






import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_			:Tuple	    =		logging.get_logger(__name__)






def 				A	(			a_   )	->		List[Any]:
					__UpperCamelCase :   Optional[Any]									=ConvNextVaConfig()

					if "atto" in checkpoint_url:
										__UpperCamelCase :   Tuple									=[2, 2, 6, 2]
										__UpperCamelCase :   List[str]									=[40, 80, 160, 320]
					if "femto" in checkpoint_url:
										__UpperCamelCase :   Optional[Any]									=[2, 2, 6, 2]
										__UpperCamelCase :   Any									=[48, 96, 192, 384]
					if "pico" in checkpoint_url:
										__UpperCamelCase :   Any									=[2, 2, 6, 2]
										__UpperCamelCase :   Any									=[64, 128, 256, 512]
					if "nano" in checkpoint_url:
										__UpperCamelCase :   List[str]									=[2, 2, 8, 2]
										__UpperCamelCase :   Tuple									=[80, 160, 320, 640]
					if "tiny" in checkpoint_url:
										__UpperCamelCase :   int									=[3, 3, 9, 3]
										__UpperCamelCase :   Optional[int]									=[96, 192, 384, 768]
					if "base" in checkpoint_url:
										__UpperCamelCase :   Optional[int]									=[3, 3, 27, 3]
										__UpperCamelCase :   str									=[128, 256, 512, 1_024]
					if "large" in checkpoint_url:
										__UpperCamelCase :   Tuple									=[3, 3, 27, 3]
										__UpperCamelCase :   str									=[192, 384, 768, 1_536]
					if "huge" in checkpoint_url:
										__UpperCamelCase :   int									=[3, 3, 27, 3]
										__UpperCamelCase :   int									=[352, 704, 1_408, 2_816]

					__UpperCamelCase :   Union[str, Any]									=1_000
					__UpperCamelCase :   int									='imagenet-1k-id2label.json'
					__UpperCamelCase :   Any									=(1, 1_000)

					__UpperCamelCase :   str									='huggingface/label-files'
					__UpperCamelCase :   str									=num_labels
					__UpperCamelCase :   Tuple									=json.load(open(hf_hub_download(a_				,a_				,repo_type='dataset'   )				,'r'   )   )
					__UpperCamelCase :   Optional[Any]									={int(a_   ): v for k, v in idalabel.items()}

					__UpperCamelCase :   Tuple									=idalabel
					__UpperCamelCase :   Union[str, Any]									={v: k for k, v in idalabel.items()}
					__UpperCamelCase :   List[str]									=hidden_sizes
					__UpperCamelCase :   str									=depths

					return config, expected_shape






def 				A	(			a_   )	->		Tuple:

					if "downsample_layers.0.0" in name:
										__UpperCamelCase :   Tuple									=name.replace('downsample_layers.0.0'				,'embeddings.patch_embeddings'   )
					if "downsample_layers.0.1" in name:
										__UpperCamelCase :   str									=name.replace('downsample_layers.0.1'				,'embeddings.norm'   )  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										__UpperCamelCase :   Tuple									=name.replace('downsample_layers.1.0'				,'stages.1.downsampling_layer.0'   )
					if "downsample_layers.1.1" in name:
										__UpperCamelCase :   Dict									=name.replace('downsample_layers.1.1'				,'stages.1.downsampling_layer.1'   )
					if "downsample_layers.2.0" in name:
										__UpperCamelCase :   Optional[Any]									=name.replace('downsample_layers.2.0'				,'stages.2.downsampling_layer.0'   )
					if "downsample_layers.2.1" in name:
										__UpperCamelCase :   Optional[Any]									=name.replace('downsample_layers.2.1'				,'stages.2.downsampling_layer.1'   )
					if "downsample_layers.3.0" in name:
										__UpperCamelCase :   Tuple									=name.replace('downsample_layers.3.0'				,'stages.3.downsampling_layer.0'   )
					if "downsample_layers.3.1" in name:
										__UpperCamelCase :   int									=name.replace('downsample_layers.3.1'				,'stages.3.downsampling_layer.1'   )
					if "stages" in name and "downsampling_layer" not in name:
										# stages.0.0. for instance should be renamed to stages.0.layers.0.
										__UpperCamelCase :   Union[str, Any]									=name[: len('stages.0'   )] + '.layers' + name[len('stages.0'   ) :]
					if "gamma" in name:
										__UpperCamelCase :   Union[str, Any]									=name.replace('gamma'				,'weight'   )
					if "beta" in name:
										__UpperCamelCase :   Any									=name.replace('beta'				,'bias'   )
					if "stages" in name:
										__UpperCamelCase :   str									=name.replace('stages'				,'encoder.stages'   )
					if "norm" in name:
										__UpperCamelCase :   List[str]									=name.replace('norm'				,'layernorm'   )
					if "head" in name:
										__UpperCamelCase :   List[Any]									=name.replace('head'				,'classifier'   )

					return name






def 				A	(			)	->		Dict:
					__UpperCamelCase :   List[Any]									='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase :   Union[str, Any]									=Image.open(requests.get(a_				,stream=a_   ).raw   )
					return im






