

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 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       Tuple   =						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			,				)
												_a      :       List[Any]   =						PNDMScheduler(skip_prk_steps=_a			)
												torch.manual_seed(0			)
												_a      :       Tuple   =						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			)
												_a      :       List[str]   =						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			,				)
												_a      :       str   =						CLIPTextModel(_a			)
												_a      :       str   =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

												_a      :       Dict   =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components


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

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

												assert image.shape == (1, 6_4, 6_4, 3)
												_a      :       str   =						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 							__lowercase							(     self			)       ->       Any:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       int   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       Dict   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
												_a      :       Any   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
												    '''/yellow_cat_sitting_on_a_park_bench.npy'''			)

												_a      :       Optional[int]   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       Union[str, Any]   =						StableDiffusionInpaintPipeline.from_pretrained(_a			,				safety_checker=_a			)
												pipe.to(_a			)
												pipe.set_progress_bar_config(disable=_a			)
												pipe.enable_attention_slicing()

												_a      :       int   =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

												_a      :       List[Any]   =						torch.manual_seed(0			)
												_a      :       Any   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				output_type='''np'''			,				)
												_a      :       Union[str, 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			)       ->       Dict:
												_a      :       List[Any]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       Optional[int]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
												_a      :       List[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'''			)

												_a      :       Tuple   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       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()

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

												_a      :       Dict   =						torch.manual_seed(0			)
												_a      :       Any   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				output_type='''np'''			,				)
												_a      :       List[str]   =						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			)       ->       List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												_a      :       Any   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       List[str]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)

												_a      :       List[Any]   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       Optional[int]   =						PNDMScheduler.from_pretrained(_a			,				subfolder='''scheduler'''			)
												_a      :       Tuple   =						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      :       Tuple   =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

												_a      :       Tuple   =						torch.manual_seed(0			)
												_a      :       Dict   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				num_inference_steps=2			,				output_type='''np'''			,				)

												_a      :       List[str]   =						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"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]   )					->Dict:






	'''simple docstring'''






	a :						Any      =							[2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
	a :						str      =							True if "large" in model_name or "huge" in model_name else False
	a :						Optional[Any]      =							True if "large" in model_name or "huge" in model_name else False
	a :						Dict      =							True if "large" in model_name or "huge" in model_name else False

	if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
		if "fl3" in model_name:
			a :						Union[str, Any]      =							[3, 3, 3, 3]
			a :						List[str]      =							[5, 5, 5, 5]
		elif "fl4" in model_name:
			a :						Any      =							[4, 4, 4, 4]
			a :						Optional[Any]      =							[3, 3, 3, 3]

	if "tiny" in model_name or "small" in model_name or "base" in model_name:
		a :						Dict      =							[3, 3, 3, 3]
		if "lrf" in model_name:
			a :						Optional[int]      =							[3, 3, 3, 3]
		else:
			a :						Tuple      =							[2, 2, 2, 2]

	if "tiny" in model_name:
		a :						List[str]      =							96
	elif "small" in model_name:
		a :						Union[str, Any]      =							96
	elif "base" in model_name:
		a :						Dict      =							128
	elif "large" in model_name:
		a :						Union[str, Any]      =							192
	elif "xlarge" in model_name:
		a :						Tuple      =							256
	elif "huge" in model_name:
		a :						List[str]      =							352

	# set label information
	a :						List[Any]      =							"huggingface/label-files"
	if "large" in model_name or "huge" in model_name:
		a :						Optional[int]      =							"imagenet-22k-id2label.json"
	else:
		a :						List[str]      =							"imagenet-1k-id2label.json"

	a :						Optional[int]      =							json.load(open(hf_hub_download(_lowercase       ,			_lowercase       ,			repo_type="dataset"   )       ,			"r"   )   )
	a :						str      =							{int(_lowercase   ): v for k, v in idalabel.items()}
	a :						List[str]      =							{v: k for k, v in idalabel.items()}

	a :						Dict      =							FocalNetConfig(
	    embed_dim=_lowercase       ,			depths=_lowercase       ,			focal_levels=_lowercase       ,			focal_windows=_lowercase       ,			use_conv_embed=_lowercase       ,			idalabel=_lowercase       ,			labelaid=_lowercase       ,			use_post_layernorm=_lowercase       ,			use_layerscale=_lowercase       ,			)

	return config






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]   )					->List[Any]:






