






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()
A_		:		List[Any]		     =			logging.get_logger(__name__)

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=False  )	->    Optional[Any]:
  UpperCamelCase_: Optional[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_: Dict   				=    [(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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=False  )	->    Any:
  for i in range(config.num_hidden_layers  ):
    if base_model:
      UpperCamelCase_: str   				=    ''
    else:
      UpperCamelCase_: int   				=    'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    UpperCamelCase_: int   				=    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_: Dict   				=    in_proj_weight[
        : config.hidden_size, :
    ]
    UpperCamelCase_: Union[str, Any]   				=    in_proj_bias[: config.hidden_size]
    UpperCamelCase_: Optional[int]   				=    in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    UpperCamelCase_: Any   				=    in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    UpperCamelCase_: Dict   				=    in_proj_weight[
        -config.hidden_size :, :
    ]
    UpperCamelCase_: List[Any]   				=    in_proj_bias[-config.hidden_size :]

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: List[str]   				=    ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(UpperCAmelCase__     ,   UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: Dict   				=    dct.pop(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    val

def   snake_case ()	->    List[str]:
  UpperCamelCase_: int   				=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_: Dict   				=    Image.open(requests.get(UpperCAmelCase__     ,   stream=UpperCAmelCase__  ).raw  )
  return im

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: List[str]   				=    ViTConfig()
  UpperCamelCase_: Any   				=    False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    UpperCamelCase_: List[str]   				=    True
    UpperCamelCase_: int   				=    int(vit_name[-1_2:-1_0]  )
    UpperCamelCase_: str   				=    int(vit_name[-9:-6]  )
  else:
    UpperCamelCase_: List[Any]   				=    1_0_0_0
    UpperCamelCase_: Optional[int]   				=    'huggingface/label-files'
    UpperCamelCase_: List[str]   				=    'imagenet-1k-id2label.json'
    UpperCamelCase_: Tuple   				=    json.load(open(hf_hub_download(UpperCAmelCase__     ,   UpperCAmelCase__     ,   repo_type='dataset'  )     ,   'r'  )  )
    UpperCamelCase_: int   				=    {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
    UpperCamelCase_: List[str]   				=    idalabel
    UpperCamelCase_: Optional[int]   				=    {v: k for k, v in idalabel.items()}
    UpperCamelCase_: Optional[int]   				=    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_9_2
      UpperCamelCase_: Dict   				=    7_6_8
      UpperCamelCase_: Optional[Any]   				=    1_2
      UpperCamelCase_: Tuple   				=    3
    elif vit_name[9:].startswith('small'  ):
      UpperCamelCase_: Optional[int]   				=    3_8_4
      UpperCamelCase_: Union[str, Any]   				=    1_5_3_6
      UpperCamelCase_: List[str]   				=    1_2
      UpperCamelCase_: List[str]   				=    6
    else:
      pass
  else:
    if vit_name[4:].startswith('small'  ):
      UpperCamelCase_: Union[str, Any]   				=    7_6_8
      UpperCamelCase_: Optional[int]   				=    2_3_0_4
      UpperCamelCase_: str   				=    8
      UpperCamelCase_: Tuple   				=    8
    elif vit_name[4:].startswith('base'  ):
      pass
    elif vit_name[4:].startswith('large'  ):
      UpperCamelCase_: List[Any]   				=    1_0_2_4
      UpperCamelCase_: str   				=    4_0_9_6
      UpperCamelCase_: Tuple   				=    2_4
      UpperCamelCase_: Optional[Any]   				=    1_6
    elif vit_name[4:].startswith('huge'  ):
      UpperCamelCase_: Dict   				=    1_2_8_0
      UpperCamelCase_: str   				=    5_1_2_0
      UpperCamelCase_: List[Any]   				=    3_2
      UpperCamelCase_: Union[str, Any]   				=    1_6

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

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

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

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

  if base_model:
    UpperCamelCase_: int   				=    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:
    UpperCamelCase_: Optional[int]   				=    timm_model(UpperCAmelCase__  )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCAmelCase__     ,   outputs.logits     ,   atol=1E-3  )

  Path(UpperCAmelCase__  ).mkdir(exist_ok=UpperCAmelCase__  )
  print(F'''Saving model {vit_name} 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__":
   A_		:		List[str]		     =			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.'
   )

   A_		:		List[Any]		     =			parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()