

'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_snake_case         =  {
    'Acehnese Arabic': 'ace_Arab',
    'Acehnese Latin': 'ace_Latn',
    'Mesopotamian Arabic': 'acm_Arab',
    'Ta\'izzi-Adeni Arabic': 'acq_Arab',
    'Tunisian Arabic': 'aeb_Arab',
    'Afrikaans': 'afr_Latn',
    'South Levantine Arabic': 'ajp_Arab',
    'Akan': 'aka_Latn',
    'Amharic': 'amh_Ethi',
    'North Levantine Arabic': 'apc_Arab',
    'Modern Standard Arabic': 'arb_Arab',
    'Modern Standard Arabic Romanized': 'arb_Latn',
    'Najdi Arabic': 'ars_Arab',
    'Moroccan Arabic': 'ary_Arab',
    'Egyptian Arabic': 'arz_Arab',
    'Assamese': 'asm_Beng',
    'Asturian': 'ast_Latn',
    'Awadhi': 'awa_Deva',
    'Central Aymara': 'ayr_Latn',
    'South Azerbaijani': 'azb_Arab',
    'North Azerbaijani': 'azj_Latn',
    'Bashkir': 'bak_Cyrl',
    'Bambara': 'bam_Latn',
    'Balinese': 'ban_Latn',
    'Belarusian': 'bel_Cyrl',
    'Bemba': 'bem_Latn',
    'Bengali': 'ben_Beng',
    'Bhojpuri': 'bho_Deva',
    'Banjar Arabic': 'bjn_Arab',
    'Banjar Latin': 'bjn_Latn',
    'Standard Tibetan': 'bod_Tibt',
    'Bosnian': 'bos_Latn',
    'Buginese': 'bug_Latn',
    'Bulgarian': 'bul_Cyrl',
    'Catalan': 'cat_Latn',
    'Cebuano': 'ceb_Latn',
    'Czech': 'ces_Latn',
    'Chokwe': 'cjk_Latn',
    'Central Kurdish': 'ckb_Arab',
    'Crimean Tatar': 'crh_Latn',
    'Welsh': 'cym_Latn',
    'Danish': 'dan_Latn',
    'German': 'deu_Latn',
    'Southwestern Dinka': 'dik_Latn',
    'Dyula': 'dyu_Latn',
    'Dzongkha': 'dzo_Tibt',
    'Greek': 'ell_Grek',
    'English': 'eng_Latn',
    'Esperanto': 'epo_Latn',
    'Estonian': 'est_Latn',
    'Basque': 'eus_Latn',
    'Ewe': 'ewe_Latn',
    'Faroese': 'fao_Latn',
    'Fijian': 'fij_Latn',
    'Finnish': 'fin_Latn',
    'Fon': 'fon_Latn',
    'French': 'fra_Latn',
    'Friulian': 'fur_Latn',
    'Nigerian Fulfulde': 'fuv_Latn',
    'Scottish Gaelic': 'gla_Latn',
    'Irish': 'gle_Latn',
    'Galician': 'glg_Latn',
    'Guarani': 'grn_Latn',
    'Gujarati': 'guj_Gujr',
    'Haitian Creole': 'hat_Latn',
    'Hausa': 'hau_Latn',
    'Hebrew': 'heb_Hebr',
    'Hindi': 'hin_Deva',
    'Chhattisgarhi': 'hne_Deva',
    'Croatian': 'hrv_Latn',
    'Hungarian': 'hun_Latn',
    'Armenian': 'hye_Armn',
    'Igbo': 'ibo_Latn',
    'Ilocano': 'ilo_Latn',
    'Indonesian': 'ind_Latn',
    'Icelandic': 'isl_Latn',
    'Italian': 'ita_Latn',
    'Javanese': 'jav_Latn',
    'Japanese': 'jpn_Jpan',
    'Kabyle': 'kab_Latn',
    'Jingpho': 'kac_Latn',
    'Kamba': 'kam_Latn',
    'Kannada': 'kan_Knda',
    'Kashmiri Arabic': 'kas_Arab',
    'Kashmiri Devanagari': 'kas_Deva',
    'Georgian': 'kat_Geor',
    'Central Kanuri Arabic': 'knc_Arab',
    'Central Kanuri Latin': 'knc_Latn',
    'Kazakh': 'kaz_Cyrl',
    'Kabiyè': 'kbp_Latn',
    'Kabuverdianu': 'kea_Latn',
    'Khmer': 'khm_Khmr',
    'Kikuyu': 'kik_Latn',
    'Kinyarwanda': 'kin_Latn',
    'Kyrgyz': 'kir_Cyrl',
    'Kimbundu': 'kmb_Latn',
    'Northern Kurdish': 'kmr_Latn',
    'Kikongo': 'kon_Latn',
    'Korean': 'kor_Hang',
    'Lao': 'lao_Laoo',
    'Ligurian': 'lij_Latn',
    'Limburgish': 'lim_Latn',
    'Lingala': 'lin_Latn',
    'Lithuanian': 'lit_Latn',
    'Lombard': 'lmo_Latn',
    'Latgalian': 'ltg_Latn',
    'Luxembourgish': 'ltz_Latn',
    'Luba-Kasai': 'lua_Latn',
    'Ganda': 'lug_Latn',
    'Luo': 'luo_Latn',
    'Mizo': 'lus_Latn',
