
'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:






	if "cls_token" in name:
		_snake_case   						=		name.replace('cls_token'			,				'vit.embeddings.cls_token'	)
	if "mask_token" in name:
		_snake_case   						=		name.replace('mask_token'			,				'decoder.mask_token'	)
	if "decoder_pos_embed" in name:
		_snake_case   						=		name.replace('decoder_pos_embed'			,				'decoder.decoder_pos_embed'	)
	if "pos_embed" in name and "decoder" not in name:
		_snake_case   						=		name.replace('pos_embed'			,				'vit.embeddings.position_embeddings'	)
	if "patch_embed.proj" in name:
		_snake_case   						=		name.replace('patch_embed.proj'			,				'vit.embeddings.patch_embeddings.projection'	)
	if "patch_embed.norm" in name:
		_snake_case   						=		name.replace('patch_embed.norm'			,				'vit.embeddings.norm'	)
	if "decoder_blocks" in name:
		_snake_case   						=		name.replace('decoder_blocks'			,				'decoder.decoder_layers'	)
	if "blocks" in name:
		_snake_case   						=		name.replace('blocks'			,				'vit.encoder.layer'	)
	if "attn.proj" in name:
		_snake_case   						=		name.replace('attn.proj'			,				'attention.output.dense'	)
	if "attn" in name:
		_snake_case   						=		name.replace('attn'			,				'attention.self'	)
	if "norm1" in name:
		_snake_case   						=		name.replace('norm1'			,				'layernorm_before'	)
	if "norm2" in name:
		_snake_case   						=		name.replace('norm2'			,				'layernorm_after'	)
	if "mlp.fc1" in name:
		_snake_case   						=		name.replace('mlp.fc1'			,				'intermediate.dense'	)
	if "mlp.fc2" in name:
		_snake_case   						=		name.replace('mlp.fc2'			,				'output.dense'	)
	if "decoder_embed" in name:
		_snake_case   						=		name.replace('decoder_embed'			,				'decoder.decoder_embed'	)
	if "decoder_norm" in name:
		_snake_case   						=		name.replace('decoder_norm'			,				'decoder.decoder_norm'	)
	if "decoder_pred" in name:
		_snake_case   						=		name.replace('decoder_pred'			,				'decoder.decoder_pred'	)
	if "norm.weight" in name and "decoder" not in name:
		_snake_case   						=		name.replace('norm.weight'			,				'vit.layernorm.weight'	)
	if "norm.bias" in name and "decoder" not in name:
		_snake_case   						=		name.replace('norm.bias'			,				'vit.layernorm.bias'	)

	return name







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[Any]:






	for key in orig_state_dict.copy().keys():
		_snake_case   						=		orig_state_dict.pop(__A	)

		if "qkv" in key:
			_snake_case   						=		key.split('.'	)
			_snake_case   						=		int(key_split[1]	)
			if "decoder_blocks" in key:
				_snake_case   						=		config.decoder_hidden_size
				_snake_case   						=		'decoder.decoder_layers.'
				if "weight" in key:
					_snake_case   						=		val[:dim, :]
					_snake_case   						=		val[dim : dim * 2, :]
					_snake_case   						=		val[-dim:, :]
				elif "bias" in key:
					_snake_case   						=		val[:dim]
					_snake_case   						=		val[dim : dim * 2]
					_snake_case   						=		val[-dim:]
			else:
				_snake_case   						=		config.hidden_size
				_snake_case   						=		'vit.encoder.layer.'
				if "weight" in key:
					_snake_case   						=		val[:dim, :]
					_snake_case   						=		val[dim : dim * 2, :]
					_snake_case   						=		val[-dim:, :]
				elif "bias" in key:
					_snake_case   						=		val[:dim]
					_snake_case   						=		val[dim : dim * 2]
					_snake_case   						=		val[-dim:]

		else:
			_snake_case   						=		val

	return orig_state_dict







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Any:
	_snake_case   						=		ViTMAEConfig()
	if "large" in checkpoint_url:
		_snake_case   						=		1_024
		_snake_case   						=		4_096
		_snake_case   						=		24
		_snake_case   						=		16
	elif "huge" in checkpoint_url:
		_snake_case   						=		14
		_snake_case   						=		1_280
		_snake_case   						=		5_120
		_snake_case   						=		32
		_snake_case   						=		16

	_snake_case   						=		ViTMAEForPreTraining(__A	)

	_snake_case   						=		torch.hub.load_state_dict_from_url(__A			,				map_location='cpu'	)['model']

	_snake_case   						=		ViTMAEImageProcessor(size=config.image_size	)

	_snake_case   						=		convert_state_dict(__A			,				__A	)

	model.load_state_dict(__A	)
	model.eval()

	_snake_case   						=		'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

	_snake_case   						=		Image.open(requests.get(__A			,				stream=__A	).raw	)
	_snake_case   						=		ViTMAEImageProcessor(size=config.image_size	)
	_snake_case   						=		image_processor(images=__A			,				return_tensors='pt'	)

	# forward pass
	torch.manual_seed(2	)
	_snake_case   						=		model(**__A	)
	_snake_case   						=		outputs.logits

	if "large" in checkpoint_url:
		_snake_case   						=		torch.tensor(
		    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]	)
	elif "huge" in checkpoint_url:
		_snake_case   						=		torch.tensor(
		    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]	)
	else:
		_snake_case   						=		torch.tensor(
		    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]	)

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

	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	)


if __name__ == "__main__":
					lowercase    :							Union[str, Any]      =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
					    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."
					)

					lowercase    :							Dict      =					parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='bert', choices=['bert'])
       parser.add_argument('--model_name', default='bert-base-uncased', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case__					:							Tuple													=     state_dict[f'{prefix}.embeddings.{w}.weight']
       for w in ["weight", "bias"]:
              snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
                     ]

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

       snake_case__					:							Optional[int]													=     state_dict['cls.predictions.decoder.weight']
       snake_case__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     state_dict[f'cls.predictions.transform.dense.{w}']
                     snake_case__					:							Optional[int]													=     state_dict[f'cls.predictions.transform.LayerNorm.{w}']

       print(f'N layers selected for distillation: {std_idx}')
       print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

       print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
       torch.save(compressed_sd, args.dump_checkpoint)





