




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class  lowerCamelCase     (		_lowerCamelCase					,unittest.TestCase    ):






			'''simple docstring'''





			UpperCamelCase__										=PaintByExamplePipeline
			UpperCamelCase__										=IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCamelCase__										=IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase__										=frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> str:
								torch.manual_seed(0	)
								__magic_name__				:						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							,    )
								__magic_name__				:						Dict     				=				PNDMScheduler(skip_prk_steps=lowerCamelCase_	)
								torch.manual_seed(0	)
								__magic_name__				:						Union[str, 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							,    )
								torch.manual_seed(0	)
								__magic_name__				:						Any     				=				CLIPVisionConfig(
								    hidden_size=32							,    projection_dim=32							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    image_size=32							,    patch_size=4							,    )
								__magic_name__				:						int     				=				PaintByExampleImageEncoder(lowerCamelCase_							,    proj_size=32	)
								__magic_name__				:						int     				=				CLIPImageProcessor(crop_size=32							,    size=32	)

								__magic_name__				:						List[Any]     				=				{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''image_encoder''': image_encoder,
								    '''safety_checker''': None,
								    '''feature_extractor''': feature_extractor,
								}
								return components

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : int	)    -> Optional[int]:
								__magic_name__				:						Optional[int]     				=				np.array(image.convert('''RGB'''	)	)
								__magic_name__				:						Optional[Any]     				=				image[None].transpose(0							,    3							,    1							,    2	)
								__magic_name__				:						Union[str, Any]     				=				torch.from_numpy(lowerCamelCase_	).to(dtype=torch.floataa	) / 1_2_7.5 - 1.0
								return image

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : int="cpu"							,    lowerCamelCase_      : List[Any]=0	)    -> Optional[Any]:
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								__magic_name__				:						Optional[int]     				=				floats_tensor((1, 3, 32, 32)							,    rng=random.Random(lowerCamelCase_	)	).to(lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				image.cpu().permute(0							,    2							,    3							,    1	)[0]
								__magic_name__				:						Any     				=				Image.fromarray(np.uinta(lowerCamelCase_	)	).convert('''RGB'''	).resize((64, 64)	)
								__magic_name__				:						Optional[Any]     				=				Image.fromarray(np.uinta(image + 4	)	).convert('''RGB'''	).resize((64, 64)	)
								__magic_name__				:						Dict     				=				Image.fromarray(np.uinta(lowerCamelCase_	)	).convert('''RGB'''	).resize((32, 32)	)

								if str(lowerCamelCase_	).startswith('''mps'''	):
													__magic_name__				:						List[str]     				=				torch.manual_seed(lowerCamelCase_	)
								else:
													__magic_name__				:						Any     				=				torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
								__magic_name__				:						str     				=				{
								    '''example_image''': example_image,
								    '''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      : Dict	)    -> Dict:
								__magic_name__				:						Optional[Any]     				=				self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								__magic_name__				:						List[Any]     				=				PaintByExamplePipeline(**lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				pipe.to('''cpu'''	)
								pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						Tuple     				=				self.get_dummy_inputs()
								__magic_name__				:						Tuple     				=				pipe(**lowerCamelCase_	)
								__magic_name__				:						Any     				=				output.images

								__magic_name__				:						Optional[int]     				=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								__magic_name__				:						List[str]     				=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]	)

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

			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> List[str]:
								__magic_name__				:						List[str]     				=				'''cpu'''
								__magic_name__				:						int     				=				self.get_dummy_inputs()
								inputs.pop('''mask_image'''	)
								__magic_name__				:						Optional[Any]     				=				self.convert_to_pt(inputs.pop('''image'''	)	)
								__magic_name__				:						List[str]     				=				image.clamp(0							,    1	) / 2

								# make sure here that pndm scheduler skips prk
								__magic_name__				:						Any     				=				PaintByExamplePipeline(**self.get_dummy_components()	)
								__magic_name__				:						List[Any]     				=				pipe.to(lowerCamelCase_	)
								pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						str     				=				pipe(image=lowerCamelCase_							,    mask_image=mask_image[:, 0]							,    **lowerCamelCase_	)
								__magic_name__				:						int     				=				output.images

								__magic_name__				:						Union[str, Any]     				=				image.cpu().permute(0							,    2							,    3							,    1	)[0]
								__magic_name__				:						str     				=				mask_image.cpu().permute(0							,    2							,    3							,    1	)[0]

								__magic_name__				:						str     				=				Image.fromarray(np.uinta(lowerCamelCase_	)	).convert('''RGB'''	)
								__magic_name__				:						Union[str, Any]     				=				Image.fromarray(np.uinta(lowerCamelCase_	)	).convert('''RGB'''	)

