






"""simple docstring"""







import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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





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

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

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

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										a__          =  [(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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(config.num_hidden_layers):
										if base_model:
															a__          =  ''''''
										else:
															a__          =  '''vit.'''
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										a__          =  state_dict.pop(f'blocks.{i}.attn.qkv.weight')
										a__          =  state_dict.pop(f'blocks.{i}.attn.qkv.bias')
										# next, add query, keys and values (in that order) to the state dict
										a__          =  in_proj_weight[
										    : config.hidden_size, :
										]
										a__          =  in_proj_bias[: config.hidden_size]
										a__          =  in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										a__          =  in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										a__          =  in_proj_weight[
										    -config.hidden_size :, :
										]
										a__          =  in_proj_bias[-config.hidden_size :]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  ['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  ViTConfig()
					a__          =  False
					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					if vit_name[-5:] == "in21k":
										a__          =  True
										a__          =  int(vit_name[-12:-10])
										a__          =  int(vit_name[-9:-6])
					else:
										a__          =  1000
										a__          =  '''huggingface/label-files'''
										a__          =  '''imagenet-1k-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}
										a__          =  int(vit_name[-6:-4])
										a__          =  int(vit_name[-3:])
					# size of the architecture
					if "deit" in vit_name:
										if vit_name[9:].startswith('''tiny'''):
															a__          =  192
															a__          =  768
															a__          =  12
															a__          =  3
										elif vit_name[9:].startswith('''small'''):
															a__          =  384
															a__          =  1536
															a__          =  12
															a__          =  6
										else:
															pass
					else:
										if vit_name[4:].startswith('''small'''):
															a__          =  768
															a__          =  2304
															a__          =  8
															a__          =  8
										elif vit_name[4:].startswith('''base'''):
															pass
										elif vit_name[4:].startswith('''large'''):
															a__          =  1024
															a__          =  4096
															a__          =  24
															a__          =  16
										elif vit_name[4:].startswith('''huge'''):
															a__          =  1280
															a__          =  5120
															a__          =  32
															a__          =  16

    # load original model from timm
					a__          =  timm.create_model(lowerCamelCase_ ,     pretrained=lowerCamelCase_)
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					a__          =  timm_model.state_dict()
					if base_model:
										remove_classification_head_(lowerCamelCase_)
					a__          =  create_rename_keys(lowerCamelCase_ ,     lowerCamelCase_)
					for src, dest in rename_keys:
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					read_in_q_k_v(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

					# load HuggingFace model
					if vit_name[-5:] == "in21k":
										a__          =  ViTModel(lowerCamelCase_).eval()
					else:
										a__          =  ViTForImageClassification(lowerCamelCase_).eval()
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
					if "deit" in vit_name:
										a__          =  DeiTImageProcessor(size=config.image_size)
					else:
										a__          =  ViTImageProcessor(size=config.image_size)
					a__          =  image_processor(images=prepare_img() ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']
					a__          =  model(lowerCamelCase_)

					if base_model:
										a__          =  timm_model.forward_features(lowerCamelCase_)
										assert timm_pooled_output.shape == outputs.pooler_output.shape
										assert torch.allclose(lowerCamelCase_ ,     outputs.pooler_output ,     atol=1E-3)
					else:
										a__          =  timm_model(lowerCamelCase_)
										assert timm_logits.shape == outputs.logits.shape
										assert torch.allclose(lowerCamelCase_ ,     outputs.logits ,     atol=1E-3)

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {vit_name} 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__":
	__a       :	List[Any]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--vit_name',
	    default='vit_base_patch16_224',
	    type=str,
	    help='Name of the ViT timm 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.'
	)

	__a       :	Tuple             = parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


