




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()
_lowerCamelCase :       List[str]     					=    logging.get_logger(__name__)






def  a_	( __lowercase     :			Tuple				)				->						List[str]:
			_snake_case						=    ConvNextVaConfig()

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

			_snake_case						=    1_000
			_snake_case						=    'imagenet-1k-id2label.json'
			_snake_case						=    (1, 1_000)

			_snake_case						=    'huggingface/label-files'
			_snake_case						=    num_labels
			_snake_case						=    json.load(open(hf_hub_download(__lowercase ,      __lowercase ,      repo_type='dataset'				) ,      'r'				)				)
			_snake_case						=    {int(__lowercase				): v for k, v in idalabel.items()}

			_snake_case						=    idalabel
			_snake_case						=    {v: k for k, v in idalabel.items()}
			_snake_case						=    hidden_sizes
			_snake_case						=    depths

			return config, expected_shape






def  a_	( __lowercase     :			Optional[int]				)				->						Optional[Any]:


			if "downsample_layers.0.0" in name:
						_snake_case						=    name.replace('downsample_layers.0.0' ,      'embeddings.patch_embeddings'				)
			if "downsample_layers.0.1" in name:
						_snake_case						=    name.replace('downsample_layers.0.1' ,      'embeddings.norm'				)  # we rename to layernorm later on
			if "downsample_layers.1.0" in name:
						_snake_case						=    name.replace('downsample_layers.1.0' ,      'stages.1.downsampling_layer.0'				)
			if "downsample_layers.1.1" in name:
						_snake_case						=    name.replace('downsample_layers.1.1' ,      'stages.1.downsampling_layer.1'				)
			if "downsample_layers.2.0" in name:
						_snake_case						=    name.replace('downsample_layers.2.0' ,      'stages.2.downsampling_layer.0'				)
			if "downsample_layers.2.1" in name:
						_snake_case						=    name.replace('downsample_layers.2.1' ,      'stages.2.downsampling_layer.1'				)
			if "downsample_layers.3.0" in name:
						_snake_case						=    name.replace('downsample_layers.3.0' ,      'stages.3.downsampling_layer.0'				)
			if "downsample_layers.3.1" in name:
						_snake_case						=    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.
						_snake_case						=    name[: len('stages.0'				)] + '.layers' + name[len('stages.0'				) :]
			if "gamma" in name:
						_snake_case						=    name.replace('gamma' ,      'weight'				)
			if "beta" in name:
						_snake_case						=    name.replace('beta' ,      'bias'				)
			if "stages" in name:
						_snake_case						=    name.replace('stages' ,      'encoder.stages'				)
			if "norm" in name:
						_snake_case						=    name.replace('norm' ,      'layernorm'				)
			if "head" in name:
						_snake_case						=    name.replace('head' ,      'classifier'				)

			return name






def  a_	( )				->						Any:
			_snake_case						=    'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case						=    Image.open(requests.get(__lowercase ,      stream=__lowercase				).raw				)
			return im






def  a_	( __lowercase     :			Union[str, Any]				)				->						Any:


			if "224" in checkpoint_url:
						_snake_case						=    224
						_snake_case						=    224 / 256
			elif "384" in checkpoint_url:
						_snake_case						=    384
						_snake_case						=    None
			else:
						_snake_case						=    512
						_snake_case						=    None

			return ConvNextImageProcessor(
			    size=__lowercase ,      crop_pct=__lowercase ,      image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] ,      image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ,      resample=PILImageResampling.BICUBIC ,      )






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

			print('Converting model parameters...'				)
			# rename keys
			for key in state_dict.copy().keys():
						_snake_case						=    state_dict.pop(__lowercase				)
						_snake_case						=    val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						_snake_case						=    state_dict.pop(__lowercase				)
						if not key.startswith('classifier'				):
									_snake_case						=    'convnextv2.' + key
						_snake_case						=    val

			# load HuggingFace model
			_snake_case						=    ConvNextVaForImageClassification(__lowercase				)
			model.load_state_dict(__lowercase				)
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			_snake_case						=    convert_preprocessor(__lowercase				)
			_snake_case						=    preprocessor(images=prepare_img() ,      return_tensors='pt'				)
			_snake_case						=    model(**__lowercase				).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":
						_snake_case						=    torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						_snake_case						=    torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						_snake_case						=    torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						_snake_case						=    torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						_snake_case						=    torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						_snake_case						=    torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						_snake_case						=    torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						_snake_case						=    torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						_snake_case						=    torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						_snake_case						=    torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]				)
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						_snake_case						=    torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]				)
			else:
						raise ValueError(f'''Unknown URL: {checkpoint_url}'''				)

			assert torch.allclose(logits[0, :5] ,      __lowercase ,      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(__lowercase				):
									os.mkdir(__lowercase				)

						model.save_pretrained(__lowercase				)
						preprocessor.save_pretrained(__lowercase				)

			_snake_case						=    '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(__lowercase				)
						preprocessor.push_to_hub(__lowercase				)


if __name__ == "__main__":
			_lowerCamelCase :       Dict     					=    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''')

			_lowerCamelCase :       str     					=    parser.parse_args()
			convert_convnextva_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
			)

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowerCamelCase :       Optional[Any]     					=    [nn.LayerNorm]

_lowerCamelCase :       Optional[int]     					=    logging.get_logger(__name__)

_lowerCamelCase :       Optional[int]     					=    version.parse(version.parse(torch.__version__).base_version)

_lowerCamelCase :       Dict     					=    parsed_torch_version_base >= version.parse('''2.0''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.12''')
_lowerCamelCase :       str     					=    parsed_torch_version_base >= version.parse('''1.11''')
_lowerCamelCase :       Dict     					=    parsed_torch_version_base < version.parse('''1.11''')






