

'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


A_											=							logging.get_logger(__name__)


A_											=							{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

A_											=							{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

A_											=							{
    "allenai/longformer-base-4096": 40_96,
    "allenai/longformer-large-4096": 40_96,
    "allenai/longformer-large-4096-finetuned-triviaqa": 40_96,
    "allenai/longformer-base-4096-extra.pos.embd.only": 40_96,
    "allenai/longformer-large-4096-extra.pos.embd.only": 40_96,
}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    A_  ( ):
  SCREAMING_SNAKE_CASE:Any						      =					(
      list(range(ord("!" )      ,       ord("~" ) + 1 ) ) + list(range(ord("¡" )      ,       ord("¬" ) + 1 ) ) + list(range(ord("®" )      ,       ord("ÿ" ) + 1 ) )
  )
  SCREAMING_SNAKE_CASE:Tuple						      =					bs[:]
  SCREAMING_SNAKE_CASE:int						      =					0
  for b in range(2**8 ):
    if b not in bs:
      bs.append(snake_case )
      cs.append(2**8 + n )
      n += 1
  SCREAMING_SNAKE_CASE:str						      =					[chr(snake_case ) for n in cs]
  return dict(zip(snake_case      ,       snake_case ) )





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					set()
  SCREAMING_SNAKE_CASE:List[str]						      =					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char) )
    SCREAMING_SNAKE_CASE:int						      =					char
  return pairs





class 							_snake_case (       _a						):
   _A       :     List[str]       =					VOCAB_FILES_NAMES
   _A       :     Union[str, Any]       =					PRETRAINED_VOCAB_FILES_MAP
   _A       :     Optional[Any]       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A       :     Optional[int]       =					['''input_ids''', '''attention_mask''']
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="replace"     ,SCREAMING_SNAKE_CASE__			:      str="<s>"     ,SCREAMING_SNAKE_CASE__			:      int="</s>"     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="</s>"     ,SCREAMING_SNAKE_CASE__			:      int="<s>"     ,SCREAMING_SNAKE_CASE__			:      int="<unk>"     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="<pad>"     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="<mask>"     ,SCREAMING_SNAKE_CASE__			:      Dict=False     ,**SCREAMING_SNAKE_CASE__			:      str     ,):
     SCREAMING_SNAKE_CASE:Dict						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else bos_token
     SCREAMING_SNAKE_CASE:Dict						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else eos_token
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else sep_token
     SCREAMING_SNAKE_CASE:Optional[int]						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else cls_token
     SCREAMING_SNAKE_CASE:List[Any]						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else unk_token
     SCREAMING_SNAKE_CASE:int						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE:str						      =					AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) else mask_token

     super().__init__(
         errors=SCREAMING_SNAKE_CASE__     ,bos_token=SCREAMING_SNAKE_CASE__     ,eos_token=SCREAMING_SNAKE_CASE__     ,unk_token=SCREAMING_SNAKE_CASE__     ,sep_token=SCREAMING_SNAKE_CASE__     ,cls_token=SCREAMING_SNAKE_CASE__     ,pad_token=SCREAMING_SNAKE_CASE__     ,mask_token=SCREAMING_SNAKE_CASE__     ,add_prefix_space=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)

     with open(SCREAMING_SNAKE_CASE__     ,encoding="utf-8"							) as vocab_handle:
       SCREAMING_SNAKE_CASE:Tuple						      =					json.load(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					{v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					errors  # how to handle errors in decoding
     SCREAMING_SNAKE_CASE:Dict						      =					bytes_to_unicode()
     SCREAMING_SNAKE_CASE:Tuple						      =					{v: k for k, v in self.byte_encoder.items()}
     with open(SCREAMING_SNAKE_CASE__     ,encoding="utf-8"							) as merges_handle:
       SCREAMING_SNAKE_CASE:Dict						      =					merges_handle.read().split("\n"							)[1:-1]
     SCREAMING_SNAKE_CASE:Tuple						      =					[tuple(merge.split()							) for merge in bpe_merges]
     SCREAMING_SNAKE_CASE:Any						      =					dict(zip(SCREAMING_SNAKE_CASE__     ,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
     SCREAMING_SNAKE_CASE:Tuple						      =					{}
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     SCREAMING_SNAKE_CASE:List[str]						      =					re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)
   @property
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return len(self.encoder							)
   def     __UpperCamelCase						( self			:      int							):
     return dict(self.encoder     ,**self.added_tokens_encoder							)
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     if token in self.cache:
       return self.cache[token]
     SCREAMING_SNAKE_CASE:List[str]						      =					tuple(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_pairs(SCREAMING_SNAKE_CASE__							)

     if not pairs:
       return token

     while True:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					min(SCREAMING_SNAKE_CASE__     ,key=lambda SCREAMING_SNAKE_CASE__							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__     ,float("inf"							)							)							)
       if bigram not in self.bpe_ranks:
         break
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					bigram
       SCREAMING_SNAKE_CASE:Any						      =					[]
       SCREAMING_SNAKE_CASE:List[str]						      =					0
       while i < len(SCREAMING_SNAKE_CASE__							):
         try:
           SCREAMING_SNAKE_CASE:List[str]						      =					word.index(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
         except ValueError:
           new_word.extend(word[i:]							)
           break
         else:
           new_word.extend(word[i:j]							)
           SCREAMING_SNAKE_CASE:List[Any]						      =					j

