''' ,   encoding='''utf-8'''   ) as f:
         for line in f.readlines():
            UpperCamelCase       =  line.strip().split()[0]
            UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )
      if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
         UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )

      UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''   ):
         UpperCamelCase       =  {}

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]
      UpperCamelCase       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase__ ,   token_ids_a=lowerCamelCase__ ,   already_has_special_tokens=lowerCamelCase__   )

      if token_ids_a is None:
         return [1] + ([0] * len(lowerCamelCase__   )) + [1]
      return [1] + ([0] * len(lowerCamelCase__   )) + [1, 1] + ([0] * len(lowerCamelCase__   )) + [1]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.fairseq_ids_to_tokens   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      else:
         return self.unk_token_id







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.fairseq_ids_to_tokens[index]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] ,   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(lowerCamelCase__ ,   '''wb'''   ) as fi:
            UpperCamelCase       =  self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase__   )

      if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
          lowerCamelCase__   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(lowerCamelCase__ ,   '''w''' ,   encoding='''utf-8'''   ) as fp:
            for token in self.fairseq_tokens_to_ids:
               if token not in self.all_special_tokens:
                  fp.write(f'{str(lowerCamelCase__   )} \n'   )

      return out_vocab_file, out_monolingual_vocab_file





