






"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Optional[int]=False )      ->				str:
  lowerCamelCase_         =						[]

  # fmt: off
  # stem:
  rename_keys.append(('cls_token', 'vit.embeddings.cls_token') )
  rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings') )

  rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight') )
  rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias') )

  # backbone
  rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight') )
  rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight') )
  rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias') )

  for stage_idx in range(len(config.backbone_config.depths ) ):
    for layer_idx in range(config.backbone_config.depths[stage_idx] ):
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )

    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )

  # transformer encoder
  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''') )

  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"
    lowerCamelCase_         =						[(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'),
        ] )
  # fmt: on

  return rename_keys







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







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_lowerCamelCase		,				_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		List[str] )      ->				List[Any]:
  lowerCamelCase_         =						dct.pop(_lowerCamelCase )
  lowerCamelCase_         =						val







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  lowerCamelCase_         =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCamelCase_         =						Image.open(requests.get(_lowerCamelCase		,				stream=_lowerCamelCase ).raw )
  return im







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Optional[int]=False )      ->				Optional[Any]:
  lowerCamelCase_         =						BitConfig(
      global_padding='same'		,				layer_type='bottleneck'		,				depths=(3, 4, 9)		,				out_features=['stage3']		,				embedding_dynamic_padding=_lowerCamelCase		,				)
  lowerCamelCase_         =						ViTHybridConfig(backbone_config=_lowerCamelCase		,				image_size=384		,				num_labels=1000 )
  lowerCamelCase_         =						False

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

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

  lowerCamelCase_         =						'huggingface/label-files'
  lowerCamelCase_         =						'imagenet-1k-id2label.json'
  lowerCamelCase_         =						json.load(open(hf_hub_download(_lowerCamelCase		,				_lowerCamelCase		,				repo_type='dataset' )		,				'r' ) )
  lowerCamelCase_         =						{int(_lowerCamelCase ): v for k, v in idalabel.items()}
  lowerCamelCase_         =						idalabel
  lowerCamelCase_         =						{v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCamelCase_         =						ViTHybridModel(_lowerCamelCase ).eval()
  else:
    lowerCamelCase_         =						ViTHybridForImageClassification(_lowerCamelCase ).eval()
  model.load_state_dict(_lowerCamelCase )

  # create image processor
  lowerCamelCase_         =						create_transform(**resolve_data_config({}		,				model=_lowerCamelCase ) )
  lowerCamelCase_         =						transform.transforms

  lowerCamelCase_         =						{
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  lowerCamelCase_         =						ViTHybridImageProcessor(
      do_resize=_lowerCamelCase		,				size={'shortest_edge': timm_transforms[0].size}		,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,				do_center_crop=_lowerCamelCase		,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}		,				do_normalize=_lowerCamelCase		,				image_mean=timm_transforms[-1].mean.tolist()		,				image_std=timm_transforms[-1].std.tolist()		,				)

  lowerCamelCase_         =						prepare_img()
  lowerCamelCase_         =						transform(_lowerCamelCase ).unsqueeze(0 )
  lowerCamelCase_         =						processor(_lowerCamelCase		,				return_tensors='pt' ).pixel_values

  # verify pixel values
  assert torch.allclose(_lowerCamelCase		,				_lowerCamelCase )

  # verify logits
  with torch.no_grad():
    lowerCamelCase_         =						model(_lowerCamelCase )
    lowerCamelCase_         =						outputs.logits

  print('Predicted class:'		,				logits.argmax(-1 ).item() )
  if base_model:
    lowerCamelCase_         =						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:
    lowerCamelCase_         =						timm_model(_lowerCamelCase )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_lowerCamelCase		,				outputs.logits		,				atol=1e-3 )
  print('Looks ok!' )

  if pytorch_dump_folder_path is not None:
    Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
    model.save_pretrained(_lowerCamelCase )
    print(F'''Saving processor to {pytorch_dump_folder_path}''' )
    processor.save_pretrained(_lowerCamelCase )

  if push_to_hub:
    print(F'''Pushing model and processor to the hub {vit_name}''' )
    model.push_to_hub(F'''ybelkada/{vit_name}''' )
    processor.push_to_hub(F'''ybelkada/{vit_name}''' )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--vit_name''',
      default='''vit_base_r50_s16_384''',
      type=str,
      help='''Name of the hybrid 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.'''
  )
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
  )

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












"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