         if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
           new_word.append(first + second							)
           i += 2
         else:
           new_word.append(word[i]							)
           i += 1
       SCREAMING_SNAKE_CASE:Any						      =					tuple(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Any						      =					new_word
       if len(SCREAMING_SNAKE_CASE__							) == 1:
         break
       else:
         SCREAMING_SNAKE_CASE:Any						      =					get_pairs(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					" ".join(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					word
     return word
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Any						      =					[]
     for token in re.findall(self.pat     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					"".join(
           self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__							).split(" "							)							)
     return bpe_tokens
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return self.encoder.get(SCREAMING_SNAKE_CASE__     ,self.encoder.get(self.unk_token							)							)
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     return self.decoder.get(SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Dict						      =					"".join(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:List[str]						      =					bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"     ,errors=self.errors							)
     return text
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[str] = None							):
     if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
       return
     SCREAMING_SNAKE_CASE:Dict						      =					os.path.join(
         SCREAMING_SNAKE_CASE__     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
     SCREAMING_SNAKE_CASE:Any						      =					os.path.join(
         SCREAMING_SNAKE_CASE__     ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

     with open(SCREAMING_SNAKE_CASE__     ,"w"     ,encoding="utf-8"							) as f:
       f.write(json.dumps(self.encoder     ,indent=2     ,sort_keys=SCREAMING_SNAKE_CASE__     ,ensure_ascii=SCREAMING_SNAKE_CASE__							) + "\n"							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					0
     with open(SCREAMING_SNAKE_CASE__     ,"w"     ,encoding="utf-8"							) as writer:
       writer.write("#version: 0.2\n"							)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,key=lambda SCREAMING_SNAKE_CASE__							: kv[1]							):
         if index != token_index:
           logger.warning(
               F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
               " Please check that the tokenizer is not corrupted!"							)
           SCREAMING_SNAKE_CASE:List[str]						      =					token_index
         writer.write(" ".join(SCREAMING_SNAKE_CASE__							) + "\n"							)
         index += 1

     return vocab_file, merge_file
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     SCREAMING_SNAKE_CASE:Tuple						      =					[self.cls_token_id]
     SCREAMING_SNAKE_CASE:List[str]						      =					[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None     ,SCREAMING_SNAKE_CASE__			:      bool = False							):
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=SCREAMING_SNAKE_CASE__     ,token_ids_a=SCREAMING_SNAKE_CASE__     ,already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

     if token_ids_a is None:
       return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
     return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:Any						      =					[self.sep_token_id]
     SCREAMING_SNAKE_CASE:Any						      =					[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]







   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Any=False     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     SCREAMING_SNAKE_CASE:int						      =					kwargs.pop("add_prefix_space"     ,self.add_prefix_space							)
     if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
       SCREAMING_SNAKE_CASE:Tuple						      =					" " + text
     return (text, kwargs)









'''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 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     str       =					KandinskyVaaInpaintPipeline
   _A       :     Tuple       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _A       :     List[str]       =					[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _A       :     Optional[Any]       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _A       :     int       =					False
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return self.time_input_dim
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return self.time_input_dim * 4
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 100
   @property
   def     __UpperCamelCase						( self			:      Any							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "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,
     }

     SCREAMING_SNAKE_CASE:Dict						      =					UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
     return model
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     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			:      int							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					VQModel(**self.dummy_movq_kwargs							)
     return model
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_unet
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.dummy_movq

     SCREAMING_SNAKE_CASE:Any						      =					DDIMScheduler(
         num_train_timesteps=1_000     ,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__     ,)

     SCREAMING_SNAKE_CASE:Any						      =					{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=0							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					floats_tensor((1, self.text_embedder_hidden_size)     ,rng=random.Random(seed + 1							)							).to(
         SCREAMING_SNAKE_CASE__							)
     # create init_image
     SCREAMING_SNAKE_CASE:Any						      =					floats_tensor((1, 3, 64, 64)     ,rng=random.Random(SCREAMING_SNAKE_CASE__							)							).to(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image.cpu().permute(0     ,2     ,3     ,1							)[0]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__							)							).convert("RGB"							).resize((256, 256)							)
     # create mask
     SCREAMING_SNAKE_CASE:int						      =					np.ones((64, 64)     ,dtype=np.floataa							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					0

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Tuple						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					{
         "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			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					"cpu"

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_dummy_components()

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images

     SCREAMING_SNAKE_CASE:int						      =					pipe(
         **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_from_tuple[0, -3:, -3:, -1]

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

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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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			:      Any							):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Any							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"							)

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

     SCREAMING_SNAKE_CASE:Optional[int]						      =					"a hat"

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"     ,torch_dtype=torch.floataa							)
     pipe_prior.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaInpaintPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder-inpaint"     ,torch_dtype=torch.floataa							)
     SCREAMING_SNAKE_CASE:List[str]						      =					pipeline.to(SCREAMING_SNAKE_CASE__							)
     pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device="cpu"							).manual_seed(0							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					pipe_prior(
         SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=5     ,negative_prompt=""     ,).to_tuple()

     SCREAMING_SNAKE_CASE:Dict						      =					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"     ,)

     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images[0]

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

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)






