

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

import sentencepiece as spm

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


_lowerCamelCase      :					Dict					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Tuple					    =      '''▁'''

_lowerCamelCase      :					Any					    =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase      :					List[str]					    =      {
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCamelCase      :					Dict					    =      {
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
_lowerCamelCase      :					Union[str, Any]					    =      ['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']





class        lowerCAmelCase__  (					SCREAMING_SNAKE_CASE_			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    ["""input_ids""", """attention_mask"""]

       lowercase_						=    []
       lowercase_						=    []

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







             '''simple docstring'''



             __A    =AddedToken(__a ,   lstrip=__a ,   rstrip=__a     ) if isinstance(__a ,   __a     ) else mask_token

             __A    ={} if sp_model_kwargs is None else sp_model_kwargs
             __A    =legacy_behaviour

             super().__init__(
                 bos_token=__a ,   eos_token=__a ,   unk_token=__a ,   sep_token=__a ,   cls_token=__a ,   pad_token=__a ,   mask_token=__a ,   tokenizer_file=__a ,   src_lang=__a ,   tgt_lang=__a ,   additional_special_tokens=__a ,   sp_model_kwargs=self.sp_model_kwargs ,   legacy_behaviour=__a ,   **__a ,   )

             __A    =spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.Load(str(__a     )     )
             __A    =vocab_file
             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
             # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
             # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

             # Mimic fairseq token-to-id alignment for the first 4 token
             __A    ={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             __A    =1

             __A    =len(self.sp_model     )
             __A    ={
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a     )
             }
             __A    ={v: k for k, v in self.lang_code_to_id.items()}
             __A    =len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
             __A    ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
             __A    =list(self.lang_code_to_id.keys()     )

             if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   self._additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in self._additional_special_tokens]     )

             __A    =src_lang if src_lang is not None else 'eng_Latn'
             __A    =self.lang_code_to_id[self._src_lang]
             __A    =tgt_lang
             self.set_src_lang_special_tokens(self._src_lang     )

       def __getstate__(							self     ):







             '''simple docstring'''



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

       def __setstate__(							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =d

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

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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._src_lang

       @src_lang.setter
       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =new_src_lang
             self.set_src_lang_special_tokens(self._src_lang     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



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

             __A    =[1] * len(self.prefix_tokens     )
             __A    =[1] * len(self.suffix_tokens     )
             if token_ids_a is None:
                   return prefix_ones + ([0] * len(__a     )) + suffix_ones
             return prefix_ones + ([0] * len(__a     )) + ([0] * len(__a     )) + suffix_ones

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[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 ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             if src_lang is None or tgt_lang is None:
                   raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''     )
             __A    =src_lang
             __A    =self(__a ,   add_special_tokens=__a ,   return_tensors=__a ,   **__a     )
             __A    =self.convert_tokens_to_ids(__a     )
             __A    =tgt_lang_id
             return inputs

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             __A    =self.sp_model.PieceToId(__a     )

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =''.join(__a     ).replace(__a ,   ''' '''     ).strip()
             return out_string

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



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

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

             return (out_vocab_file,)

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = "eng_Latn" ,   lowercase__ = None ,   lowercase__ = "fra_Latn" ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =src_lang
             __A    =tgt_lang
             return super().prepare_seqaseq_batch(__a ,   __a ,   **__a     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.set_src_lang_special_tokens(self.src_lang     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.set_tgt_lang_special_tokens(self.tgt_lang     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.lang_code_to_id[src_lang]
             if self.legacy_behaviour:
                   __A    =[]
                   __A    =[self.eos_token_id, self.cur_lang_code]
             else:
                   __A    =[self.cur_lang_code]
                   __A    =[self.eos_token_id]


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.lang_code_to_id[lang]
             if self.legacy_behaviour:
                   __A    =[]
                   __A    =[self.eos_token_id, self.cur_lang_code]
             else:
                   __A    =[self.cur_lang_code]
                   __A    =[self.eos_token_id]







'''simple docstring'''





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  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyInpaintPipeline
 A__				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							Dict  )      ->   Any:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							int  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
       return tokenizer
 @property
 def A_   (   self						:							Tuple  )      ->   str:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    MCLIPConfig(
           numDims=self.cross_attention_dim      ,     transformerDimensions=self.text_embedder_hidden_size      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     vocab_size=1005      ,     )

       __snake_case :   List[Any]													=    MultilingualCLIP(__a  )
       __snake_case :   List[str]													=    text_encoder.eval()

       return text_encoder
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Tuple													=    {
           '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,
       }

       __snake_case :   Any													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       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 A_   (   self						:							int  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   Optional[int]													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.dummy_text_encoder
       __snake_case :   str													=    self.dummy_tokenizer
       __snake_case :   Any													=    self.dummy_unet
       __snake_case :   Optional[Any]													=    self.dummy_movq

       __snake_case :   Optional[Any]													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   List[str]													=    {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							List[str]      ,     __a						:							Dict      ,     __a						:							int=0  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   str													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(seed + 1  )  ).to(__a  )
       # create init_image
       __snake_case :   Any													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   Optional[int]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   List[str]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[Any]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Any													=    torch.manual_seed(__a  )
       else:
             __snake_case :   Optional[int]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   int													=    {
           'prompt': 'horse',
           '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 A_   (   self						:							Any  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[str]													=    'cpu'

       __snake_case :   Tuple													=    self.get_dummy_components()

       __snake_case :   Optional[int]													=    self.pipeline_class(**__a  )
       __snake_case :   Union[str, Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Dict													=    output.images

       __snake_case :   Union[str, Any]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   int													=    image[0, -3:, -3:, -1]
       __snake_case :   Optional[int]													=    image_from_tuple[0, -3:, -3:, -1]

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

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

       __snake_case :   List[Any]													=    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 A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Tuple													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   Tuple													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Tuple													=    0

       __snake_case :   Tuple													=    'a hat'

       __snake_case :   Optional[Any]													=    KandinskyPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   int													=    KandinskyInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Any													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Union[str, Any]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           __a      ,     image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   int													=    output.images[0]

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

       assert_mean_pixel_difference(__a      ,     __a  )