    'Standard Latvian': 'lvs_Latn',
    'Magahi': 'mag_Deva',
    'Maithili': 'mai_Deva',
    'Malayalam': 'mal_Mlym',
    'Marathi': 'mar_Deva',
    'Minangkabau Arabic ': 'min_Arab',
    'Minangkabau Latin': 'min_Latn',
    'Macedonian': 'mkd_Cyrl',
    'Plateau Malagasy': 'plt_Latn',
    'Maltese': 'mlt_Latn',
    'Meitei Bengali': 'mni_Beng',
    'Halh Mongolian': 'khk_Cyrl',
    'Mossi': 'mos_Latn',
    'Maori': 'mri_Latn',
    'Burmese': 'mya_Mymr',
    'Dutch': 'nld_Latn',
    'Norwegian Nynorsk': 'nno_Latn',
    'Norwegian Bokmål': 'nob_Latn',
    'Nepali': 'npi_Deva',
    'Northern Sotho': 'nso_Latn',
    'Nuer': 'nus_Latn',
    'Nyanja': 'nya_Latn',
    'Occitan': 'oci_Latn',
    'West Central Oromo': 'gaz_Latn',
    'Odia': 'ory_Orya',
    'Pangasinan': 'pag_Latn',
    'Eastern Panjabi': 'pan_Guru',
    'Papiamento': 'pap_Latn',
    'Western Persian': 'pes_Arab',
    'Polish': 'pol_Latn',
    'Portuguese': 'por_Latn',
    'Dari': 'prs_Arab',
    'Southern Pashto': 'pbt_Arab',
    'Ayacucho Quechua': 'quy_Latn',
    'Romanian': 'ron_Latn',
    'Rundi': 'run_Latn',
    'Russian': 'rus_Cyrl',
    'Sango': 'sag_Latn',
    'Sanskrit': 'san_Deva',
    'Santali': 'sat_Olck',
    'Sicilian': 'scn_Latn',
    'Shan': 'shn_Mymr',
    'Sinhala': 'sin_Sinh',
    'Slovak': 'slk_Latn',
    'Slovenian': 'slv_Latn',
    'Samoan': 'smo_Latn',
    'Shona': 'sna_Latn',
    'Sindhi': 'snd_Arab',
    'Somali': 'som_Latn',
    'Southern Sotho': 'sot_Latn',
    'Spanish': 'spa_Latn',
    'Tosk Albanian': 'als_Latn',
    'Sardinian': 'srd_Latn',
    'Serbian': 'srp_Cyrl',
    'Swati': 'ssw_Latn',
    'Sundanese': 'sun_Latn',
    'Swedish': 'swe_Latn',
    'Swahili': 'swh_Latn',
    'Silesian': 'szl_Latn',
    'Tamil': 'tam_Taml',
    'Tatar': 'tat_Cyrl',
    'Telugu': 'tel_Telu',
    'Tajik': 'tgk_Cyrl',
    'Tagalog': 'tgl_Latn',
    'Thai': 'tha_Thai',
    'Tigrinya': 'tir_Ethi',
    'Tamasheq Latin': 'taq_Latn',
    'Tamasheq Tifinagh': 'taq_Tfng',
    'Tok Pisin': 'tpi_Latn',
    'Tswana': 'tsn_Latn',
    'Tsonga': 'tso_Latn',
    'Turkmen': 'tuk_Latn',
    'Tumbuka': 'tum_Latn',
    'Turkish': 'tur_Latn',
    'Twi': 'twi_Latn',
    'Central Atlas Tamazight': 'tzm_Tfng',
    'Uyghur': 'uig_Arab',
    'Ukrainian': 'ukr_Cyrl',
    'Umbundu': 'umb_Latn',
    'Urdu': 'urd_Arab',
    'Northern Uzbek': 'uzn_Latn',
    'Venetian': 'vec_Latn',
    'Vietnamese': 'vie_Latn',
    'Waray': 'war_Latn',
    'Wolof': 'wol_Latn',
    'Xhosa': 'xho_Latn',
    'Eastern Yiddish': 'ydd_Hebr',
    'Yoruba': 'yor_Latn',
    'Yue Chinese': 'yue_Hant',
    'Chinese Simplified': 'zho_Hans',
    'Chinese Traditional': 'zho_Hant',
    'Standard Malay': 'zsm_Latn',
    'Zulu': 'zul_Latn',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							'facebook/nllb-200-distilled-600M'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							(
       'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
       'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
       'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
       'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
   )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							'translator'
   _SCREAMING_SNAKE_CASE   :					str     	=							AutoTokenizer
   _SCREAMING_SNAKE_CASE   :					Dict     	=							AutoModelForSeqaSeqLM
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							LANGUAGE_CODES

