

'''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 lowerCamelCase			(  lowerCamelCase_	,						lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	StableDiffusionInpaintPipeline
			__snake_case        =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__snake_case        =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__snake_case        =	frozenset(
			    []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__snake_case        =	frozenset([]						)


			def lowercase__						(				self	:						Optional[Any]			)      ->		Tuple:





									'''simple docstring'''
									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=__snake_case		,      )
									A__					:       Dict     =PNDMScheduler(skip_prk_steps=__snake_case			)
									torch.manual_seed(0			)
									A__					:       List[Any]     =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=1_28		,      )
									torch.manual_seed(0			)
									A__					:       int     =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=10_00		,      hidden_act="""gelu"""		,      projection_dim=5_12		,      )
									A__					:       List[Any]     =CLIPTextModel(__snake_case			)
									A__					:       List[Any]     =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 lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						int=0			)      ->		List[Any]:





									'''simple docstring'''
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									A__					:       Optional[int]     =floats_tensor((1, 3, 32, 32)		,      rng=random.Random(__snake_case			)			).to(__snake_case			)
									A__					:       Any     =image.cpu().permute(0		,      2		,      3		,      1			)[0]
									A__					:       Dict     =Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((64, 64)			)
									A__					:       Dict     =Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
									if str(__snake_case			).startswith("""mps"""			):
															A__					:       Dict     =torch.manual_seed(__snake_case			)
									else:
															A__					:       Dict     =torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
									A__					:       List[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 lowercase__						(				self	:						int			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
									A__					:       Optional[int]     =self.get_dummy_components()
									A__					:       List[Any]     =StableDiffusionInpaintPipeline(**__snake_case			)
									A__					:       List[str]     =sd_pipe.to(__snake_case			)
									sd_pipe.set_progress_bar_config(disable=__snake_case			)

									A__					:       Union[str, Any]     =self.get_dummy_inputs(__snake_case			)
									A__					:       List[str]     =sd_pipe(**__snake_case			).images
									A__					:       Any     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									A__					:       Optional[int]     =np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

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



			def lowercase__						(				self	:						List[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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



			'''simple docstring'''






			def lowercase__						(				self	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


			def lowercase__						(				self	:						Union[str, Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-inpaint/init_image.png"""			)
									A__					:       List[Any]     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
									A__					:       Optional[int]     =load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
									    """/yellow_cat_sitting_on_a_park_bench.npy"""			)

									A__					:       str     ="""stabilityai/stable-diffusion-2-inpainting"""
									A__					:       Any     =StableDiffusionInpaintPipeline.from_pretrained(__snake_case		,      safety_checker=__snake_case			)
									pipe.to(__snake_case			)
									pipe.set_progress_bar_config(disable=__snake_case			)
									pipe.enable_attention_slicing()

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

									A__					:       Tuple     =torch.manual_seed(0			)
									A__					:       Tuple     =pipe(
									    prompt=__snake_case		,      image=__snake_case		,      mask_image=__snake_case		,      generator=__snake_case		,      output_type="""np"""		,      )
									A__					:       Optional[Any]     =output.images[0]

									assert image.shape == (5_12, 5_12, 3)
									assert np.abs(expected_image - image			).max() < 9e-3


			def lowercase__						(				self	:						List[Any]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       List[Any]     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-inpaint/init_image.png"""			)
									A__					:       List[str]     =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
									A__					:       Optional[Any]     =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[int]     ="""stabilityai/stable-diffusion-2-inpainting"""
									A__					:       Tuple     =StableDiffusionInpaintPipeline.from_pretrained(
									    __snake_case		,      torch_dtype=torch.floataa		,      safety_checker=__snake_case		,      )
									pipe.to(__snake_case			)
									pipe.set_progress_bar_config(disable=__snake_case			)
									pipe.enable_attention_slicing()

									A__					:       List[str]     ="""Face of a yellow cat, high resolution, sitting on a park bench"""

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

									assert image.shape == (5_12, 5_12, 3)
									assert np.abs(expected_image - image			).max() < 5e-1



			def lowercase__						(				self	:						List[str]			)      ->		Union[str, Any]:





									'''simple docstring'''
									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__					:       Dict     ="""stabilityai/stable-diffusion-2-inpainting"""
									A__					:       Any     =PNDMScheduler.from_pretrained(__snake_case		,      subfolder="""scheduler"""			)
									A__					:       int     =StableDiffusionInpaintPipeline.from_pretrained(
									    __snake_case		,      safety_checker=__snake_case		,      scheduler=__snake_case		,      torch_dtype=torch.floataa		,      )
									pipe.to(__snake_case			)
									pipe.set_progress_bar_config(disable=__snake_case			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

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

									A__					:       Tuple     =torch.manual_seed(0			)
									A__					:       str     =pipe(
									    prompt=__snake_case		,      image=__snake_case		,      mask_image=__snake_case		,      generator=__snake_case		,      num_inference_steps=2		,      output_type="""np"""		,      )

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




'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""			)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""			)

									super().__init__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor_class



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor

