

'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase  :		str  =		get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
     import sentencepiece as sp


_UpperCAmelCase  :		Optional[int]  =		5
_UpperCAmelCase  :		Any  =		10

@require_sentencepiece
@require_tokenizers
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 UpperCamelCase__     =       SpeechaTextTokenizer
 UpperCamelCase__     =       False
 UpperCamelCase__     =       True







 def 						_A(       self   ):
   super().setUp()

   lowercase									=sp.SentencePieceProcessor()
   spm_model.Load(snake_case_   )
   lowercase									=['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

   vocab += [spm_model.IdToPiece(id_   ) for id_ in range(len(snake_case_   )   )]
   lowercase									=dict(zip(snake_case_  , range(len(snake_case_   )   )   )   )

   lowercase									=Path(self.tmpdirname   )
   save_json(snake_case_  , save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
   if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
     copyfile(snake_case_  , save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

   lowercase									=SpeechaTextTokenizer.from_pretrained(self.tmpdirname   )
   tokenizer.save_pretrained(self.tmpdirname   )







 def 						_A(       self   ):
   lowercase									='''<pad>'''
   lowercase									=1

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







 def 						_A(       self   ):
   lowercase									=list(self.get_tokenizer().get_vocab().keys()   )

   self.assertEqual(vocab_keys[0]  , '''<s>'''   )
   self.assertEqual(vocab_keys[1]  , '''<pad>'''   )
   self.assertEqual(vocab_keys[-1]  , '''j'''   )
   self.assertEqual(len(snake_case_   )  , 10_01   )







 def 						_A(       self   ):
   self.assertEqual(self.get_tokenizer().vocab_size  , 10_01   )







 def 						_A(       self   ):
   lowercase									=SpeechaTextTokenizer.from_pretrained(self.tmpdirname   )

   lowercase									=tokenizer.tokenize('''This is a test'''   )
   self.assertListEqual(snake_case_  , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(snake_case_   )  , [2_89, 50, 14, 1_74, 3_86]  , )

   lowercase									=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
   self.assertListEqual(
       snake_case_  , [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']  , )
   lowercase									=tokenizer.convert_tokens_to_ids(snake_case_   )
   self.assertListEqual(snake_case_  , [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]   )

   lowercase									=tokenizer.convert_ids_to_tokens(snake_case_   )
   self.assertListEqual(
       snake_case_  , [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']  , )







 @slow
 def 						_A(       self   ):
   # fmt: off
   lowercase									={'''input_ids''': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 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, 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, 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, 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, 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, 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=snake_case_  , model_name='''facebook/s2t-small-mustc-en-de-st'''  , revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''  , )

@require_sentencepiece
class 						__magic_name__   (							unittest.TestCase ):
 UpperCamelCase__     =       'valhalla/s2t_mustc_multilinguial_medium'

 UpperCamelCase__     =       'C\'est trop cool'
 UpperCamelCase__     =       'Esto es genial'







 @classmethod
 def 						_A(       cls   ):
   lowercase									=SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name   )
   return cls







 def 						_A(       self   ):
   self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']  , 4   )
   self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']  , 6   )
   self.assertEqual(self.tokenizer.lang_code_to_id['''it''']  , 9   )
   self.assertEqual(self.tokenizer.lang_code_to_id['''de''']  , 11   )







 def 						_A(       self   ):
   self.assertEqual(self.tokenizer.vocab_size  , 1_00_00   )







 def 						_A(       self   ):
   self.assertIn(snake_case_  , self.tokenizer.all_special_ids   )
   lowercase									=[ES_CODE, 4, 16_01, 47, 76_47, 2]
   lowercase									=self.tokenizer.decode(snake_case_  , skip_special_tokens=snake_case_   )
   lowercase									=self.tokenizer.decode(generated_ids[1:]  , skip_special_tokens=snake_case_   )
   self.assertEqual(snake_case_  , snake_case_   )
   self.assertNotIn(self.tokenizer.eos_token  , snake_case_   )







 def 						_A(       self   ):
   lowercase									='''fr'''
   lowercase									=self.tokenizer(self.french_text   ).input_ids
   self.assertEqual(encoded[0]  , snake_case_   )
   self.assertEqual(encoded[-1]  , self.tokenizer.eos_token_id   )







 def 						_A(       self   ):
   lowercase									='''fr'''
   self.assertListEqual(self.tokenizer.prefix_tokens  , [FR_CODE]   )

   lowercase									='''es'''
   self.assertListEqual(self.tokenizer.prefix_tokens  , [ES_CODE]   )


'''simple docstring'''