   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['text', 'text', 'text']
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							['text']


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if src_lang not in self.lang_to_code:
         raise ValueError(f'''{src_lang} is not a supported language.'''  )
      if tgt_lang not in self.lang_to_code:
         raise ValueError(f'''{tgt_lang} is not a supported language.'''  )
      _lowercase      :				int      					=       self.lang_to_code[src_lang]
      _lowercase      :				int      					=       self.lang_to_code[tgt_lang]
      return self.pre_processor._build_translation_inputs(
          _UpperCamelCase      ,    return_tensors="pt"      ,    src_lang=_UpperCamelCase      ,    tgt_lang=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.model.generate(**_UpperCamelCase  )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.post_processor.decode(outputs[0].tolist()      ,    skip_special_tokens=_UpperCamelCase  )









'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							StableUnCLIPImgaImgPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _SCREAMING_SNAKE_CASE   :					Any     	=							frozenset(
       []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							frozenset([]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       32
      _lowercase      :				Any      					=       embedder_hidden_size

      # image encoding components

      _lowercase      :				Optional[int]      					=       CLIPImageProcessor(crop_size=32      ,    size=32  )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       CLIPVisionModelWithProjection(
          CLIPVisionConfig(
              hidden_size=_UpperCamelCase      ,    projection_dim=_UpperCamelCase      ,    num_hidden_layers=5      ,    num_attention_heads=4      ,    image_size=32      ,    intermediate_size=37      ,    patch_size=1      ,    )  )

