


"""simple docstring"""


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class snake_case      (  tf.keras.layers.Layer):







		def __init__(					self      :       str    ,   a__      :       Dict[str, int]    ,   a__      :       List[str]    ,   a__      :       int = None    ,   a__      :       int = None		)       ->						Optional[Any]:





						'''simple docstring'''







						super().__init__()
						_A					  =			pad_token_id
						_A					  =			max_length
						_A					  =			vocab
						_A					  =			merges
						_A					  =			BytePairTokenizer(a__    ,   a__    ,   sequence_length=a__		)







		@classmethod
		def  a_ (					cls      :       Optional[Any]    ,   a__      :       GPTaTokenizer    ,   *a__      :       Dict    ,   **a__      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			[" ".join(a__		) for m in tokenizer.bpe_ranks.keys()]
						_A					  =			tokenizer.get_vocab()
						return cls(a__    ,   a__    ,   *a__    ,   **a__		)







		@classmethod
		def  a_ (					cls      :       List[str]    ,   a__      :       Union[str, os.PathLike]    ,   *a__      :       Tuple    ,   **a__      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			GPTaTokenizer.from_pretrained(a__    ,   *a__    ,   **a__		)
						return cls.from_tokenizer(a__    ,   *a__    ,   **a__		)







		@classmethod
		def  a_ (					cls      :       List[Any]    ,   a__      :       int		)       ->						Dict:





						'''simple docstring'''







						return cls(**a__		)







		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						return {
						    "vocab": self.vocab,
						    "merges": self.merges,
						    "max_length": self.max_length,
						    "pad_token_id": self.pad_token_id,
						}







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





						'''simple docstring'''







						_A					  =			self.tf_tokenizer(a__		)
						_A					  =			tf.ones_like(a__		)

						if self.pad_token_id is not None:
										# pad the tokens up to max length
										_A					  =			max_length if max_length is not None else self.max_length

										if max_length is not None:
														_A  ,		_A					  =			pad_model_inputs(
														    a__    ,   max_seq_length=a__    ,   pad_value=self.pad_token_id		)

						return {"attention_mask": attention_mask, "input_ids": input_ids}



"""simple docstring"""


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 snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__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  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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__    ,   )
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						torch.manual_seed(0		)
						_A					  =			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=1_28    ,   )
						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
						_A					  =			CLIPTextModel(a__		)
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A					  =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple=0		)       ->						int:





						'''simple docstring'''







						_A					  =			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(a__		)		).to(a__		)
						_A					  =			image.cpu().permute(0    ,   2    ,   3    ,   1		)[0]
						_A					  =			Image.fromarray(np.uinta(a__		)		).convert("RGB"		).resize((64, 64)		)
						_A					  =			Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
						if str(a__		).startswith("mps"		):
										_A					  =			torch.manual_seed(a__		)
						else:
										_A					  =			torch.Generator(device=a__		).manual_seed(a__		)
						_A					  =			{
						    "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  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.get_dummy_components()
						_A					  =			StableDiffusionInpaintPipeline(**a__		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

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

						assert image.shape == (1, 64, 64, 3)
						_A					  =			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  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







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





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







		def  a_ (					self      :       List[Any]		)       ->						Any:





						'''simple docstring'''







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







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





						'''simple docstring'''







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

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			StableDiffusionInpaintPipeline.from_pretrained(a__    ,   safety_checker=a__		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)
						pipe.enable_attention_slicing()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 9E-3







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





						'''simple docstring'''







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

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			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()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   output_type="np"    ,   )
						_A					  =			output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image		).max() < 5E-1







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





						'''simple docstring'''







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

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

						_A					  =			"stabilityai/stable-diffusion-2-inpainting"
						_A					  =			PNDMScheduler.from_pretrained(a__    ,   subfolder="scheduler"		)
						_A					  =			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()

						_A					  =			"Face of a yellow cat, high resolution, sitting on a park bench"

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe(
						    prompt=a__    ,   image=a__    ,   mask_image=a__    ,   generator=a__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

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