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

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


enable_full_determinism()




class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				int	=			StableDiffusionDiffEditPipeline
 A_				:				List[Any]	=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
 A_				:				str	=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
 A_				:				Dict	=			frozenset(
     []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 A_				:				Union[str, Any]	=			frozenset([]  )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   str     		=	UNetaDConditionModel(
      block_out_channels=(32, 64)			,							layers_per_block=2			,							sample_size=32			,							in_channels=4			,							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=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_one=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMInverseScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_zero=a__			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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    )
  lowerCAmelCase__     :   Dict     		=	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			,							)
  lowerCAmelCase__     :   Tuple     		=	CLIPTextModel(a__    )
  lowerCAmelCase__     :   int     		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

  lowerCAmelCase__     :   List[Any]     		=	{
      "unet": unet,
      "scheduler": scheduler,
      "inverse_scheduler": inverse_scheduler,
      "vae": vae,
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "safety_checker": None,
      "feature_extractor": None,
  }

  return components


 def    _A	(    self  : Dict			,							a__  : List[Any]			,							a__  : str=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor((1, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	floats_tensor((1, 2, 4, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Any     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   int     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	{
      "prompt": "a dog and a newt",
      "mask_image": mask,
      "image_latents": latents,
      "generator": generator,
      "num_inference_steps": 2,
      "inpaint_strength": 1.0,
      "guidance_scale": 6.0,
      "output_type": "numpy",
  }

  return inputs


 def    _A	(    self  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Tuple=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   str     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   List[str]     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "image": image,
      "source_prompt": "a cat and a frog",
      "target_prompt": "a dog and a newt",
      "generator": generator,
      "num_inference_steps": 2,
      "num_maps_per_mask": 2,
      "mask_encode_strength": 1.0,
      "guidance_scale": 6.0,
      "output_type": "numpy",
  }

  return inputs


 def    _A	(    self  : Tuple			,							a__  : Any			,							a__  : Optional[Any]=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   List[str]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Dict     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   Tuple     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "image": image,
      "prompt": "a cat and a frog",
      "generator": generator,
      "num_inference_steps": 2,
      "inpaint_strength": 1.0,
      "guidance_scale": 6.0,
      "decode_latents": True,
      "output_type": "numpy",
  }
  return inputs


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  if not hasattr(self.pipeline_class			,							"_optional_components"    ):
   return

  lowerCAmelCase__     :   int     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Optional[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  # set all optional components to None and update pipeline config accordingly
  for optional_component in pipe._optional_components:
   setattr(a__			,							a__			,							a__    )
  pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   List[Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(a__			,							a__    ) is None			,							F'''`{optional_component}` did not stay set to None after loading.'''			,							)

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   int     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Dict     		=	np.abs(output - output_loaded    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"cpu"

  lowerCAmelCase__     :   Any     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_mask_inputs(a__    )
  lowerCAmelCase__     :   List[str]     		=	pipe.generate_mask(**a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	mask[0, -3:, -3:]

  self.assertEqual(mask.shape			,							(1, 16, 16)    )
  lowerCAmelCase__     :   str     		=	np.array([0] * 9    )
  lowerCAmelCase__     :   Dict     		=	np.abs(mask_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )
  self.assertEqual(mask[0, -3, -4]			,							0    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"cpu"

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   List[str]     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Dict     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Tuple     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  super().test_inference_batch_single_identical(expected_max_diff=5e-3    )


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"cpu"

  lowerCAmelCase__     :   Optional[int]     		=	self.get_dummy_components()

  lowerCAmelCase__     :   List[Any]     		=	{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepScheduler(**a__    )
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepInverseScheduler(**a__    )

  lowerCAmelCase__     :   Any     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Dict     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Any     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Dict     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   int     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )




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


 def    _A	(    self  : int    ):
  '''simple docstring'''

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


 @classmethod
 def    _A	(    cls  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	load_image(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"    )

  lowerCAmelCase__     :   Dict     		=	raw_image.convert("RGB"    ).resize((768, 768)    )

  lowerCAmelCase__     :   str     		=	raw_image


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   Optional[Any]     		=	DDIMScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   int     		=	DDIMInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Union[str, Any]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   str     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__    ).latents

  lowerCAmelCase__     :   Tuple     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   List[str]     		=	(
      np.array(
          load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/diffedit/pears.png"    ).resize((768, 768)    )    )
      / 255
  )
  assert np.abs((expected_image - image).max()    ) < 5e-1


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   int     		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   Any     		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Optional[int]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__			,							num_inference_steps=25			,							).latents

  lowerCAmelCase__     :   Any     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							num_inference_steps=25			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   Optional[int]     		=	(
      np.array(
          load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/diffedit/pears.png"    ).resize((768, 768)    )    )
      / 255
  )
  assert np.abs((expected_image - image).max()    ) < 5e-1