      # regular denoising components

      torch.manual_seed(0  )
      _lowercase      :				str      					=       StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
      _lowercase      :				Dict      					=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

      torch.manual_seed(0  )
      _lowercase      :				str      					=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

      torch.manual_seed(0  )
      _lowercase      :				Optional[Any]      					=       CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=_UpperCamelCase      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1E-0_5      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=1000      ,    )  )

      torch.manual_seed(0  )
      _lowercase      :				List[str]      					=       UNetaDConditionModel(
          sample_size=32      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")      ,    up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")      ,    block_out_channels=(32, 64)      ,    attention_head_dim=(2, 4)      ,    class_embed_type="projection"      ,    projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,    cross_attention_dim=_UpperCamelCase      ,    layers_per_block=1      ,    upcast_attention=_UpperCamelCase      ,    use_linear_projection=_UpperCamelCase      ,    )

      torch.manual_seed(0  )
      _lowercase      :				int      					=       DDIMScheduler(
          beta_schedule="scaled_linear"      ,    beta_start=0.0_0_0_8_5      ,    beta_end=0.0_1_2      ,    prediction_type="v_prediction"      ,    set_alpha_to_one=_UpperCamelCase      ,    steps_offset=1      ,    )

      torch.manual_seed(0  )
      _lowercase      :				Dict      					=       AutoencoderKL()

      _lowercase      :				int      					=       {
          # image encoding components
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder.eval(),
          # image noising components
          "image_normalizer": image_normalizer.eval(),
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder.eval(),
          "unet": unet.eval(),
          "scheduler": scheduler,
          "vae": vae.eval(),
      }

      return components


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				List[Any]      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				List[str]      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      if pil_image:
         _lowercase      :				Optional[Any]      					=       input_image * 0.5 + 0.5
         _lowercase      :				Optional[int]      					=       input_image.clamp(0      ,    1  )
         _lowercase      :				str      					=       input_image.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()
         _lowercase      :				int      					=       DiffusionPipeline.numpy_to_pil(_UpperCamelCase  )[0]

      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }


   @skip_mps
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowercase      :				List[str]      					=       self.get_dummy_components()
      _lowercase      :				str      					=       StableUnCLIPImgaImgPipeline(**_UpperCamelCase  )
      _lowercase      :				int      					=       sd_pipe.to(_UpperCamelCase  )
      sd_pipe.set_progress_bar_config(disable=_UpperCamelCase  )

      _lowercase      :				str      					=       self.get_dummy_inputs(_UpperCamelCase  )
      inputs.update({"image_embeds": None}  )
      _lowercase      :				Tuple      					=       sd_pipe(**_UpperCamelCase  ).images
      _lowercase      :				str      					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowercase      :				Tuple      					=       np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       torch_device in ["cpu", "mps"]

      self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )






   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,    reason="XFormers attention is only available with CUDA and `xformers` installed"      ,    )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCamelCase  )




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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				List[str]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-l-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				int      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				List[Any]      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				str      					=       output.images[0]

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

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      _lowercase      :				Any      					=       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"  )

      _lowercase      :				Any      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				Optional[int]      					=       torch.Generator(device="cpu"  ).manual_seed(0  )
      _lowercase      :				Any      					=       pipe(_UpperCamelCase      ,    "anime turle"      ,    generator=_UpperCamelCase      ,    output_type="np"  )

      _lowercase      :				Tuple      					=       output.images[0]

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

      assert_mean_pixel_difference(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"  )

      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _lowercase      :				Optional[Any]      					=       StableUnCLIPImgaImgPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-h-img2img"      ,    torch_dtype=torch.floataa  )
      _lowercase      :				Tuple      					=       pipe.to(_UpperCamelCase  )
      pipe.set_progress_bar_config(disable=_UpperCamelCase  )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _lowercase      :				Optional[int]      					=       pipe(
          _UpperCamelCase      ,    "anime turtle"      ,    num_inference_steps=2      ,    output_type="np"      ,    )

      _lowercase      :				str      					=       torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9