def 				A	(			a_   )	->		Tuple:

					if "224" in checkpoint_url:
										__UpperCamelCase :   int									=224
										__UpperCamelCase :   Tuple									=224 / 256
					elif "384" in checkpoint_url:
										__UpperCamelCase :   str									=384
										__UpperCamelCase :   List[Any]									=None
					else:
										__UpperCamelCase :   Union[str, Any]									=512
										__UpperCamelCase :   List[str]									=None

					return ConvNextImageProcessor(
					    size=a_				,crop_pct=a_				,image_mean=[0.485, 0.456, 0.406]				,image_std=[0.229, 0.224, 0.225]				,resample=PILImageResampling.BICUBIC				,)






@torch.no_grad()
def 				A	(			a_				,a_				,a_				,a_   )	->		Any:
					print('Downloading original model from checkpoint...'   )
					# define ConvNeXTV2 configuration based on URL
					__UpperCamelCase						,							__UpperCamelCase :   List[Any]									=get_convnextva_config(a_   )
					# load original state_dict from URL
					__UpperCamelCase :   Union[str, Any]									=torch.hub.load_state_dict_from_url(a_   )['model']

					print('Converting model parameters...'   )
					# rename keys
					for key in state_dict.copy().keys():
										__UpperCamelCase :   Dict									=state_dict.pop(a_   )
										__UpperCamelCase :   Optional[Any]									=val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										__UpperCamelCase :   int									=state_dict.pop(a_   )
										if not key.startswith('classifier'   ):
															__UpperCamelCase :   List[str]									='convnextv2.' + key
										__UpperCamelCase :   Optional[Any]									=val

					# load HuggingFace model
					__UpperCamelCase :   str									=ConvNextVaForImageClassification(a_   )
					model.load_state_dict(a_   )
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					__UpperCamelCase :   str									=convert_preprocessor(a_   )
					__UpperCamelCase :   List[str]									=preprocessor(images=prepare_img()				,return_tensors='pt'   )
					__UpperCamelCase :   Any									=model(**a_   ).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
										__UpperCamelCase :   List[str]									=torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
										__UpperCamelCase :   List[str]									=torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
										__UpperCamelCase :   str									=torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
										__UpperCamelCase :   Any									=torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
										__UpperCamelCase :   Dict									=torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
										__UpperCamelCase :   Dict									=torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
										__UpperCamelCase :   Optional[Any]									=torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
										__UpperCamelCase :   Tuple									=torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
										__UpperCamelCase :   Optional[Any]									=torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
										__UpperCamelCase :   Union[str, Any]									=torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
										__UpperCamelCase :   List[Any]									=torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
										__UpperCamelCase :   int									=torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
										__UpperCamelCase :   Optional[Any]									=torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
										__UpperCamelCase :   Dict									=torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
										__UpperCamelCase :   str									=torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
										__UpperCamelCase :   str									=torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
										__UpperCamelCase :   Any									=torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]   )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
										__UpperCamelCase :   str									=torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]   )
					else:
										raise ValueError(F'Unknown URL: {checkpoint_url}'   )

					assert torch.allclose(logits[0, :5]				,a_				,atol=1e-3   )
					assert logits.shape == expected_shape
					print('Model outputs match the original results!'   )

					if save_model:
										print('Saving model to local...'   )
										# Create folder to save model
										if not os.path.isdir(a_   ):
															os.mkdir(a_   )

										model.save_pretrained(a_   )
										preprocessor.save_pretrained(a_   )

					__UpperCamelCase :   List[Any]									='convnextv2'
					if "atto" in checkpoint_url:
										model_name += "-atto"
					if "femto" in checkpoint_url:
										model_name += "-femto"
					if "pico" in checkpoint_url:
										model_name += "-pico"
					if "nano" in checkpoint_url:
										model_name += "-nano"
					elif "tiny" in checkpoint_url:
										model_name += "-tiny"
					elif "base" in checkpoint_url:
										model_name += "-base"
					elif "large" in checkpoint_url:
										model_name += "-large"
					elif "huge" in checkpoint_url:
										model_name += "-huge"
					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
										model_name += "-22k"
					elif "22k" in checkpoint_url and "1k" in checkpoint_url:
										model_name += "-22k-1k"
					elif "1k" in checkpoint_url:
										model_name += "-1k"
					if "224" in checkpoint_url:
										model_name += "-224"
					elif "384" in checkpoint_url:
										model_name += "-384"
					elif "512" in checkpoint_url:
										model_name += "-512"

					if push_to_hub:
										print(F'Pushing {model_name} to the hub...'   )
										model.push_to_hub(a_   )
										preprocessor.push_to_hub(a_   )


if __name__ == "__main__":
							A_			:Optional[Any]	    =		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
							    type=str,
							    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''model''',
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

							A_			:int	    =		parser.parse_args()
							convert_convnextva_checkpoint(
							    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
							)



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
