
"""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()
SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)




def      lowercase__    (       lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       List[Any]=False			)			->   List[str]:






    """simple docstring"""




    UpperCAmelCase          =       []
    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          =       [(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      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       int=False			)			->   str:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			)			->   Dict:






    """simple docstring"""




    UpperCAmelCase          =       ['head.weight', 'head.bias']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase			,					lowerCAmelCase			)




def      lowercase__    (       lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       List[Any]			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       dct.pop(lowerCAmelCase			)
    UpperCAmelCase          =       val




def      lowercase__    (       )			->   List[Any]:






    """simple docstring"""




    UpperCAmelCase          =       'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase          =       Image.open(requests.get(lowerCAmelCase			,					stream=lowerCAmelCase			).raw			)
    return im




@torch.no_grad()
def      lowercase__    (       lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Tuple			)			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       ViTConfig()
    UpperCAmelCase          =       False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        UpperCAmelCase          =       True
        UpperCAmelCase          =       int(vit_name[-12:-10]			)
        UpperCAmelCase          =       int(vit_name[-9:-6]			)
    else:
        UpperCAmelCase          =       1_000
        UpperCAmelCase          =       'huggingface/label-files'
        UpperCAmelCase          =       'imagenet-1k-id2label.json'
        UpperCAmelCase          =       json.load(open(hf_hub_download(lowerCAmelCase			,					lowerCAmelCase			,					repo_type='dataset'			)			,					'r'			)			)
        UpperCAmelCase          =       {int(lowerCAmelCase			): v for k, v in idalabel.items()}
        UpperCAmelCase          =       idalabel
        UpperCAmelCase          =       {v: k for k, v in idalabel.items()}
        UpperCAmelCase          =       int(vit_name[-6:-4]			)
        UpperCAmelCase          =       int(vit_name[-3:]			)
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith('tiny'			):
            UpperCAmelCase          =       192
            UpperCAmelCase          =       768
            UpperCAmelCase          =       12
            UpperCAmelCase          =       3
        elif vit_name[9:].startswith('small'			):
            UpperCAmelCase          =       384
            UpperCAmelCase          =       1_536
            UpperCAmelCase          =       12
            UpperCAmelCase          =       6
        else:
            pass
    else:
        if vit_name[4:].startswith('small'			):
            UpperCAmelCase          =       768
            UpperCAmelCase          =       2_304
            UpperCAmelCase          =       8
            UpperCAmelCase          =       8
        elif vit_name[4:].startswith('base'			):
            pass
        elif vit_name[4:].startswith('large'			):
            UpperCAmelCase          =       1_024
            UpperCAmelCase          =       4_096
            UpperCAmelCase          =       24
            UpperCAmelCase          =       16
        elif vit_name[4:].startswith('huge'			):
            UpperCAmelCase          =       1_280
            UpperCAmelCase          =       5_120
            UpperCAmelCase          =       32
            UpperCAmelCase          =       16

    # load original model from timm
    UpperCAmelCase          =       timm.create_model(lowerCAmelCase			,					pretrained=lowerCAmelCase			)
    timm_model.eval()

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

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

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

    if base_model:
        UpperCAmelCase          =       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:
        UpperCAmelCase          =       timm_model(lowerCAmelCase			)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(lowerCAmelCase			,					outputs.logits			,					atol=1E-3			)

    Path(lowerCAmelCase			).mkdir(exist_ok=lowerCAmelCase			)
    print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(lowerCAmelCase			)
    print(F"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(lowerCAmelCase			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    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.'''
   )

   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





"""simple docstring"""

from math import ceil, sqrt




def      lowercase__    (       lowerCAmelCase					:       int = 1_000_000			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       0

    for outer_width in range(3			,					(limit // 4) + 2			):
        if outer_width**2 > limit:
            UpperCAmelCase          =       max(ceil(sqrt(outer_width**2 - limit			)			)			,					1			)
        else:
            UpperCAmelCase          =       1
        if (outer_width - hole_width_lower_bound) % 2:
            hole_width_lower_bound += 1

        answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

    return answer


if __name__ == "__main__":
   print(F'{solution() = }')



