






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 (      snake_case__     ,       snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			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       ,    )
							_UpperCAmelCase									=			PNDMScheduler(skip_prk_steps=UpperCAmelCase    )
							torch.manual_seed(0    )
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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       ,    )
							_UpperCAmelCase									=			CLIPTextModel(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
							_UpperCAmelCase									=			Image.fromarray(np.uinta(UpperCAmelCase    )    ).convert('RGB'    ).resize((64, 64)    )
							_UpperCAmelCase									=			Image.fromarray(np.uinta(image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    '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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			StableDiffusionInpaintPipeline(**UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe.to(UpperCAmelCase    )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							_UpperCAmelCase									=			np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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





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





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )
							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
							    '/yellow_cat_sitting_on_a_park_bench.npy'    )

							_UpperCAmelCase									=			'stabilityai/stable-diffusion-2-inpainting'
							_UpperCAmelCase									=			StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase       ,    safety_checker=UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )
							pipe.enable_attention_slicing()

							_UpperCAmelCase									=			'Face of a yellow cat, high resolution, sitting on a park bench'

							_UpperCAmelCase									=			torch.manual_seed(0    )
							_UpperCAmelCase									=			pipe(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    mask_image=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    )
							_UpperCAmelCase									=			output.images[0]

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )
							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
							    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'    )

							_UpperCAmelCase									=			'stabilityai/stable-diffusion-2-inpainting'
							_UpperCAmelCase									=			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()

							_UpperCAmelCase									=			'Face of a yellow cat, high resolution, sitting on a park bench'

							_UpperCAmelCase									=			torch.manual_seed(0    )
							_UpperCAmelCase									=			pipe(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    mask_image=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    )
							_UpperCAmelCase									=			output.images[0]

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )

							_UpperCAmelCase									=			'stabilityai/stable-diffusion-2-inpainting'
							_UpperCAmelCase									=			PNDMScheduler.from_pretrained(UpperCAmelCase       ,    subfolder='scheduler'    )
							_UpperCAmelCase									=			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()

							_UpperCAmelCase									=			'Face of a yellow cat, high resolution, sitting on a park bench'

							_UpperCAmelCase									=			torch.manual_seed(0    )
							_UpperCAmelCase									=			pipe(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    mask_image=UpperCAmelCase       ,    generator=UpperCAmelCase       ,    num_inference_steps=2       ,    output_type='np'       ,    )

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



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_       	=	{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase_       	=	{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   List[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE     :   List[int]									=				[]







			def __init__(     self :				List[str]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict="<unk>"							,   _UpperCamelCase :				List[str]="<s>"							,   _UpperCamelCase :				Tuple="</s>"							,   _UpperCamelCase :				Any="<pad>"							,   _UpperCamelCase :				Any="[SEP]"							,   _UpperCamelCase :				Optional[Any]="[MASK]"							,   _UpperCamelCase :				Any="[CLS]"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				Dict							,   )      ->None:
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else bos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else eos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else unk_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else pad_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else cls_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else mask_token

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   mask_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )

									snake_case_   =	vocab_file

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(_UpperCamelCase							)







			@property
			def 				snake_case__(     self :				str							)      ->List[Any]:
									return self.sp_model.get_piece_size()







			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def __getstate__(     self :				Tuple							)      ->Any:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									return state







			def __setstate__(     self :				str							,   _UpperCamelCase :				List[Any]							)      ->List[str]:
									snake_case_   =	d

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

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







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							)      ->Tuple:
									return self.sp_model.piece_to_id(_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->List[Any]:
									snake_case_   =	self.sp_model.IdToPiece(_UpperCamelCase							)
									return token







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									snake_case_   =	[]
									snake_case_   =	''''''
									snake_case_   =	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(_UpperCamelCase							) + token
																					snake_case_   =	True
																					snake_case_   =	[]
															else:
																					current_sub_tokens.append(_UpperCamelCase							)
																					snake_case_   =	False
									out_string += self.sp_model.decode(_UpperCamelCase							)
									return out_string.strip()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				List[str]							,   )      ->str:
									snake_case_   =	kwargs.pop('''use_source_tokenizer'''							,   _UpperCamelCase							)

									snake_case_   =	self.convert_ids_to_tokens(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							)

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									snake_case_   =	[]
									snake_case_   =	[]
									for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																					continue
															if token in self.added_tokens_encoder:
																					if current_sub_text:
																											sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)
																											snake_case_   =	[]
																					sub_texts.append(_UpperCamelCase							)
															else:
																					current_sub_text.append(_UpperCamelCase							)
									if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
															snake_case_   =	re.sub(R''' (\[(MASK|SEP)\])'''							,   R'''\1'''							,   ''' '''.join(_UpperCamelCase							)							)
									else:
															snake_case_   =	''''''.join(_UpperCamelCase							)

									snake_case_   =	(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
															snake_case_   =	self.clean_up_tokenization(_UpperCamelCase							)
															return clean_text
									else:
															return text







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

									return (out_vocab_file,)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									snake_case_   =	[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[int]:
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,   token_ids_a=_UpperCamelCase							,   already_has_special_tokens=_UpperCamelCase							)

									if token_ids_a is None:
															return [1] + ([0] * len(_UpperCamelCase							)) + [1]
									return [1] + ([0] * len(_UpperCamelCase							)) + [1] + ([0] * len(_UpperCamelCase							)) + [1]







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep							) * [0]
									return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]