'''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()
UpperCamelCase__  :						str					=      logging.get_logger(__name__)







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







def __UpperCamelCase( _A       :					List[Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(_A	,      _A     )







def __UpperCamelCase( _A       :					Optional[int]	,      _A       :					Any	,      _A       :					str     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	dct.pop(_A     )
   UpperCAmelCase__				:					Dict 					=	val







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__				:					Optional[Any] 					=	Image.open(requests.get(_A	,      stream=_A     ).raw     )
   return im







@torch.no_grad()
def __UpperCamelCase( _A       :					Union[str, Any]	,      _A       :					Optional[int]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	ViTConfig()
   UpperCAmelCase__				:					Tuple 					=	False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      UpperCAmelCase__				:					Tuple 					=	True
      UpperCAmelCase__				:					str 					=	int(vit_name[-12:-10]     )
      UpperCAmelCase__				:					Optional[int] 					=	int(vit_name[-9:-6]     )
   else:
      UpperCAmelCase__				:					Union[str, Any] 					=	10_00
      UpperCAmelCase__				:					Optional[int] 					=	'''huggingface/label-files'''
      UpperCAmelCase__				:					int 					=	'''imagenet-1k-id2label.json'''
      UpperCAmelCase__				:					Tuple 					=	json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''     )	,      '''r'''     )     )
      UpperCAmelCase__				:					Optional[Any] 					=	{int(_A     ): v for k, v in idalabel.items()}
      UpperCAmelCase__				:					Dict 					=	idalabel
      UpperCAmelCase__				:					Union[str, Any] 					=	{v: k for k, v in idalabel.items()}
      UpperCAmelCase__				:					Dict 					=	int(vit_name[-6:-4]     )
      UpperCAmelCase__				:					List[Any] 					=	int(vit_name[-3:]     )
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith('''tiny'''     ):
         UpperCAmelCase__				:					str 					=	1_92
         UpperCAmelCase__				:					Optional[int] 					=	7_68
         UpperCAmelCase__				:					Any 					=	12
         UpperCAmelCase__				:					Dict 					=	3
      elif vit_name[9:].startswith('''small'''     ):
         UpperCAmelCase__				:					Tuple 					=	3_84
         UpperCAmelCase__				:					Optional[Any] 					=	15_36
         UpperCAmelCase__				:					Optional[Any] 					=	12
         UpperCAmelCase__				:					Dict 					=	6
      else:
         pass
   else:
      if vit_name[4:].startswith('''small'''     ):
         UpperCAmelCase__				:					List[str] 					=	7_68
         UpperCAmelCase__				:					Optional[Any] 					=	23_04
         UpperCAmelCase__				:					Optional[int] 					=	8
         UpperCAmelCase__				:					Dict 					=	8
      elif vit_name[4:].startswith('''base'''     ):
         pass
      elif vit_name[4:].startswith('''large'''     ):
         UpperCAmelCase__				:					Tuple 					=	10_24
         UpperCAmelCase__				:					Union[str, Any] 					=	40_96
         UpperCAmelCase__				:					Optional[int] 					=	24
         UpperCAmelCase__				:					Any 					=	16
      elif vit_name[4:].startswith('''huge'''     ):
         UpperCAmelCase__				:					Dict 					=	12_80
         UpperCAmelCase__				:					Dict 					=	51_20
         UpperCAmelCase__				:					List[Any] 					=	32
         UpperCAmelCase__				:					str 					=	16

    # load original model from timm
   UpperCAmelCase__				:					Optional[Any] 					=	timm.create_model(_A	,      pretrained=_A     )
   timm_model.eval()

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

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

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

   if base_model:
      UpperCAmelCase__				:					Any 					=	timm_model.forward_features(_A     )
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(_A	,      outputs.pooler_output	,      atol=1e-3     )
   else:
      UpperCAmelCase__				:					Any 					=	timm_model(_A     )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_A	,      outputs.logits	,      atol=1e-3     )

   Path(_A     ).mkdir(exist_ok=_A     )
   print(F'''Saving model {vit_name} 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__":
  UpperCamelCase__  :						int					=      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.'
  )

  UpperCamelCase__  :						Optional[int]					=      parser.parse_args()
  convert_vit_checkpoint(args.vit_name, 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__  :						Dict					=      logging.getLogger(__name__)







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

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

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

   UpperCAmelCase__				:					Dict 					=	F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
   UpperCAmelCase__				:					Dict 					=	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      UpperCAmelCase__				:					Any 					=	[np.uintaa(_A     ) for d in rslt]
   else:
      UpperCAmelCase__				:					str 					=	[np.intaa(_A     ) for d in rslt]
   random.shuffle(rslt_     )
   logger.info(F'''Dump to {dp_file}'''     )
   with open(_A	,      '''wb'''     ) as handle:
      pickle.dump(rslt_	,      _A	,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
  main()