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Optional[int] ,      __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      self     :			Optional[int]				)				->						Dict:
			from torch import _softmax_backward_data

			if is_torch_less_than_1_11:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self				)
			else:
						return _softmax_backward_data(__lowercase ,      __lowercase ,      parent.dim ,      self.dtype				)






def  a_	( __lowercase     :			nn.Linear ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 0				)				->						nn.Linear:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if layer.bias is not None:
						if dim == 1:
									_snake_case						=    layer.bias.clone().detach()
						else:
									_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    nn.Linear(new_size[1] ,      new_size[0] ,      bias=layer.bias is not None				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			if layer.bias is not None:
						_snake_case						=    False
						new_layer.bias.copy_(b.contiguous()				)
						_snake_case						=    True
			return new_layer




class      SCREAMING_SNAKE_CASE__  (			nn.Module      ):





	'''simple docstring'''



	def __init__(						self			:					Optional[Any]    ,						lowercase			:					List[str]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				super().__init__()
				_snake_case						=    nf
				_snake_case						=    nn.Parameter(torch.empty(lowercase    ,						lowercase			)			)
				_snake_case						=    nn.Parameter(torch.zeros(lowercase			)			)
				nn.init.normal_(self.weight    ,						std=0.02			)





	def 				A							(						self			:					List[Any]    ,						lowercase			:					int			):


				'''simple docstring'''


				_snake_case						=    x.size()[:-1] + (self.nf,)
				_snake_case						=    torch.addmm(self.bias    ,						x.view(-1    ,						x.size(-1			)			)    ,						self.weight			)
				_snake_case						=    x.view(lowercase			)
				return x






def  a_	( __lowercase     :			ConvaD ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			int = 1				)				->						ConvaD:
			_snake_case						=    index.to(layer.weight.device				)
			_snake_case						=    layer.weight.index_select(__lowercase ,      __lowercase				).clone().detach()
			if dim == 0:
						_snake_case						=    layer.bias.clone().detach()
			else:
						_snake_case						=    layer.bias[index].clone().detach()
			_snake_case						=    list(layer.weight.size()				)
			_snake_case						=    len(__lowercase				)
			_snake_case						=    ConvaD(new_size[1] ,      new_size[0]				).to(layer.weight.device				)
			_snake_case						=    False
			new_layer.weight.copy_(W.contiguous()				)
			_snake_case						=    True
			_snake_case						=    False
			new_layer.bias.copy_(b.contiguous()				)
			_snake_case						=    True
			return new_layer






def  a_	( __lowercase     :			Union[nn.Linear, ConvaD] ,      __lowercase     :			torch.LongTensor ,      __lowercase     :			Optional[int] = None				)				->						Union[nn.Linear, ConvaD]:
			if isinstance(__lowercase ,      nn.Linear				):
						return prune_linear_layer(__lowercase ,      __lowercase ,      dim=0 if dim is None else dim				)
			elif isinstance(__lowercase ,      __lowercase				):
						return prune_convad_layer(__lowercase ,      __lowercase ,      dim=1 if dim is None else dim				)
			else:
						raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''				)






def  a_	( __lowercase     :			Callable[..., torch.Tensor] ,      __lowercase     :			int ,      __lowercase     :			int ,      *__lowercase     :			List[str]				)				->						torch.Tensor:
			assert len(__lowercase				) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

			# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
			_snake_case						=    len(inspect.signature(__lowercase				).parameters				)
			if num_args_in_forward_chunk_fn != len(__lowercase				):
						raise ValueError(
						    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowercase				)} input '''
						    'tensors are given'				)

			if chunk_size > 0:
						_snake_case						=    input_tensors[0].shape[chunk_dim]
						for input_tensor in input_tensors:
									if input_tensor.shape[chunk_dim] != tensor_shape:
												raise ValueError(
												    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
												    f'''found shape {input_tensor.shape[chunk_dim]}'''				)

						if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
									raise ValueError(
									    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
									    f'''size {chunk_size}'''				)

						_snake_case						=    input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						_snake_case						=    tuple(input_tensor.chunk(__lowercase ,      dim=__lowercase				) for input_tensor in input_tensors				)
						# apply forward fn to every tuple
						_snake_case						=    tuple(forward_fn(*__lowercase				) for input_tensors_chunk in zip(*__lowercase				)				)
						# concatenate output at same dimension
						return torch.cat(__lowercase ,      dim=__lowercase				)

			return forward_fn(*__lowercase				)






def  a_	( __lowercase     :			List[int] ,      __lowercase     :			int ,      __lowercase     :			int ,      __lowercase     :			Set[int]				)				->						Tuple[Set[int], torch.LongTensor]:
			_snake_case						=    torch.ones(__lowercase ,      __lowercase				)
			_snake_case						=    set(__lowercase				) - already_pruned_heads  # Convert to set and remove already pruned heads
			for head in heads:
						# Compute how many pruned heads are before the head and move the index accordingly
						_snake_case						=    head - sum(1 if h < head else 0 for h in already_pruned_heads				)
						_snake_case						=    0
			_snake_case						=    mask.view(-1				).contiguous().eq(1				)
			_snake_case						=    torch.arange(len(__lowercase				)				)[mask].long()
			return heads, index






def  a_	( *__lowercase     :			Union[torch.Tensor, List[torch.Tensor]] ,      __lowercase     :			Optional[str] = None				)				->						Tuple[torch.Tensor, ...]:
			return torch.meshgrid(*__lowercase ,      indexing=__lowercase				)






def  a_	( __lowercase     :			torch.Tensor				)				->						Tuple[torch.device, int, int]:
			return tensor.device, storage_ptr(__lowercase				), storage_size(__lowercase				)