


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   A__							(						UpperCamelCase__      ,   unittest.TestCase     ):
						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    :					Tuple     )							->   Any:


										"""simple docstring"""





										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =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 ,			)
										_SCREAMING_SNAKE_CASE	     =PNDMScheduler(skip_prk_steps=_a     )
										torch.manual_seed(0     )
										_SCREAMING_SNAKE_CASE	     =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     )
										_SCREAMING_SNAKE_CASE	     =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 ,			)
										_SCREAMING_SNAKE_CASE	     =PaintByExampleImageEncoder(_a ,			proj_size=32     )
										_SCREAMING_SNAKE_CASE	     =CLIPImageProcessor(crop_size=32 ,			size=32     )

										_SCREAMING_SNAKE_CASE	     ={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''image_encoder''': image_encoder,
										    '''safety_checker''': None,
										    '''feature_extractor''': feature_extractor,
										}
										return components




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					Dict     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =np.array(image.convert('''RGB'''     )     )
										_SCREAMING_SNAKE_CASE	     =image[None].transpose(0 ,			3 ,			1 ,			2     )
										_SCREAMING_SNAKE_CASE	     =torch.from_numpy(_a     ).to(dtype=torch.floataa     ) / 1_27.5 - 1.0
										return image




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Tuple="cpu" ,			_a    :					Optional[int]=0     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =floats_tensor((1, 3, 32, 32) ,			rng=random.Random(_a     )     ).to(_a     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     ).resize((64, 64)     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(image + 4     )     ).convert('''RGB'''     ).resize((64, 64)     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     ).resize((32, 32)     )

										if str(_a     ).startswith('''mps'''     ):
														_SCREAMING_SNAKE_CASE	     =torch.manual_seed(_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =torch.Generator(device=_a     ).manual_seed(_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''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    :					Optional[int]     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline(**_a     )
										_SCREAMING_SNAKE_CASE	     =pipe.to('''cpu'''     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =self.get_dummy_inputs()
										_SCREAMING_SNAKE_CASE	     =pipe(**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]     )

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




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     ='''cpu'''
										_SCREAMING_SNAKE_CASE	     =self.get_dummy_inputs()
										inputs.pop('''mask_image'''     )
										_SCREAMING_SNAKE_CASE	     =self.convert_to_pt(inputs.pop('''image'''     )     )
										_SCREAMING_SNAKE_CASE	     =image.clamp(0 ,			1     ) / 2

										# make sure here that pndm scheduler skips prk
										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline(**self.get_dummy_components()     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =pipe(image=_a ,			mask_image=mask_image[:, 0] ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]
										_SCREAMING_SNAKE_CASE	     =mask_image.cpu().permute(0 ,			2 ,			3 ,			1     )[0]

										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )
										_SCREAMING_SNAKE_CASE	     =Image.fromarray(np.uinta(_a     )     ).convert('''RGB'''     )

										_SCREAMING_SNAKE_CASE	     =pipe(**self.get_dummy_inputs()     )
										_SCREAMING_SNAKE_CASE	     =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    :					List[str]     )							->   Any:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[Any]:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/dog_in_bucket.png'''     )
										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/mask.png'''     )
										_SCREAMING_SNAKE_CASE	     =load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/paint_by_example/panda.jpg'''     )

										_SCREAMING_SNAKE_CASE	     =PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''     )
										_SCREAMING_SNAKE_CASE	     =pipe.to(_a     )
										pipe.set_progress_bar_config(disable=_a     )

										_SCREAMING_SNAKE_CASE	     =torch.manual_seed(321     )
										_SCREAMING_SNAKE_CASE	     =pipe(
										    image=_a ,			mask_image=_a ,			example_image=_a ,			generator=_a ,			guidance_scale=5.0 ,			num_inference_steps=50 ,			output_type='''np''' ,			)

										_SCREAMING_SNAKE_CASE	     =output.images

										_SCREAMING_SNAKE_CASE	     =image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										_SCREAMING_SNAKE_CASE	     =np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]     )

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



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)