


"""simple docstring"""







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 __lowercase    					=      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowercase    					=      128022
__lowercase    					=      128028






@require_sentencepiece
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       MaMaaaTokenizer
    UpperCAmelCase       :							List[str]	=       False
    UpperCAmelCase       :							Tuple	=       False
    UpperCAmelCase       :							str	=       True






    def 	__snake_case							(					self				:    int):
      super().setUp()

      a						:		Any      							=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
      a						:		Any      							=			dict(zip(__UpperCAmelCase				, range(len(__UpperCAmelCase))))
      a						:		List[str]      							=			Path(self.tmpdirname)
      save_json(__UpperCAmelCase				, save_dir / VOCAB_FILES_NAMES["vocab_file"])
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
        copyfile(__UpperCAmelCase				, save_dir / VOCAB_FILES_NAMES["spm_file"])

      a						:		Tuple      							=			MaMaaaTokenizer.from_pretrained(self.tmpdirname)
      tokenizer.save_pretrained(self.tmpdirname)






    def 	__snake_case							(					self				:    str				, **__UpperCAmelCase				:    str):
      return MaMaaaTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    List[Any]):
      return (
          "This is a test",
          "This is a test",
      )






    def 	__snake_case							(					self				:    Tuple):
      a						:		int      							=			"</s>"
      a						:		Dict      							=			0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase)				, __UpperCAmelCase)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase)				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Tuple      							=			self.get_tokenizer()
      a						:		Optional[Any]      							=			list(tokenizer.get_vocab().keys())

      self.assertEqual(vocab_keys[0]				, "</s>")
      self.assertEqual(vocab_keys[1]				, "<unk>")
      self.assertEqual(vocab_keys[-1]				, "<s>")
      self.assertEqual(len(__UpperCAmelCase)				, tokenizer.vocab_size + len(tokenizer.get_added_vocab()))






    @unittest.skip("Skip this test while all models are still to be uploaded.")
    def 	__snake_case							(					self				:    str):
      pass






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			self.get_tokenizer()

      a						:		Dict      							=			tokenizer.tokenize("This is a test")
      self.assertListEqual(__UpperCAmelCase				, ["▁This", "▁is", "▁a", "▁t", "est"])

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [2, 3, 4, 5, 6]				, )

      a						:		str      							=			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6])
      self.assertListEqual(__UpperCAmelCase				, ["▁This", "▁is", "▁a", "▁t", "est"])

      a						:		Optional[Any]      							=			tokenizer.convert_tokens_to_string(__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, "This is a test")






    @slow
    def 	__snake_case							(					self				:    Tuple):
      # fmt: off
      a						:		Optional[int]      							=			{"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase				, model_name="facebook/m2m100_418M"				, revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"				, )






@require_torch
@require_sentencepiece
@require_tokenizers
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Union[str, Any]	=       """facebook/m2m100_418M"""
    UpperCAmelCase       :							Union[str, Any]	=       [
        """In my opinion, there are two levels of response from the French government.""",
        """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
    ]
    UpperCAmelCase       :							Dict	=       [
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
    ]

    # fmt: off
    UpperCAmelCase       :							List[str]	=       [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]






    @classmethod
    def 	__snake_case							(					cls				:    List[str]):
      a						:		MaMaaaTokenizer      							=			MaMaaaTokenizer.from_pretrained(
          cls.checkpoint_name				, src_lang="en"				, tgt_lang="fr")
      a						:		List[str]      							=			1
      return cls






    def 	__snake_case							(					self				:    Union[str, Any]):
      self.assertEqual(self.tokenizer.get_lang_id("ar")				, 128006)
      self.assertEqual(self.tokenizer.get_lang_id("en")				, 128022)
      self.assertEqual(self.tokenizer.get_lang_id("ro")				, 128076)
      self.assertEqual(self.tokenizer.get_lang_id("mr")				, 128063)






    def 	__snake_case							(					self				:    Tuple):
      a						:		Union[str, Any]      							=			self.tokenizer.get_vocab()
      self.assertEqual(len(__UpperCAmelCase)				, self.tokenizer.vocab_size)
      self.assertEqual(vocab["<unk>"]				, 3)
      self.assertIn(self.tokenizer.get_lang_token("en")				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[Any]):
      a						:		int      							=			"en"
      a						:		int      							=			self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
      self.assertListEqual(self.expected_src_tokens				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Any):
      self.assertIn(__UpperCAmelCase				, self.tokenizer.all_special_ids)
      # fmt: off
      a						:		List[Any]      							=			[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
      # fmt: on
      a						:		str      							=			self.tokenizer.decode(__UpperCAmelCase				, skip_special_tokens=__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.tokenizer.decode(generated_ids[1:]				, skip_special_tokens=__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)
      self.assertNotIn(self.tokenizer.eos_token				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			tempfile.mkdtemp()
      a						:		Union[str, Any]      							=			self.tokenizer.lang_token_to_id
      self.tokenizer.save_pretrained(__UpperCAmelCase)
      a						:		int      							=			MaMaaaTokenizer.from_pretrained(__UpperCAmelCase)
      self.assertDictEqual(new_tok.lang_token_to_id				, __UpperCAmelCase)






