



'''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 							lowerCamelCase	(					_snake_case	:    List[Any]  ,_snake_case	:    Tuple=False							):





    '''simple docstring'''


    lowercase__									=    []
    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"
        lowercase__									=    [(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 							lowerCamelCase	(					_snake_case	:    Any  ,_snake_case	:    Any  ,_snake_case	:    Tuple=False							):





    '''simple docstring'''


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




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]							):





    '''simple docstring'''


    lowercase__									=    ["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_snake_case  ,_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    lowercase__									=    dct.pop(_snake_case							)
    lowercase__									=    val




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    "http://images.cocodataset.org/val2017/000000039769.jpg"
    lowercase__									=    Image.open(requests.get(_snake_case  ,stream=_snake_case							).raw							)
    return im




@torch.no_grad()
def 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Optional[Any]							):





    '''simple docstring'''


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

    # load original model from timm
    lowercase__									=    timm.create_model(_snake_case  ,pretrained=_snake_case							)
    timm_model.eval()

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

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

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

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

    Path(_snake_case							).mkdir(exist_ok=_snake_case							)
    print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''							)
    model.save_pretrained(_snake_case							)
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
    image_processor.save_pretrained(_snake_case							)


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 __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




