





"""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()
lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       Optional[Any]										=  []

  # 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"
    A_  :       List[str]										=  [(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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_UpperCAmelCase ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  dct.pop(_UpperCAmelCase					)
  A_  :       Optional[int]										=  val





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Optional[int]										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       int										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)
  return im





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       List[Any]										=  BitConfig(
      global_padding='same' ,					layer_type='bottleneck' ,					depths=(3, 4, 9) ,					out_features=['stage3'] ,					embedding_dynamic_padding=_UpperCAmelCase ,					)
  A_  :       Optional[int]										=  ViTHybridConfig(backbone_config=_UpperCAmelCase ,					image_size=384 ,					num_labels=1000					)
  A_  :       Union[str, Any]										=  False

  # load original model from timm
  A_  :       List[Any]										=  timm.create_model(_UpperCAmelCase ,					pretrained=_UpperCAmelCase					)
  timm_model.eval()

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

  A_  :       Union[str, Any]										=  'huggingface/label-files'
  A_  :       Dict										=  'imagenet-1k-id2label.json'
  A_  :       List[str]										=  json.load(open(hf_hub_download(_UpperCAmelCase ,					_UpperCAmelCase ,					repo_type='dataset'					) ,					'r'					)					)
  A_  :       str										=  {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
  A_  :       Any										=  idalabel
  A_  :       Optional[int]										=  {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    A_  :       List[Any]										=  ViTHybridModel(_UpperCAmelCase					).eval()
  else:
    A_  :       str										=  ViTHybridForImageClassification(_UpperCAmelCase					).eval()
  model.load_state_dict(_UpperCAmelCase					)

  # create image processor
  A_  :       Dict										=  create_transform(**resolve_data_config({} ,					model=_UpperCAmelCase					)					)
  A_  :       List[str]										=  transform.transforms

  A_  :       List[str]										=  {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

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

  A_  :       Optional[Any]										=  prepare_img()
  A_  :       Any										=  transform(_UpperCAmelCase					).unsqueeze(0					)
  A_  :       Dict										=  processor(_UpperCAmelCase ,					return_tensors='pt'					).pixel_values

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

  # verify logits
  with torch.no_grad():
    A_  :       List[Any]										=  model(_UpperCAmelCase					)
    A_  :       List[str]										=  outputs.logits

  print('Predicted class:' ,					logits.argmax(-1					).item()					)
  if base_model:
    A_  :       Union[str, Any]										=  timm_model.forward_features(_UpperCAmelCase					)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.pooler_output ,					atol=1E-3					)
  else:
    A_  :       Tuple										=  timm_model(_UpperCAmelCase					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.logits ,					atol=1E-3					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
    print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
    model.save_pretrained(_UpperCAmelCase					)
    print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
    processor.save_pretrained(_UpperCAmelCase					)

  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__":
   lowerCamelCase_     :						Optional[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.'
   )

   lowerCamelCase_     :						List[str]						=					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_     :						List[str]						=					[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Union[str, Any]										=  os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
  A_  :       Tuple										=  os.path.join(_UpperCAmelCase ,					'words.txt'					)

  A_  :       List[Any]										=  ''
  with open(_UpperCAmelCase					) as f:
    A_  :       int										=  f.readline()

  A_  :       Optional[Any]										=  [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
  A_  :       Dict										=  [
      word
      for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
      if word in TRIANGULAR_NUMBERS
  ]
  return len(_UpperCAmelCase					)


if __name__ == "__main__":
   print(solution())