    @require_torch
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Dict      							=			"en"
      a						:		List[str]      							=			"fr"

      a						:		str      							=			self.tokenizer(self.src_text				, text_target=self.tgt_text				, padding=__UpperCAmelCase				, return_tensors="pt")

      a						:		Any      							=			shift_tokens_right(
          batch["labels"]				, self.tokenizer.pad_token_id				, self.tokenizer.eos_token_id)

      for k in batch:
        a						:		str      							=			batch[k].tolist()
      # batch = {k: v.tolist() for k,v in batch.items()}
      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      # batch.decoder_inputs_ids[0][0] ==
      assert batch.input_ids[1][0] == EN_CODE
      assert batch.input_ids[1][-1] == 2
      assert batch.labels[1][0] == FR_CODE
      assert batch.labels[1][-1] == 2
      assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






    @require_torch
    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			"mr"
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("mr")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])

      a						:		List[Any]      							=			"zh"
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("zh")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])






    @require_torch
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			"mr"
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("mr")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])

      a						:		List[Any]      							=			"zh"
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("zh")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])






    @require_torch
    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			self.tokenizer._build_translation_inputs("A test"				, return_tensors="pt"				, src_lang="en"				, tgt_lang="ar")

      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, {
              # en_XX, A, test, EOS
              "input_ids": [[128022, 58, 4183, 2]],
              "attention_mask": [[1, 1, 1, 1]],
              # ar_AR
              "forced_bos_token_id": 128006,
          }				, )










"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						_A  (  _a    ,_a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       StableDiffusionInpaintPipeline
    UpperCAmelCase       :							Optional[Any]	=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase       :							Any	=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase       :							Union[str, Any]	=       frozenset(
        []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCAmelCase       :							int	=       frozenset([]  )






    def 	__snake_case							(					self				:    Dict):
      torch.manual_seed(0)
      a						:		List[Any]      							=			UNetaDConditionModel(
          block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				, cross_attention_dim=32				, attention_head_dim=(2, 4)				, use_linear_projection=__UpperCAmelCase				, )
      a						:		Tuple      							=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      torch.manual_seed(0)
      a						:		str      							=			AutoencoderKL(
          block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				, latent_channels=4				, sample_size=128				, )
      torch.manual_seed(0)
      a						:		List[str]      							=			CLIPTextConfig(
          bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="gelu"				, projection_dim=512				, )
      a						:		Any      							=			CLIPTextModel(__UpperCAmelCase)
      a						:		List[str]      							=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }
      return components






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Union[str, Any]=0):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      a						:		Dict      							=			floats_tensor((1, 3, 32, 32)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			image.cpu().permute(0				, 2				, 3				, 1)[0]
      a						:		Union[str, Any]      							=			Image.fromarray(np.uinta(__UpperCAmelCase)).convert("RGB").resize((64, 64))
      a						:		Dict      							=			Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Tuple      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		Tuple      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		Optional[Any]      							=			{
          "prompt": "A painting of a squirrel eating a burger",
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs






    def 	__snake_case							(					self				:    List[str]):
      a						:		Union[str, Any]      							=			"cpu"  # ensure determinism for the device-dependent torch.Generator
      a						:		Tuple      							=			self.get_dummy_components()
      a						:		Optional[int]      							=			StableDiffusionInpaintPipeline(**__UpperCAmelCase)
      a						:		int      							=			sd_pipe.to(__UpperCAmelCase)
      sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Any      							=			self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[int]      							=			sd_pipe(**__UpperCAmelCase).images
      a						:		Optional[int]      							=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a						:		int      							=			np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






    def 	__snake_case							(					self				:    str):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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







    """simple docstring"""




    def 	__snake_case							(					self				:    Union[str, Any]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench.npy")

      a						:		Tuple      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[Any]      							=			StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase				, safety_checker=__UpperCAmelCase)
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Any      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		str      							=			torch.manual_seed(0)
      a						:		Union[str, Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		List[str]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 9e-3






    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		str      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
          "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Any      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=__UpperCAmelCase				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Dict      							=			torch.manual_seed(0)
      a						:		List[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		Optional[Any]      							=			output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image).max() < 5e-1






    def 	__snake_case							(					self				:    int):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a						:		Tuple      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		Optional[int]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[int]      							=			PNDMScheduler.from_pretrained(__UpperCAmelCase				, subfolder="scheduler")
      a						:		int      							=			StableDiffusionInpaintPipeline.from_pretrained(
          __UpperCAmelCase				, safety_checker=__UpperCAmelCase				, scheduler=__UpperCAmelCase				, torch_dtype=torch.floataa				, )
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing(1)
      pipe.enable_sequential_cpu_offload()

      a						:		Optional[int]      							=			"Face of a yellow cat, high resolution, sitting on a park bench"

      a						:		Optional[int]      							=			torch.manual_seed(0)
      a						:		str      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=2				, output_type="np"				, )

      a						:		int      							=			torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






