





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 SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_       ,				snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       StableDiffusionInpaintPipeline
						__magic_name__:			str       =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						__magic_name__:			Dict       =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						__magic_name__:			List[Any]       =       frozenset(
						    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						__magic_name__:			Dict       =       frozenset([]					)






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







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			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=_A		,   )
							snake_case_		:			str											=	PNDMScheduler(skip_prk_steps=_A	)
							torch.manual_seed(0	)
							snake_case_		:			Optional[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=128		,   )
							torch.manual_seed(0	)
							snake_case_		:			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		,   )
							snake_case_		:			Tuple											=	CLIPTextModel(_A	)
							snake_case_		:			Any											=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[str]		,   _A      :      str=0	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Any											=	floats_tensor((1, 3, 32, 32)		,   rng=random.Random(_A	)	).to(_A	)
							snake_case_		:			List[Any]											=	image.cpu().permute(0		,   2		,   3		,   1	)[0]
							snake_case_		:			Tuple											=	Image.fromarray(np.uinta(_A	)	).convert('RGB'	).resize((64, 64)	)
							snake_case_		:			Union[str, Any]											=	Image.fromarray(np.uinta(image + 4	)	).convert('RGB'	).resize((64, 64)	)
							if str(_A	).startswith('mps'	):
								snake_case_		:			str											=	torch.manual_seed(_A	)
							else:
								snake_case_		:			Any											=	torch.Generator(device=_A	).manual_seed(_A	)
							snake_case_		:			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 UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[str]											=	'cpu'  # ensure determinism for the device-dependent torch.Generator
							snake_case_		:			List[Any]											=	self.get_dummy_components()
							snake_case_		:			Dict											=	StableDiffusionInpaintPipeline(**_A	)
							snake_case_		:			Union[str, Any]											=	sd_pipe.to(_A	)
							sd_pipe.set_progress_bar_config(disable=_A	)

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

							assert image.shape == (1, 64, 64, 3)
							snake_case_		:			Tuple											=	np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]	)

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






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







							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3	)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






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







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Tuple:







							"""simple docstring"""


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

							snake_case_		:			Optional[int]											=	'stabilityai/stable-diffusion-2-inpainting'
							snake_case_		:			Any											=	StableDiffusionInpaintPipeline.from_pretrained(_A		,   safety_checker=_A	)
							pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)
							pipe.enable_attention_slicing()

							snake_case_		:			int											=	'Face of a yellow cat, high resolution, sitting on a park bench'

							snake_case_		:			List[str]											=	torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	pipe(
							    prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   output_type='np'		,   )
							snake_case_		:			Dict											=	output.images[0]

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






						def UpperCAmelCase_				(      self      :      List[str]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'	)
							snake_case_		:			Union[str, Any]											=	load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
							snake_case_		:			List[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'	)

							snake_case_		:			Any											=	'stabilityai/stable-diffusion-2-inpainting'
							snake_case_		:			Dict											=	StableDiffusionInpaintPipeline.from_pretrained(
							    _A		,   torch_dtype=torch.floataa		,   safety_checker=_A		,   )
							pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)
							pipe.enable_attention_slicing()

							snake_case_		:			int											=	'Face of a yellow cat, high resolution, sitting on a park bench'

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	pipe(
							    prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   output_type='np'		,   )
							snake_case_		:			List[str]											=	output.images[0]

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






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







							"""simple docstring"""


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

							snake_case_		:			List[Any]											=	load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'	)
							snake_case_		:			List[str]											=	load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)

							snake_case_		:			Any											=	'stabilityai/stable-diffusion-2-inpainting'
							snake_case_		:			Union[str, Any]											=	PNDMScheduler.from_pretrained(_A		,   subfolder='scheduler'	)
							snake_case_		:			str											=	StableDiffusionInpaintPipeline.from_pretrained(
							    _A		,   safety_checker=_A		,   scheduler=_A		,   torch_dtype=torch.floataa		,   )
							pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)
							pipe.enable_attention_slicing(1	)
							pipe.enable_sequential_cpu_offload()

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

							snake_case_		:			Union[str, Any]											=	torch.manual_seed(0	)
							snake_case_		:			str											=	pipe(
							    prompt=_A		,   image=_A		,   mask_image=_A		,   generator=_A		,   num_inference_steps=2		,   output_type='np'		,   )

							snake_case_		:			Tuple											=	torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.6_5 * 10**9








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






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







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

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






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






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







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

