






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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		StableDiffusionInpaintPipeline
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_     =		frozenset(
					    []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					a_     =		frozenset([]						)



					def 	_lowercase					( self					:  List[Any]	):
						torch.manual_seed(0	)
						snake_case__			:				Optional[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							,		attention_head_dim=(2, 4)							,		use_linear_projection=__A							,		)
						snake_case__			:				Dict       =  PNDMScheduler(skip_prk_steps=__A	)
						torch.manual_seed(0	)
						snake_case__			:				Optional[int]       =  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	)
						snake_case__			:				int       =  CLIPTextConfig(
						    bos_token_id=0							,		eos_token_id=2							,		hidden_size=3_2							,		intermediate_size=3_7							,		layer_norm_eps=1e-0_5							,		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							,		)
						snake_case__			:				Union[str, Any]       =  CLIPTextModel(__A	)
						snake_case__			:				Dict       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

						snake_case__			:				List[str]       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Union[str, Any]       =  sd_pipe(**__A	).images
						snake_case__			:				Optional[Any]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						snake_case__			:				Optional[int]       =  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 	_lowercase					( self					:  str	):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3	)



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





					"""simple docstring"""






					def 	_lowercase					( self					:  Union[str, Any]	):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



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

						snake_case__			:				Dict       =  "stabilityai/stable-diffusion-2-inpainting"
						snake_case__			:				Optional[int]       =  StableDiffusionInpaintPipeline.from_pretrained(__A							,		safety_checker=__A	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Optional[Any]       =  "Face of a yellow cat, high resolution, sitting on a park bench"

						snake_case__			:				Optional[Any]       =  torch.manual_seed(0	)
						snake_case__			:				Tuple       =  pipe(
						    prompt=__A							,		image=__A							,		mask_image=__A							,		generator=__A							,		output_type="np"							,		)
						snake_case__			:				Optional[Any]       =  output.images[0]

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



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

						snake_case__			:				Optional[int]       =  "stabilityai/stable-diffusion-2-inpainting"
						snake_case__			:				Optional[int]       =  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()

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

						snake_case__			:				Any       =  torch.manual_seed(0	)
						snake_case__			:				Union[str, Any]       =  pipe(
						    prompt=__A							,		image=__A							,		mask_image=__A							,		generator=__A							,		output_type="np"							,		)
						snake_case__			:				Any       =  output.images[0]

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






					def 	_lowercase					( self					:  Union[str, Any]	):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						snake_case__			:				Optional[Any]       =  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"	)
						snake_case__			:				Dict       =  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"	)

						snake_case__			:				str       =  "stabilityai/stable-diffusion-2-inpainting"
						snake_case__			:				str       =  PNDMScheduler.from_pretrained(__A							,		subfolder="scheduler"	)
						snake_case__			:				Union[str, Any]       =  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()

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

						snake_case__			:				str       =  torch.manual_seed(0	)
						snake_case__			:				Any       =  pipe(
						    prompt=__A							,		image=__A							,		mask_image=__A							,		generator=__A							,		num_inference_steps=2							,		output_type="np"							,		)

						snake_case__			:				Tuple       =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 1_0**9











from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  SMALL_MODEL_IDENTIFIER
						snake_case__			:				Any       =  "pt"
						snake_case__			:				Any       =  "tf"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				int       =  AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__A	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  TFAutoModel.from_pretrained(self.test_model							,		from_pt=__A	)
						model_tf.save_pretrained(__A	)



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  "mock_framework"

						# Framework provided - return whatever the user provides
						snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model							,		__A	)
						self.assertEqual(__A							,		__A	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				Optional[int]       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				int       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)



					def 	_lowercase					( self					:  Dict	):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				List[str]       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				Tuple       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
							with self.assertRaises(__A	):
								snake_case__			:				int       =  FeaturesManager.determine_framework(__A	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	):
							snake_case__			:				List[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						snake_case__			:				Tuple       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				int       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Both in environment -> use PyTorch
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[int]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# Both not in environment -> raise error
						snake_case__			:				List[str]       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[Any]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							with self.assertRaises(__A	):
								snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)



