

"""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    lowerCamelCase						(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,unittest.TestCase			):
			lowerCamelCase__   :		List[str]							=							StableDiffusionInpaintPipeline
			lowerCamelCase__   :		int							=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase__   :		Optional[Any]							=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__   :		Dict							=							frozenset(
			    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase__   :		str							=							frozenset([]			)

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      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    ,				attention_head_dim=(2, 4)    ,				use_linear_projection=UpperCamelCase_    ,				)
						SCREAMING_SNAKE_CASE__								=      PNDMScheduler(skip_prk_steps=UpperCamelCase_       )
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      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    ,				sample_size=1_2_8    ,				)
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      CLIPTextConfig(
						    bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=3_2    ,				intermediate_size=3_7    ,				layer_norm_eps=1e-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_0_0_0    ,				hidden_act="""gelu"""    ,				projection_dim=5_1_2    ,				)
						SCREAMING_SNAKE_CASE__								=      CLIPTextModel(UpperCamelCase_       )
						SCREAMING_SNAKE_CASE__								=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

						SCREAMING_SNAKE_CASE__								=      {
						    """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				:						Any    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]=0       )   ->    int:
						# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 3_2, 3_2)    ,				rng=random.Random(UpperCamelCase_       )       ).to(UpperCamelCase_       )
						SCREAMING_SNAKE_CASE__								=      image.cpu().permute(0    ,				2    ,				3    ,				1       )[0]
						SCREAMING_SNAKE_CASE__								=      Image.fromarray(np.uinta(UpperCamelCase_       )       ).convert("""RGB"""       ).resize((6_4, 6_4)       )
						SCREAMING_SNAKE_CASE__								=      Image.fromarray(np.uinta(image + 4       )       ).convert("""RGB"""       ).resize((6_4, 6_4)       )
						if str(UpperCamelCase_       ).startswith("""mps"""       ):
									SCREAMING_SNAKE_CASE__								=      torch.manual_seed(UpperCamelCase_       )
						else:
									SCREAMING_SNAKE_CASE__								=      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
						SCREAMING_SNAKE_CASE__								=      {
						    """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				:						List[Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE__								=      self.get_dummy_components()
						SCREAMING_SNAKE_CASE__								=      StableDiffusionInpaintPipeline(**UpperCamelCase_       )
						SCREAMING_SNAKE_CASE__								=      sd_pipe.to(UpperCamelCase_       )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase_       )

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

						assert image.shape == (1, 6_4, 6_4, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]       )

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						super().test_inference_batch_single_identical(expected_max_diff=3e-3       )





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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/sd2-inpaint/init_image.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      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__								=      """stabilityai/stable-diffusion-2-inpainting"""
						SCREAMING_SNAKE_CASE__								=      StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    ,				safety_checker=UpperCamelCase_       )
						pipe.to(UpperCamelCase_       )
						pipe.set_progress_bar_config(disable=UpperCamelCase_       )
						pipe.enable_attention_slicing()

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

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

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 9e-3

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/sd2-inpaint/init_image.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      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__								=      """stabilityai/stable-diffusion-2-inpainting"""
						SCREAMING_SNAKE_CASE__								=      StableDiffusionInpaintPipeline.from_pretrained(
						    UpperCamelCase_    ,				torch_dtype=torch.floataa    ,				safety_checker=UpperCamelCase_    ,				)
						pipe.to(UpperCamelCase_       )
						pipe.set_progress_bar_config(disable=UpperCamelCase_       )
						pipe.enable_attention_slicing()

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

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

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 5e-1





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						SCREAMING_SNAKE_CASE__								=      """stabilityai/stable-diffusion-2-inpainting"""
						SCREAMING_SNAKE_CASE__								=      PNDMScheduler.from_pretrained(UpperCamelCase_    ,				subfolder="""scheduler"""       )
						SCREAMING_SNAKE_CASE__								=      StableDiffusionInpaintPipeline.from_pretrained(
						    UpperCamelCase_    ,				safety_checker=UpperCamelCase_    ,				scheduler=UpperCamelCase_    ,				torch_dtype=torch.floataa    ,				)
						pipe.to(UpperCamelCase_       )
						pipe.set_progress_bar_config(disable=UpperCamelCase_       )
						pipe.enable_attention_slicing(1       )
						pipe.enable_sequential_cpu_offload()

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

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

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 1_0**9








"""simple docstring"""




# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_							:			Dict											= {
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			List[Any]											= ["EfficientNetImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:			Dict											= [
						    "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "EfficientNetForImageClassification",
						    "EfficientNetModel",
						    "EfficientNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_efficientnet import (
						    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    EfficientNetConfig,
						    EfficientNetOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_efficientnet import EfficientNetImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_efficientnet import (
												    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    EfficientNetForImageClassification,
												    EfficientNetModel,
												    EfficientNetPreTrainedModel,
												)

else:
						import sys

						A_							:			List[Any]											= _LazyModule(__name__, globals()["__file__"], _import_structure)


