




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
			from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {"vocab_file": "spiece.model"}

UpperCAmelCase__				     = {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase__				     = {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

UpperCAmelCase__				     = "▁"







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       Any       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       int       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       Optional[int]       	=		['input_ids', 'attention_mask']






						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[int]="</s>"				,							lowerCamelCase__ :     Optional[int]="<unk>"				,							lowerCamelCase__ :     Dict="<pad>"				,							lowerCamelCase__ :     Optional[int]=100				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Optional[Dict[str, Any]] = None				,							lowerCamelCase__ :     int=True				,							**lowerCamelCase__ :     Optional[int]				,							)    ->						None:
													"""simple docstring"""


													if extra_ids > 0 and additional_special_tokens is None:
																				__lowercase      =    [f'<extra_id_{i}>' for i in range(lowerCamelCase__				)]
													elif extra_ids > 0 and additional_special_tokens is not None:
																				# Check that we have the right number of extra_id special tokens
																				__lowercase      =    len(set(filter(lambda lowerCamelCase__				: bool('''extra_id''' in str(lowerCamelCase__				)				)				,							lowerCamelCase__				)				)				)
																				if extra_tokens != extra_ids:
																											raise ValueError(
																											    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
																											    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																											    ''' tokens'''				)
													if legacy:
																				logger.warning_once(
																				    f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
																				    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''				)

													__lowercase      =    legacy
													__lowercase      =    {} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    eos_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							extra_ids=lowerCamelCase__				,							additional_special_tokens=lowerCamelCase__				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=lowerCamelCase__				,							**lowerCamelCase__				,							)

													__lowercase      =    vocab_file
													__lowercase      =    extra_ids

													__lowercase      =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(lowerCamelCase__				)






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     int				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																				__lowercase      =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																				if init_max_model_length is not None and init_max_model_length != max_model_length:
																											return init_max_model_length
																				elif init_max_model_length is None:
																											warnings.warn(
																											    '''This tokenizer was incorrectly instantiated with a model max length of'''
																											    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
																											    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																											    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																											    f' {pretrained_model_name_or_path} automatically truncating your input to'
																											    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
																											    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
																											    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																											    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''				,							lowerCamelCase__				,							)

													return max_model_length






						@property
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						int:
													"""simple docstring"""


													return self.sp_model.get_piece_size() + self._extra_ids






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				,							lowerCamelCase__ :     bool = False				)    ->						List[int]:
													"""simple docstring"""


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

													# normal case: some special tokens
													if token_ids_a is None:
																				return ([0] * len(lowerCamelCase__				)) + [1]
													return ([0] * len(lowerCamelCase__				)) + [1] + ([0] * len(lowerCamelCase__				)) + [1]






						def     UpperCAmelCase_      ( self :     Any				)    ->						int:
													"""simple docstring"""


													return list(
													    set(filter(lambda lowerCamelCase__				: bool(re.search(R'''<extra_id_\d+>'''				,							lowerCamelCase__				)				) is not None				,							self.additional_special_tokens				)				)				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													return [self._convert_token_to_id(lowerCamelCase__				) for token in self.get_sentinel_tokens()]






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     List[int]				)    ->						List[int]:
													"""simple docstring"""


													if len(lowerCamelCase__				) > 0 and token_ids[-1] == self.eos_token_id:
																				warnings.warn(
																				    f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
																				    ''' eos tokens being added.'''				)
																				return token_ids
													else:
																				return token_ids + [self.eos_token_id]






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    [self.eos_token_id]

													if token_ids_a is None:
																				return len(token_ids_a + eos				) * [0]
													return len(token_ids_a + eos + token_ids_a + eos				) * [0]






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    self._add_eos_if_not_present(lowerCamelCase__				)
													if token_ids_a is None:
																				return token_ids_a
													else:
																				__lowercase      =    self._add_eos_if_not_present(lowerCamelCase__				)
																				return token_ids_a + token_ids_a






						def __getstate__( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.__dict__.copy()
													__lowercase      =    None
													return state






						def __setstate__( self :     Union[str, Any]				,							lowerCamelCase__ :     Optional[Any]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    d