								__magic_name__				:						Any     				=				pipe(**self.get_dummy_inputs()	)
								__magic_name__				:						int     				=				output.images

								assert out_a.shape == (1, 64, 64, 3)
								assert np.abs(out_a.flatten() - out_a.flatten()	).max() < 5E-2


			def 				UpperCAmelCase__							(			self      : Tuple	)    -> int:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> List[str]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


			def 				UpperCAmelCase__							(			self      : Tuple	)    -> int:
								# make sure here that pndm scheduler skips prk
								__magic_name__				:						str     				=				load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/dog_in_bucket.png'''	)
								__magic_name__				:						Dict     				=				load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/mask.png'''	)
								__magic_name__				:						List[Any]     				=				load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/paint_by_example/panda.jpg'''	)

								__magic_name__				:						int     				=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''	)
								__magic_name__				:						Union[str, Any]     				=				pipe.to(lowerCamelCase_	)
								pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						Any     				=				torch.manual_seed(321	)
								__magic_name__				:						str     				=				pipe(
								    image=lowerCamelCase_							,    mask_image=lowerCamelCase_							,    example_image=lowerCamelCase_							,    generator=lowerCamelCase_							,    guidance_scale=5.0							,    num_inference_steps=50							,    output_type='''np'''							,    )

								__magic_name__				:						List[Any]     				=				output.images

								__magic_name__				:						Dict     				=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								__magic_name__				:						Optional[int]     				=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]	)

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









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__lowerCamelCase :    List[Any]      						=      logging.get_logger(__name__)

__lowerCamelCase :    Optional[int]      						=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase :    List[Any]      						=      {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase :    int      						=      {
    '''microsoft/mpnet-base''': 5_12,
}

__lowerCamelCase :    Union[str, Any]      						=      {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										=VOCAB_FILES_NAMES
			UpperCamelCase__										=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__										=PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__										=MPNetTokenizer
			UpperCamelCase__										=['''input_ids''', '''attention_mask''']

			def __init__(			self      : Optional[int]							,    lowerCamelCase_      : List[str]=None							,    lowerCamelCase_      : int=None							,    lowerCamelCase_      : Dict=True							,    lowerCamelCase_      : int="<s>"							,    lowerCamelCase_      : Dict="</s>"							,    lowerCamelCase_      : int="</s>"							,    lowerCamelCase_      : List[Any]="<s>"							,    lowerCamelCase_      : Union[str, Any]="[UNK]"							,    lowerCamelCase_      : Any="<pad>"							,    lowerCamelCase_      : Dict="<mask>"							,    lowerCamelCase_      : Optional[Any]=True							,    lowerCamelCase_      : List[Any]=None							,    **lowerCamelCase_      : List[Any]							,    )    -> Dict:
								super().__init__(
								    lowerCamelCase_							,    tokenizer_file=lowerCamelCase_							,    do_lower_case=lowerCamelCase_							,    bos_token=lowerCamelCase_							,    eos_token=lowerCamelCase_							,    sep_token=lowerCamelCase_							,    cls_token=lowerCamelCase_							,    unk_token=lowerCamelCase_							,    pad_token=lowerCamelCase_							,    mask_token=lowerCamelCase_							,    tokenize_chinese_chars=lowerCamelCase_							,    strip_accents=lowerCamelCase_							,    **lowerCamelCase_							,    )

								__magic_name__				:						Any     				=				json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    pre_tok_state.get('''lowercase'''							,    lowerCamelCase_	) != do_lower_case
								    or pre_tok_state.get('''strip_accents'''							,    lowerCamelCase_	) != strip_accents
								):
													__magic_name__				:						int     				=				getattr(lowerCamelCase_							,    pre_tok_state.pop('''type'''	)	)
													__magic_name__				:						str     				=				do_lower_case
													__magic_name__				:						Union[str, Any]     				=				strip_accents
													__magic_name__				:						str     				=				pre_tok_class(**lowerCamelCase_	)

								__magic_name__				:						List[Any]     				=				do_lower_case

			@property
			def 				UpperCAmelCase__							(			self      : str	)    -> str:
								if self._mask_token is None:
													if self.verbose:
																		logger.error('''Using mask_token, but it is not set yet.'''	)
													return None
								return str(self._mask_token	)

			@mask_token.setter
			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : List[str]	)    -> Optional[int]:
								__magic_name__				:						List[str]     				=				AddedToken(lowerCamelCase_							,    lstrip=lowerCamelCase_							,    rstrip=lowerCamelCase_	) if isinstance(lowerCamelCase_							,    lowerCamelCase_	) else value
								__magic_name__				:						Tuple     				=				value

			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : Any=None	)    -> List[Any]:
								__magic_name__				:						Optional[int]     				=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
													return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : List[int]							,    lowerCamelCase_      : Optional[List[int]] = None	)    -> List[int]:
								__magic_name__				:						Tuple     				=				[self.sep_token_id]
								__magic_name__				:						Any     				=				[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 + sep + token_ids_a + sep	) * [0]


			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : str							,    lowerCamelCase_      : Optional[str] = None	)    -> Tuple[str]:
								__magic_name__				:						List[Any]     				=				self._tokenizer.model.save(lowerCamelCase_							,    name=lowerCamelCase_	)
								return tuple(lowerCamelCase_	)



