


'''simple docstring'''

import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict=False		) ->    Tuple:





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[]
	for i in range(config.num_hidden_layers		):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((f'module.blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')		)
		rename_keys.append((f'module.blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')		)
		rename_keys.append(
		    (f'module.blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')		)
		rename_keys.append((f'module.blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')		)
		rename_keys.append((f'module.blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')		)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('module.cls_token', 'vit.embeddings.cls_token'),
	        ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
	        ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
	        ('module.pos_embed', 'vit.embeddings.position_embeddings'),
	    ]		)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('module.norm.weight', 'layernorm.weight'),
		        ('module.norm.bias', 'layernorm.bias'),
		    ]		)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__magic_name__	:   Dict					     =					[(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('norm.weight', 'vit.layernorm.weight'),
		        ('norm.bias', 'vit.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]		)

	return rename_keys





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any=False		) ->    List[str]:





	"""simple docstring"""







	for i in range(config.num_hidden_layers		):
		if base_model:
			__magic_name__	:   int					     =					''
		else:
			__magic_name__	:   int					     =					'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__magic_name__	:   List[Any]					     =					state_dict.pop(f'module.blocks.{i}.attn.qkv.weight'		)
		__magic_name__	:   Dict					     =					state_dict.pop(f'module.blocks.{i}.attn.qkv.bias'		)
		# next, add query, keys and values (in that order) to the state dict
		__magic_name__	:   List[str]					     =					in_proj_weight[
		    : config.hidden_size, :
		]
		__magic_name__	:   str					     =					in_proj_bias[: config.hidden_size]
		__magic_name__	:   int					     =					in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__magic_name__	:   Any					     =					in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__magic_name__	:   List[Any]					     =					in_proj_weight[
		    -config.hidden_size :, :
		]
		__magic_name__	:   List[Any]					     =					in_proj_bias[-config.hidden_size :]





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		) ->    Dict:





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		) ->    Union[str, Any]:





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					[
	    'module.fc.fc1.weight',
	    'module.fc.fc1.bias',
	    'module.fc.bn1.weight',
	    'module.fc.bn1.bias',
	    'module.fc.bn1.running_mean',
	    'module.fc.bn1.running_var',
	    'module.fc.bn1.num_batches_tracked',
	    'module.fc.fc2.weight',
	    'module.fc.fc2.bias',
	    'module.fc.bn2.weight',
	    'module.fc.bn2.bias',
	    'module.fc.bn2.running_mean',
	    'module.fc.bn2.running_var',
	    'module.fc.bn2.num_batches_tracked',
	    'module.fc.fc3.weight',
	    'module.fc.fc3.bias',
	]
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Dict		) ->    List[Any]:





	"""simple docstring"""







	__magic_name__	:   Dict					     =					dct.pop(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					val





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Optional[Any]		) ->    Any:





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					ViTMSNConfig()
	__magic_name__	:   List[Any]					     =					1000

	__magic_name__	:   Optional[int]					     =					'datasets/huggingface/label-files'
	__magic_name__	:   Optional[Any]					     =					'imagenet-1k-id2label.json'
	__magic_name__	:   Any					     =					json.load(open(hf_hub_download(lowerCAmelCase		,		lowerCAmelCase		)		,		'r'		)		)
	__magic_name__	:   Optional[int]					     =					{int(lowerCAmelCase		): v for k, v in idalabel.items()}
	__magic_name__	:   List[Any]					     =					idalabel
	__magic_name__	:   int					     =					{v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		__magic_name__	:   str					     =					384
		__magic_name__	:   Dict					     =					1536
		__magic_name__	:   str					     =					6
	elif "l16" in checkpoint_url:
		__magic_name__	:   Tuple					     =					1024
		__magic_name__	:   List[str]					     =					4096
		__magic_name__	:   Union[str, Any]					     =					24
		__magic_name__	:   Optional[int]					     =					16
		__magic_name__	:   List[str]					     =					0.1
	elif "b4" in checkpoint_url:
		__magic_name__	:   Optional[int]					     =					4
	elif "l7" in checkpoint_url:
		__magic_name__	:   Tuple					     =					7
		__magic_name__	:   str					     =					1024
		__magic_name__	:   int					     =					4096
		__magic_name__	:   List[str]					     =					24
		__magic_name__	:   Tuple					     =					16
		__magic_name__	:   Dict					     =					0.1

	__magic_name__	:   Optional[int]					     =					ViTMSNModel(lowerCAmelCase		)

	__magic_name__	:   Dict					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase		,		map_location='cpu'		)['target_encoder']

