


import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase		=						logging.get_logger(__name__)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Tuple       	=   ConvNextConfig()

		if "tiny" in checkpoint_url:
				lowerCAmelCase__  :     str       	=   [3, 3, 9, 3]
				lowerCAmelCase__  :     Tuple       	=   [96, 192, 384, 768]
		if "small" in checkpoint_url:
				lowerCAmelCase__  :     Tuple       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     Optional[int]       	=   [96, 192, 384, 768]
		if "base" in checkpoint_url:
				lowerCAmelCase__  :     Dict       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     Tuple       	=   [128, 256, 512, 1_024]
		if "large" in checkpoint_url:
				lowerCAmelCase__  :     List[str]       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     Any       	=   [192, 384, 768, 1_536]
		if "xlarge" in checkpoint_url:
				lowerCAmelCase__  :     Union[str, Any]       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     List[Any]       	=   [256, 512, 1_024, 2_048]

		if "1k" in checkpoint_url:
				lowerCAmelCase__  :     Any       	=   1_000
				lowerCAmelCase__  :     str       	=   '''imagenet-1k-id2label.json'''
				lowerCAmelCase__  :     str       	=   (1, 1_000)
		else:
				lowerCAmelCase__  :     str       	=   21_841
				lowerCAmelCase__  :     List[Any]       	=   '''imagenet-22k-id2label.json'''
				lowerCAmelCase__  :     List[Any]       	=   (1, 21_841)

		lowerCAmelCase__  :     Optional[int]       	=   '''huggingface/label-files'''
		lowerCAmelCase__  :     Union[str, Any]       	=   num_labels
		lowerCAmelCase__  :     Optional[int]       	=   json.load(open(hf_hub_download(_a      ,  _a      ,  repo_type='''dataset'''			)      ,  '''r'''			)			)
		lowerCAmelCase__  :     int       	=   {int(_a			): v for k, v in idalabel.items()}
		if "1k" not in checkpoint_url:
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[9_205]
				del idalabel[15_027]
		lowerCAmelCase__  :     Optional[Any]       	=   idalabel
		lowerCAmelCase__  :     List[Any]       	=   {v: k for k, v in idalabel.items()}
		lowerCAmelCase__  :     int       	=   hidden_sizes
		lowerCAmelCase__  :     List[str]       	=   depths

		return config, expected_shape






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if "downsample_layers.0.0" in name:
				lowerCAmelCase__  :     int       	=   name.replace('''downsample_layers.0.0'''      ,  '''embeddings.patch_embeddings'''			)
		if "downsample_layers.0.1" in name:
				lowerCAmelCase__  :     List[Any]       	=   name.replace('''downsample_layers.0.1'''      ,  '''embeddings.norm'''			)  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				lowerCAmelCase__  :     Tuple       	=   name.replace('''downsample_layers.1.0'''      ,  '''stages.1.downsampling_layer.0'''			)
		if "downsample_layers.1.1" in name:
				lowerCAmelCase__  :     Any       	=   name.replace('''downsample_layers.1.1'''      ,  '''stages.1.downsampling_layer.1'''			)
		if "downsample_layers.2.0" in name:
				lowerCAmelCase__  :     List[Any]       	=   name.replace('''downsample_layers.2.0'''      ,  '''stages.2.downsampling_layer.0'''			)
		if "downsample_layers.2.1" in name:
				lowerCAmelCase__  :     Any       	=   name.replace('''downsample_layers.2.1'''      ,  '''stages.2.downsampling_layer.1'''			)
		if "downsample_layers.3.0" in name:
				lowerCAmelCase__  :     List[Any]       	=   name.replace('''downsample_layers.3.0'''      ,  '''stages.3.downsampling_layer.0'''			)
		if "downsample_layers.3.1" in name:
				lowerCAmelCase__  :     List[Any]       	=   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.
				lowerCAmelCase__  :     Optional[Any]       	=   name[: len('''stages.0'''			)] + '''.layers''' + name[len('''stages.0'''			) :]
		if "stages" in name:
				lowerCAmelCase__  :     int       	=   name.replace('''stages'''      ,  '''encoder.stages'''			)
		if "norm" in name:
				lowerCAmelCase__  :     Any       	=   name.replace('''norm'''      ,  '''layernorm'''			)
		if "gamma" in name:
				lowerCAmelCase__  :     Dict       	=   name.replace('''gamma'''      ,  '''layer_scale_parameter'''			)
		if "head" in name:
				lowerCAmelCase__  :     str       	=   name.replace('''head'''      ,  '''classifier'''			)

		return name






def 	lowerCamelCase_       (			):




		"""simple docstring"""






		lowerCAmelCase__  :     str       	=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__  :     str       	=   Image.open(requests.get(_a      ,  stream=_a			).raw			)
		return im






@torch.no_grad()
def 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__	,      lowerCAmelCase__  :     List[str]       	=   get_convnext_config(_a			)
		# load original state_dict from URL
		lowerCAmelCase__  :     List[Any]       	=   torch.hub.load_state_dict_from_url(_a			)['''model''']
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase__  :     Optional[Any]       	=   state_dict.pop(_a			)
				lowerCAmelCase__  :     Optional[int]       	=   val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCAmelCase__  :     Any       	=   state_dict.pop(_a			)
				if not key.startswith('''classifier'''			):
						lowerCAmelCase__  :     Dict       	=   '''convnext.''' + key
				lowerCAmelCase__  :     Optional[int]       	=   val

		# load HuggingFace model
		lowerCAmelCase__  :     str       	=   ConvNextForImageClassification(_a			)
		model.load_state_dict(_a			)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCAmelCase__  :     List[str]       	=   224 if '''224''' in checkpoint_url else 384
		lowerCAmelCase__  :     Tuple       	=   ConvNextImageProcessor(size=_a			)
		lowerCAmelCase__  :     str       	=   image_processor(images=prepare_img()      ,  return_tensors='''pt'''			).pixel_values

		lowerCAmelCase__  :     Optional[Any]       	=   model(_a			).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
				lowerCAmelCase__  :     int       	=   torch.tensor([-0.12_10, -0.66_05, 0.19_18]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
				lowerCAmelCase__  :     Dict       	=   torch.tensor([-0.44_73, -0.18_47, -0.63_65]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([0.45_25, 0.75_39, 0.03_08]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
				lowerCAmelCase__  :     str       	=   torch.tensor([0.35_61, 0.63_50, -0.03_84]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([0.41_74, -0.09_89, 0.14_89]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
				lowerCAmelCase__  :     Dict       	=   torch.tensor([0.25_13, -0.13_49, -0.16_13]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
				lowerCAmelCase__  :     str       	=   torch.tensor([1.29_80, 0.36_31, -0.11_98]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
				lowerCAmelCase__  :     int       	=   torch.tensor([1.29_63, 0.12_27, 0.17_23]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([1.79_56, 0.83_90, 0.28_20]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
				lowerCAmelCase__  :     int       	=   torch.tensor([-0.28_22, -0.05_02, -0.08_78]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([-0.56_72, -0.07_30, -0.43_48]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([0.26_81, 0.23_65, 0.62_46]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([-0.26_42, 0.39_31, 0.51_16]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
				lowerCAmelCase__  :     Any       	=   torch.tensor([-0.66_77, -0.18_73, -0.83_79]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
				lowerCAmelCase__  :     List[Any]       	=   torch.tensor([-0.77_49, -0.29_67, -0.64_44]			)
		else:
				raise ValueError(f'Unknown URL: {checkpoint_url}'			)

		assert torch.allclose(logits[0, :3]      ,  _a      ,  atol=1e-3			)
		assert logits.shape == expected_shape

		Path(_a			).mkdir(exist_ok=_a			)
		print(f'Saving model to {pytorch_dump_folder_path}'			)
		model.save_pretrained(_a			)
		print(f'Saving image processor to {pytorch_dump_folder_path}'			)
		image_processor.save_pretrained(_a			)

		print('''Pushing model to the hub...'''			)
		lowerCAmelCase__  :     Union[str, Any]       	=   '''convnext'''
		if "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "small" in checkpoint_url:
				model_name += "-small"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "xlarge" in checkpoint_url:
				model_name += "-xlarge"
		elif "large" in checkpoint_url:
				model_name += "-large"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		if "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"

		model.push_to_hub(
		    repo_path_or_name=Path(_a      ,  _a			)      ,  organization='''nielsr'''      ,  commit_message='''Add model'''      ,  )


if __name__ == "__main__":
		lowerCamelCase		=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
		    type=str,
		    help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)

		lowerCamelCase		=						parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