	'''simple docstring'''






	if "patch_embed.proj" in name:
		a :						Any      =							name.replace("patch_embed.proj"       ,			"embeddings.patch_embeddings.projection"   )
	if "patch_embed.norm" in name:
		a :						List[str]      =							name.replace("patch_embed.norm"       ,			"embeddings.norm"   )
	if "layers" in name:
		a :						List[Any]      =							"encoder." + name
	if "encoder.layers" in name:
		a :						int      =							name.replace("encoder.layers"       ,			"encoder.stages"   )
	if "downsample.proj" in name:
		a :						Any      =							name.replace("downsample.proj"       ,			"downsample.projection"   )
	if "blocks" in name:
		a :						str      =							name.replace("blocks"       ,			"layers"   )
	if "modulation.f.weight" in name or "modulation.f.bias" in name:
		a :						Union[str, Any]      =							name.replace("modulation.f"       ,			"modulation.projection_in"   )
	if "modulation.h.weight" in name or "modulation.h.bias" in name:
		a :						Dict      =							name.replace("modulation.h"       ,			"modulation.projection_context"   )
	if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
		a :						Any      =							name.replace("modulation.proj"       ,			"modulation.projection_out"   )

	if name == "norm.weight":
		a :						str      =							"layernorm.weight"
	if name == "norm.bias":
		a :						Optional[Any]      =							"layernorm.bias"

	if "head" in name:
		a :						Tuple      =							name.replace("head"       ,			"classifier"   )
	else:
		a :						int      =							"focalnet." + name

	return name






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       Optional[Any]       ,			_lowercase		:       Tuple=False   )					->str:






	'''simple docstring'''






	a :						List[Any]      =							{
	    "focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
	    "focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
	    "focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
	    "focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
	    "focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
	    "focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
	    "focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
	    "focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
	    "focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
	    "focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
	}
	# fmt: on

	a :						str      =							model_name_to_url[model_name]
	print("Checkpoint URL: "       ,			_lowercase   )
	a :						Any      =							torch.hub.load_state_dict_from_url(_lowercase       ,			map_location="cpu"   )["model"]

	# rename keys
	for key in state_dict.copy().keys():
		a :						Any      =							state_dict.pop(_lowercase   )
		a :						Any      =							val

	a :						Any      =							get_focalnet_config(_lowercase   )
	a :						Optional[int]      =							FocalNetForImageClassification(_lowercase   )
	model.eval()

	# load state dict
	model.load_state_dict(_lowercase   )

	# verify conversion
	a :						Union[str, Any]      =							"http://images.cocodataset.org/val2017/000000039769.jpg"

	a :						Optional[int]      =							BitImageProcessor(
	    do_resize=_lowercase       ,			size={"shortest_edge": 256}       ,			resample=PILImageResampling.BILINEAR       ,			do_center_crop=_lowercase       ,			crop_size=224       ,			do_normalize=_lowercase       ,			image_mean=_lowercase       ,			image_std=_lowercase       ,			)
	a :						int      =							Image.open(requests.get(_lowercase       ,			stream=_lowercase   ).raw   )
	a :						Dict      =							processor(images=_lowercase       ,			return_tensors="pt"   )

	a :						Optional[int]      =							transforms.Compose(
	    [
	        transforms.Resize(256   ),
	        transforms.CenterCrop(224   ),
	        transforms.ToTensor(),
	        transforms.Normalize(mean=[0.485, 0.456, 0.406]       ,			std=[0.229, 0.224, 0.225]   ),
	    ]   )

	a :						str      =							image_transforms(_lowercase   ).unsqueeze(0   )

	# verify pixel_values
	assert torch.allclose(inputs.pixel_values       ,			_lowercase       ,			atol=1E-4   )

	a :						Dict      =							model(**_lowercase   )

	a :						List[str]      =							outputs.logits.argmax(-1   ).item()
	print("Predicted class:"       ,			model.config.idalabel[predicted_class_idx]   )

	print("First values of logits:"       ,			outputs.logits[0, :3]   )

	if model_name == "focalnet-tiny":
		a :						Union[str, Any]      =							torch.tensor([0.2166, -0.4368, 0.2191]   )
	elif model_name == "focalnet-tiny-lrf":
		a :						Union[str, Any]      =							torch.tensor([1.1669, 0.0125, -0.1695]   )
	elif model_name == "focalnet-small":
		a :						Dict      =							torch.tensor([0.4917, -0.0430, 0.1341]   )
	elif model_name == "focalnet-small-lrf":
		a :						Dict      =							torch.tensor([-0.2588, -0.5342, -0.2331]   )
	elif model_name == "focalnet-base":
		a :						Any      =							torch.tensor([-0.1655, -0.4090, -0.1730]   )
	elif model_name == "focalnet-base-lrf":
		a :						str      =							torch.tensor([0.5306, -0.0483, -0.3928]   )
	assert torch.allclose(outputs.logits[0, :3]       ,			_lowercase       ,			atol=1E-4   )
	print("Looks ok!"   )

	if pytorch_dump_folder_path is not None:
		print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""   )
		model.save_pretrained(_lowercase   )
		processor.save_pretrained(_lowercase   )

	if push_to_hub:
		print(F"""Pushing model and processor of {model_name} to the hub..."""   )
		model.push_to_hub(F"""{model_name}"""   )
		processor.push_to_hub(F"""{model_name}"""   )


if __name__ == "__main__":
		a		:					str	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''focalnet-tiny''',
		    type=str,
		    help='''Name of the FocalNet model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether to push the model and processor to the hub.''',
		)

		a		:					Tuple	      =     parser.parse_args()
		convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


