

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[Any]            =						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any            =						[
						    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MgpstrModel""",
						    """MgpstrPreTrainedModel""",
						    """MgpstrForSceneTextRecognition""",
						]

if TYPE_CHECKING:
						from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
						from .processing_mgp_str import MgpstrProcessor
						from .tokenization_mgp_str import MgpstrTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mgp_str import (
												    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MgpstrForSceneTextRecognition,
												    MgpstrModel,
												    MgpstrPreTrainedModel,
												)
else:
						import sys

						lowerCAmelCase							:  Dict            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					StableDiffusionInpaintPipeline
			_UpperCAmelCase       :							Tuple      							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase       :							List[str]      							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase       :							Tuple      							=					frozenset(
			    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase       :							Optional[int]      							=					frozenset([]     )



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[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=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			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       ,						sample_size=128       ,						)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_:			Dict  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[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 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			int  		=	StableDiffusionInpaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Dict  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			str  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	output.images[0]

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Any  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_:			str  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Dict  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						safety_checker=lowerCAmelCase__       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	output.images[0]

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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PNDMScheduler.from_pretrained(lowerCAmelCase__       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_:			Any  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=2       ,						output_type="np"       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






