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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_nllb import NllbTokenizer
else:
      A   :     str       =				None


A   :     int       =				logging.get_logger(__name__)


A   :     Union[str, Any]       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A   :     Optional[Any]       =				{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

A   :     Union[str, Any]       =				{
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
A   :     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"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  ['''input_ids''', '''attention_mask''']
  lowerCamelCase__   =  NllbTokenizer

  lowerCamelCase__   =  []
  lowerCamelCase__   =  []







  def __init__(							self       :				Dict     ,   __magic_name__       :				List[str]=None     ,   __magic_name__       :				List[Any]=None     ,   __magic_name__       :				Dict="<s>"     ,   __magic_name__       :				str="</s>"     ,   __magic_name__       :				Union[str, Any]="</s>"     ,   __magic_name__       :				int="<s>"     ,   __magic_name__       :				str="<unk>"     ,   __magic_name__       :				int="<pad>"     ,   __magic_name__       :				Union[str, Any]="<mask>"     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				Optional[Any]=False     ,   **__magic_name__       :				Optional[Any]     ,   )     ->		Any:
        # Mask token behave like a normal word, i.e. include the space before it
        SCREAMING_SNAKE_CASE_      =     AddedToken(__magic_name__     ,   lstrip=__magic_name__     ,   rstrip=__magic_name__      ) if isinstance(__magic_name__     ,   __magic_name__      ) else mask_token
        SCREAMING_SNAKE_CASE_      =     legacy_behaviour
        super().__init__(
            vocab_file=__magic_name__     ,   tokenizer_file=__magic_name__     ,   bos_token=__magic_name__     ,   eos_token=__magic_name__     ,   sep_token=__magic_name__     ,   cls_token=__magic_name__     ,   unk_token=__magic_name__     ,   pad_token=__magic_name__     ,   mask_token=__magic_name__     ,   src_lang=__magic_name__     ,   tgt_lang=__magic_name__     ,   additional_special_tokens=__magic_name__     ,   legacy_behaviour=__magic_name__     ,   **__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     vocab_file
        SCREAMING_SNAKE_CASE_      =     False if not self.vocab_file else True

        SCREAMING_SNAKE_CASE_      =     FAIRSEQ_LANGUAGE_CODES.copy()

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

        self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}      )
        SCREAMING_SNAKE_CASE_      =     {
            lang_code: self.convert_tokens_to_ids(__magic_name__      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        SCREAMING_SNAKE_CASE_      =     src_lang if src_lang is not None else "eng_Latn"
        SCREAMING_SNAKE_CASE_      =     self.convert_tokens_to_ids(self._src_lang      )
        SCREAMING_SNAKE_CASE_      =     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang      )







  @property
  def       __A   (							self       :				List[str]      )     ->		str:
        return self._src_lang







  @src_lang.setter
  def       __A   (							self       :				int     ,   __magic_name__       :				str      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang      )







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None      )     ->		List[int]:
        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       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None      )     ->		List[int]:
        SCREAMING_SNAKE_CASE_      =     [self.sep_token_id]
        SCREAMING_SNAKE_CASE_      =     [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       __A   (							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[str]     ,   __magic_name__       :				Optional[str]     ,   **__magic_name__       :				Any      )     ->		Optional[Any]:
        if src_lang is None or tgt_lang is None:
              raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"      )
        SCREAMING_SNAKE_CASE_      =     src_lang
        SCREAMING_SNAKE_CASE_      =     self(__magic_name__     ,   add_special_tokens=__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.convert_tokens_to_ids(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tgt_lang_id
        return inputs







  def       __A   (							self       :				str     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				str = "eng_Latn"     ,   __magic_name__       :				Optional[List[str]] = None     ,   __magic_name__       :				str = "fra_Latn"     ,   **__magic_name__       :				Any     ,   )     ->		BatchEncoding:
        SCREAMING_SNAKE_CASE_      =     src_lang
        SCREAMING_SNAKE_CASE_      =     tgt_lang
        return super().prepare_seqaseq_batch(__magic_name__     ,   __magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				int      )     ->		List[Any]:
        return self.set_src_lang_special_tokens(self.src_lang      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        return self.set_tgt_lang_special_tokens(self.tgt_lang      )







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				Optional[int]      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     self.convert_tokens_to_ids(__magic_name__      )

        if self.legacy_behaviour:
              SCREAMING_SNAKE_CASE_      =     []
              SCREAMING_SNAKE_CASE_      =     [self.eos_token_id, self.cur_lang_code]
        else:
              SCREAMING_SNAKE_CASE_      =     [self.cur_lang_code]
              SCREAMING_SNAKE_CASE_      =     [self.eos_token_id]

        SCREAMING_SNAKE_CASE_      =     self.convert_ids_to_tokens(self.prefix_tokens      )
        SCREAMING_SNAKE_CASE_      =     self.convert_ids_to_tokens(self.suffix_tokens      )

        SCREAMING_SNAKE_CASE_      =     processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,   pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,   self.prefix_tokens + self.suffix_tokens      )      )     ,   )







  def       __A   (							self       :				Dict     ,   __magic_name__       :				str      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     self.convert_tokens_to_ids(__magic_name__      )
        if self.legacy_behaviour:
              SCREAMING_SNAKE_CASE_      =     []
              SCREAMING_SNAKE_CASE_      =     [self.eos_token_id, self.cur_lang_code]
        else:
              SCREAMING_SNAKE_CASE_      =     [self.cur_lang_code]
              SCREAMING_SNAKE_CASE_      =     [self.eos_token_id]

        SCREAMING_SNAKE_CASE_      =     self.convert_ids_to_tokens(self.prefix_tokens      )
        SCREAMING_SNAKE_CASE_      =     self.convert_ids_to_tokens(self.suffix_tokens      )

        SCREAMING_SNAKE_CASE_      =     processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str     ,   pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str     ,   special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,   self.prefix_tokens + self.suffix_tokens      )      )     ,   )







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[str] = None      )     ->		Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."      )

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

        if os.path.abspath(self.vocab_file      ) != os.path.abspath(__magic_name__      ):
              copyfile(self.vocab_file     ,   __magic_name__      )

        return (out_vocab_file,)



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "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_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

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

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

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        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()}'''



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







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







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