	__magic_name__	:   Union[str, Any]					     =					ViTImageProcessor(size=config.image_size		)

	remove_projection_head(lowerCAmelCase		)
	__magic_name__	:   Any					     =					create_rename_keys(lowerCAmelCase		,		base_model=lowerCAmelCase		)

	for src, dest in rename_keys:
		rename_key(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
	read_in_q_k_v(lowerCAmelCase		,		lowerCAmelCase		,		base_model=lowerCAmelCase		)

	model.load_state_dict(lowerCAmelCase		)
	model.eval()

	__magic_name__	:   Tuple					     =					'http://images.cocodataset.org/val2017/000000039769.jpg'

	__magic_name__	:   Optional[Any]					     =					Image.open(requests.get(lowerCAmelCase		,		stream=lowerCAmelCase		).raw		)
	__magic_name__	:   Optional[Any]					     =					ViTImageProcessor(
	    size=config.image_size		,		image_mean=lowerCAmelCase		,		image_std=lowerCAmelCase		)
	__magic_name__	:   Any					     =					image_processor(images=lowerCAmelCase		,		return_tensors='pt'		)

	# forward pass
	torch.manual_seed(2		)
	__magic_name__	:   str					     =					model(**lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					outputs.last_hidden_state

	# The following Colab Notebook was used to generate these outputs:
	# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
	if "s16" in checkpoint_url:
		__magic_name__	:   Dict					     =					torch.tensor([[-1.0915, -1.4876, -1.1809]]		)
	elif "b16" in checkpoint_url:
		__magic_name__	:   Tuple					     =					torch.tensor([[14.2889, -18.9045, 11.7281]]		)
	elif "l16" in checkpoint_url:
		__magic_name__	:   str					     =					torch.tensor([[41.5028, -22.8681, 45.6475]]		)
	elif "b4" in checkpoint_url:
		__magic_name__	:   Union[str, Any]					     =					torch.tensor([[-4.3868, 5.2932, -0.4137]]		)
	else:
		__magic_name__	:   Optional[Any]					     =					torch.tensor([[-0.1792, -0.6465, 2.4263]]		)

	# verify logits
	assert torch.allclose(last_hidden_state[:, 0, :3]		,		lowerCAmelCase		,		atol=1e-4		)

	print(f'Saving model to {pytorch_dump_folder_path}'		)
	model.save_pretrained(lowerCAmelCase		)

	print(f'Saving image processor to {pytorch_dump_folder_path}'		)
	image_processor.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:str        =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
							    type=str,
							    help='''URL of the checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							lowerCAmelCase				:str        =      parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      int				   =    ["""pixel_values"""]







		def __init__( self		:						Any  ,					_A		:						bool = True  ,					_A		:						Optional[Dict[str, int]] = None  ,					_A		:						PILImageResampling = PILImageResampling.BILINEAR  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						bool = True  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Optional[Any]  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'shortest_edge': 256}
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   List[str]					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[Any]					     =					size
			__magic_name__	:   List[str]					     =					resample
			__magic_name__	:   Dict					     =					do_center_crop
			__magic_name__	:   List[str]					     =					crop_size
			__magic_name__	:   int					     =					do_rescale
			__magic_name__	:   Tuple					     =					rescale_factor
			__magic_name__	:   List[str]					     =					do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   Dict					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[str]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			if "shortest_edge" not in size:
				raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
			__magic_name__	:   Dict					     =					get_resize_output_image_size(_A  ,					size=size['shortest_edge']  ,					default_to_square=_A      )
			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Optional[int]  ,					)  ->			np.ndarray:
			__magic_name__	:   int					     =					get_size_dict(_A      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						float  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Tuple      )  ->			np.ndarray:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						ImageInput  ,					_A		:						Optional[bool] = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[float] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[str, TensorType]] = None  ,					_A		:						Union[str, ChannelDimension] = ChannelDimension.FIRST  ,					**_A		:						List[Any]  ,					)  ->			List[str]:
			__magic_name__	:   int					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					size if size is not None else self.size
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[Any]					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Dict					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Any					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Tuple					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Union[str, Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   int					     =					make_list_of_images(_A      )

			if not valid_images(_A      ):
				raise ValueError(
				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
				    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

			if do_resize and size is None:
				raise ValueError('Size must be specified if do_resize is True.'      )

			if do_center_crop and crop_size is None:
				raise ValueError('Crop size must be specified if do_center_crop is True.'      )

			if do_rescale and rescale_factor is None:
				raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

			# All transformations expect numpy arrays.
			__magic_name__	:   List[Any]					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Union[str, Any]					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_center_crop:
				__magic_name__	:   Union[str, Any]					     =					[self.center_crop(image=_A  ,					size=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   List[Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

			if do_normalize:
				__magic_name__	:   Optional[Any]					     =					[self.normalize(image=_A  ,					mean=_A  ,					std=_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   List[str]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )