


'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 if "cls_token" in name:
  __A  :		Union[str, Any]       =			name.replace('cls_token',				'vit.embeddings.cls_token'			)
 if "mask_token" in name:
  __A  :		Tuple       =			name.replace('mask_token',				'decoder.mask_token'			)
 if "decoder_pos_embed" in name:
  __A  :		int       =			name.replace('decoder_pos_embed',				'decoder.decoder_pos_embed'			)
 if "pos_embed" in name and "decoder" not in name:
  __A  :		Optional[Any]       =			name.replace('pos_embed',				'vit.embeddings.position_embeddings'			)
 if "patch_embed.proj" in name:
  __A  :		Optional[int]       =			name.replace('patch_embed.proj',				'vit.embeddings.patch_embeddings.projection'			)
 if "patch_embed.norm" in name:
  __A  :		str       =			name.replace('patch_embed.norm',				'vit.embeddings.norm'			)
 if "decoder_blocks" in name:
  __A  :		Dict       =			name.replace('decoder_blocks',				'decoder.decoder_layers'			)
 if "blocks" in name:
  __A  :		Union[str, Any]       =			name.replace('blocks',				'vit.encoder.layer'			)
 if "attn.proj" in name:
  __A  :		int       =			name.replace('attn.proj',				'attention.output.dense'			)
 if "attn" in name:
  __A  :		Tuple       =			name.replace('attn',				'attention.self'			)
 if "norm1" in name:
  __A  :		List[Any]       =			name.replace('norm1',				'layernorm_before'			)
 if "norm2" in name:
  __A  :		Optional[Any]       =			name.replace('norm2',				'layernorm_after'			)
 if "mlp.fc1" in name:
  __A  :		Optional[Any]       =			name.replace('mlp.fc1',				'intermediate.dense'			)
 if "mlp.fc2" in name:
  __A  :		Tuple       =			name.replace('mlp.fc2',				'output.dense'			)
 if "decoder_embed" in name:
  __A  :		Optional[int]       =			name.replace('decoder_embed',				'decoder.decoder_embed'			)
 if "decoder_norm" in name:
  __A  :		str       =			name.replace('decoder_norm',				'decoder.decoder_norm'			)
 if "decoder_pred" in name:
  __A  :		Tuple       =			name.replace('decoder_pred',				'decoder.decoder_pred'			)
 if "norm.weight" in name and "decoder" not in name:
  __A  :		Union[str, Any]       =			name.replace('norm.weight',				'vit.layernorm.weight'			)
 if "norm.bias" in name and "decoder" not in name:
  __A  :		List[str]       =			name.replace('norm.bias',				'vit.layernorm.bias'			)

 return name


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Optional[Any]			):
 for key in orig_state_dict.copy().keys():
  __A  :		str       =			orig_state_dict.pop(UpperCamelCase__			)

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

  else:
   __A  :		Tuple       =			val

 return orig_state_dict


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Tuple			):
 __A  :		str       =			ViTMAEConfig()
 if "large" in checkpoint_url:
  __A  :		int       =			1024
  __A  :		Union[str, Any]       =			4096
  __A  :		str       =			24
  __A  :		List[Any]       =			16
 elif "huge" in checkpoint_url:
  __A  :		Dict       =			14
  __A  :		Tuple       =			1280
  __A  :		int       =			5120
  __A  :		Union[str, Any]       =			32
  __A  :		List[str]       =			16

 __A  :		Optional[int]       =			ViTMAEForPreTraining(UpperCamelCase__			)

 __A  :		str       =			torch.hub.load_state_dict_from_url(UpperCamelCase__,				map_location='cpu'			)['model']

 __A  :		Optional[int]       =			ViTMAEImageProcessor(size=config.image_size			)

 __A  :		Tuple       =			convert_state_dict(UpperCamelCase__,				UpperCamelCase__			)

 model.load_state_dict(UpperCamelCase__			)
 model.eval()

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

 __A  :		Union[str, Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 __A  :		Optional[Any]       =			ViTMAEImageProcessor(size=config.image_size			)
 __A  :		Union[str, Any]       =			image_processor(images=UpperCamelCase__,				return_tensors='pt'			)

 # forward pass
 torch.manual_seed(2			)
 __A  :		Optional[Any]       =			model(**UpperCamelCase__			)
 __A  :		Union[str, Any]       =			outputs.logits

 if "large" in checkpoint_url:
  __A  :		Optional[int]       =			torch.tensor(
      [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]			)
 elif "huge" in checkpoint_url:
  __A  :		List[Any]       =			torch.tensor(
      [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
 else:
  __A  :		Optional[int]       =			torch.tensor(
      [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]			)

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

 print(f"""Saving model to {pytorch_dump_folder_path}"""			)
 model.save_pretrained(UpperCamelCase__			)

 print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
 image_processor.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[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.'
 )

 UpperCAmelCase_ :     str								=      parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


def 			_lowercase       (				):
 __A  :		Tuple       =			argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path',				type=UpperCamelCase__,				default='data/dump.txt',				help='The path to the data.'			)
 parser.add_argument('--tokenizer_type',				type=UpperCamelCase__,				default='bert',				choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name',				type=UpperCamelCase__,				default='bert-base-uncased',				help='The tokenizer to use.'			)
 parser.add_argument('--dump_file',				type=UpperCamelCase__,				default='data/dump',				help='The dump file prefix.'			)
 __A  :		Tuple       =			parser.parse_args()

 logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""			)
 if args.tokenizer_type == "bert":
  __A  :		Any       =			BertTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  __A  :		List[Any]       =			tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  __A  :		Any       =			RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		List[Any]       =			tokenizer.special_tokens_map['cls_token']  # `<s>`
  __A  :		Dict       =			tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  __A  :		List[str]       =			GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  __A  :		Union[str, Any]       =			tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(f"""Loading text from {args.file_path}"""			)
 with open(args.file_path,				'r',				encoding='utf8'			) as fp:
  __A  :		Optional[int]       =			fp.readlines()

 logger.info('Start encoding'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples to process."""			)

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

  iter += 1
  if iter % interval == 0:
   __A  :		Any       =			time.time()
   logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""			)
   __A  :		Any       =			time.time()
 logger.info('Finished binarization'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples processed."""			)

 __A  :		List[str]       =			f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
 __A  :		str       =			tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __A  :		Optional[int]       =			[np.uintaa(UpperCamelCase__			) for d in rslt]
 else:
  __A  :		int       =			[np.intaa(UpperCamelCase__			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(f"""Dump to {dp_file}"""			)
 with open(UpperCamelCase__,				'wb'			) as handle:
  pickle.dump(rslt_,				UpperCamelCase__,				protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
 main()