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

													__lowercase      =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(self.vocab_file				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				,							lowerCamelCase__ :     "TextInput"				,							**lowerCamelCase__ :     Optional[Any]				)    ->						List[str]:
													"""simple docstring"""


													if not self.legacy:
																				__lowercase      =    SPIECE_UNDERLINE + text.replace(lowerCamelCase__				,							''' '''				)
													return super().tokenize(lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Dict:
													"""simple docstring"""


													if not self.legacy:
																				__lowercase      =    text.startswith(lowerCamelCase__				)
																				if is_first:
																											__lowercase      =    text[1:]

													__lowercase      =    self.sp_model.encode(lowerCamelCase__				,							out_type=lowerCamelCase__				)

													if not self.legacy and not is_first and not text.startswith(''' '''				) and tokens[0].startswith(lowerCamelCase__				):
																				__lowercase      =    ([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
													return tokens






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     Tuple				)    ->						str:
													"""simple docstring"""


													if token.startswith('''<extra_id_'''				):
																				__lowercase      =    re.match(R'''<extra_id_(\d+)>'''				,							lowerCamelCase__				)
																				__lowercase      =    int(match.group(1				)				)
																				return self.vocab_size - num - 1
													return self.sp_model.piece_to_id(lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     Optional[int]				)    ->						Any:
													"""simple docstring"""


													if index < self.sp_model.get_piece_size():
																				__lowercase      =    self.sp_model.IdToPiece(lowerCamelCase__				)
													else:
																				__lowercase      =    f'<extra_id_{self.vocab_size - 1 - index}>'
													return token






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     List[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    []
													__lowercase      =    ''''''
													__lowercase      =    False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special:
																																		out_string += " "
																											out_string += self.sp_model.decode(lowerCamelCase__				) + token
																											__lowercase      =    True
																											__lowercase      =    []
																				else:
																											current_sub_tokens.append(lowerCamelCase__				)
																											__lowercase      =    False
													out_string += self.sp_model.decode(lowerCamelCase__				)
													return out_string.strip()






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str] = None				)    ->						Tuple[str]:
													"""simple docstring"""


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

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

													return (out_vocab_file,)












import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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


enable_full_determinism()







class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		CycleDiffusionPipeline
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						UpperCamelCase_	:       int       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       Dict       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
						UpperCamelCase_	:       Dict       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       Optional[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    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				,							)
													__lowercase      =    DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='''scaled_linear'''				,							num_train_timesteps=1_000				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							)
													torch.manual_seed(0				)
													__lowercase      =    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				,							)
													torch.manual_seed(0				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__lowercase      =    CLIPTextModel(lowerCamelCase__				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

													__lowercase      =    {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''safety_checker''': None,
													    '''feature_extractor''': None,
													}
													return components






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    image / 2 + 0.5
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''prompt''': '''An astronaut riding an elephant''',
													    '''source_prompt''': '''An astronaut riding a horse''',
													    '''image''': image,
													    '''generator''': generator,
													    '''num_inference_steps''': 2,
													    '''eta''': 0.1,
													    '''strength''': 0.8,
													    '''guidance_scale''': 3,
													    '''source_guidance_scale''': 1,
													    '''output_type''': '''numpy''',
													}
													return inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

													__lowercase      =    self.get_dummy_components()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

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






						@unittest.skipIf(torch_device != '''cuda'''				,							'''This test requires a GPU'''				)
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(lowerCamelCase__				,							'''half'''				):
																											__lowercase      =    module.half()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

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






						@skip_mps
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_local()






						@unittest.skip('''non-deterministic pipeline'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return super().test_inference_batch_single_identical()






						@skip_mps
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_optional_components()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													return super().test_attention_slicing_forward_pass()







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

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(
													    lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				,							torch_dtype=torch.floataa				,							revision='''fp16'''				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image				).max() < 5e-1






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													assert np.abs(image - expected_image				).max() < 2e-2






