




'''simple docstring'''






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						(					SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							PaintByExamplePipeline
				A       =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
				A       =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				A       =							frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
				def 			a_			(self     )      ->			int:
							torch.manual_seed(0     )
							__UpperCamelCase :     int										=  UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)					,    layers_per_block=2					,    sample_size=3_2					,    in_channels=9					,    out_channels=4					,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,    cross_attention_dim=3_2					,    )
							__UpperCamelCase :     int										=  PNDMScheduler(skip_prk_steps=_UpperCAmelCase     )
							torch.manual_seed(0     )
							__UpperCamelCase :     List[Any]										=  AutoencoderKL(
							    block_out_channels=[3_2, 6_4]					,    in_channels=3					,    out_channels=3					,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,    latent_channels=4					,    )
							torch.manual_seed(0     )
							__UpperCamelCase :     Any										=  CLIPVisionConfig(
							    hidden_size=3_2					,    projection_dim=3_2					,    intermediate_size=3_7					,    layer_norm_eps=1E-05					,    num_attention_heads=4					,    num_hidden_layers=5					,    image_size=3_2					,    patch_size=4					,    )
							__UpperCamelCase :     Tuple										=  PaintByExampleImageEncoder(_UpperCAmelCase					,    proj_size=3_2     )
							__UpperCamelCase :     str										=  CLIPImageProcessor(crop_size=3_2					,    size=3_2     )

							__UpperCamelCase :     Tuple										=  {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Any:
							__UpperCamelCase :     List[str]										=  np.array(image.convert("RGB"     )     )
							__UpperCamelCase :     List[str]										=  image[None].transpose(0					,    3					,    1					,    2     )
							__UpperCamelCase :     Union[str, Any]										=  torch.from_numpy(_UpperCAmelCase     ).to(dtype=torch.floataa     ) / 127.5 - 1.0
							return image
				def 			a_			(self					,    _UpperCAmelCase="cpu"					,    _UpperCAmelCase=0     )      ->			Optional[Any]:
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							__UpperCamelCase :     Any										=  floats_tensor((1, 3, 3_2, 3_2)					,    rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
							__UpperCamelCase :     Union[str, Any]										=  image.cpu().permute(0					,    2					,    3					,    1     )[0]
							__UpperCamelCase :     Optional[int]										=  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     ).resize((6_4, 6_4)     )
							__UpperCamelCase :     Any										=  Image.fromarray(np.uinta(image + 4     )     ).convert("RGB"     ).resize((6_4, 6_4)     )
							__UpperCamelCase :     Optional[Any]										=  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     ).resize((3_2, 3_2)     )

							if str(_UpperCAmelCase     ).startswith("mps"     ):
										__UpperCamelCase :     Tuple										=  torch.manual_seed(_UpperCAmelCase     )
							else:
										__UpperCamelCase :     Union[str, Any]										=  torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )
							__UpperCamelCase :     Optional[int]										=  {
							    "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 			a_			(self     )      ->			Any:
							__UpperCamelCase :     Any										=  self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							__UpperCamelCase :     Union[str, Any]										=  PaintByExamplePipeline(**_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  pipe.to("cpu"     )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

							__UpperCamelCase :     List[Any]										=  self.get_dummy_inputs()
							__UpperCamelCase :     int										=  pipe(**_UpperCAmelCase     )
							__UpperCamelCase :     int										=  output.images

							__UpperCamelCase :     int										=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							__UpperCamelCase :     Dict										=  np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase :     List[Any]										=  "cpu"
							__UpperCamelCase :     List[Any]										=  self.get_dummy_inputs()
							inputs.pop("mask_image"     )
							__UpperCamelCase :     Optional[Any]										=  self.convert_to_pt(inputs.pop("image"     )     )
							__UpperCamelCase :     Optional[Any]										=  image.clamp(0					,    1     ) / 2

							# make sure here that pndm scheduler skips prk
							__UpperCamelCase :     Dict										=  PaintByExamplePipeline(**self.get_dummy_components()     )
							__UpperCamelCase :     Optional[int]										=  pipe.to(_UpperCAmelCase     )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

							__UpperCamelCase :     int										=  pipe(image=_UpperCAmelCase					,    mask_image=mask_image[:, 0]					,    **_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  output.images

							__UpperCamelCase :     Tuple										=  image.cpu().permute(0					,    2					,    3					,    1     )[0]
							__UpperCamelCase :     str										=  mask_image.cpu().permute(0					,    2					,    3					,    1     )[0]

							__UpperCamelCase :     Optional[int]										=  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     )
							__UpperCamelCase :     Optional[int]										=  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     )

							__UpperCamelCase :     Optional[Any]										=  pipe(**self.get_dummy_inputs()     )
							__UpperCamelCase :     Union[str, Any]										=  output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()     ).max() < 5E-2





				def 			a_			(self     )      ->			Dict:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3     )







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




				'''simple docstring'''





				def 			a_			(self     )      ->			Dict:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





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

							__UpperCamelCase :     int										=  PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"     )
							__UpperCamelCase :     Any										=  pipe.to(_UpperCAmelCase     )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

							__UpperCamelCase :     Optional[int]										=  torch.manual_seed(3_2_1     )
							__UpperCamelCase :     Dict										=  pipe(
							    image=_UpperCAmelCase					,    mask_image=_UpperCAmelCase					,    example_image=_UpperCAmelCase					,    generator=_UpperCAmelCase					,    guidance_scale=5.0					,    num_inference_steps=5_0					,    output_type="np"					,    )

							__UpperCamelCase :     int										=  output.images

							__UpperCamelCase :     List[str]										=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCamelCase :     str										=  np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]     )

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









'''simple docstring'''






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							BertweetTokenizer
				A       =							False
				def 			a_			(self     )      ->			Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							__UpperCamelCase :     str										=  ["I", "m", "V@@", "R@@", "r", "e@@"]
							__UpperCamelCase :     int										=  dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase     )     )     )     )
							__UpperCamelCase :     List[Any]										=  ["#version: 0.2", "a m</w>"]
							__UpperCamelCase :     str										=  {"unk_token": "<unk>"}

							__UpperCamelCase :     Any										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["vocab_file"]     )
							__UpperCamelCase :     Tuple										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file					,    "w"					,    encoding="utf-8"     ) as fp:
										for token in vocab_tokens:
													fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file					,    "w"					,    encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCAmelCase     )     )
				def 			a_			(self					,    **_UpperCAmelCase     )      ->			str:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     Dict										=  "I am VinAI Research"
							__UpperCamelCase :     Any										=  "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text





				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Tuple										=  BertweetTokenizer(self.vocab_file					,    self.merges_file					,    **self.special_tokens_map     )
							__UpperCamelCase :     Union[str, Any]										=  "I am VinAI Research"
							__UpperCamelCase :     List[str]										=  "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							__UpperCamelCase :     Optional[int]										=  tokenizer.tokenize(_UpperCAmelCase     )
							self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase     )

							__UpperCamelCase :     Optional[Any]										=  tokens + [tokenizer.unk_token]

							__UpperCamelCase :     Any										=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )					,    _UpperCAmelCase     )



