





'''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 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any=False     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    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"
        _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"),
            ]     )

    return rename_keys


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       List[Any]=False     ):



    '''simple docstring'''





    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 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





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


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   dct.pop(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   val


def 						__a():



    '''simple docstring'''





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


@torch.no_grad()
def 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       List[Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   ViTConfig()
    _lowerCAmelCase						      =   False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        _lowerCAmelCase						      =   True
        _lowerCAmelCase						      =   int(vit_name[-12:-10]     )
        _lowerCAmelCase						      =   int(vit_name[-9:-6]     )
    else:
        _lowerCAmelCase						      =   1000
        _lowerCAmelCase						      =   "huggingface/label-files"
        _lowerCAmelCase						      =   "imagenet-1k-id2label.json"
        _lowerCAmelCase						      =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							repo_type="dataset"     )		,							"r"     )     )
        _lowerCAmelCase						      =   {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
        _lowerCAmelCase						      =   idalabel
        _lowerCAmelCase						      =   {v: k for k, v in idalabel.items()}
        _lowerCAmelCase						      =   int(vit_name[-6:-4]     )
        _lowerCAmelCase						      =   int(vit_name[-3:]     )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("tiny"     ):
            _lowerCAmelCase						      =   192
            _lowerCAmelCase						      =   768
            _lowerCAmelCase						      =   12
            _lowerCAmelCase						      =   3
        elif vit_name[9:].startswith("small"     ):
            _lowerCAmelCase						      =   384
            _lowerCAmelCase						      =   1536
            _lowerCAmelCase						      =   12
            _lowerCAmelCase						      =   6
        else:
            pass
    else:
        if vit_name[4:].startswith("small"     ):
            _lowerCAmelCase						      =   768
            _lowerCAmelCase						      =   2304
            _lowerCAmelCase						      =   8
            _lowerCAmelCase						      =   8
        elif vit_name[4:].startswith("base"     ):
            pass
        elif vit_name[4:].startswith("large"     ):
            _lowerCAmelCase						      =   1024
            _lowerCAmelCase						      =   4096
            _lowerCAmelCase						      =   24
            _lowerCAmelCase						      =   16
        elif vit_name[4:].startswith("huge"     ):
            _lowerCAmelCase						      =   1280
            _lowerCAmelCase						      =   5120
            _lowerCAmelCase						      =   32
            _lowerCAmelCase						      =   16

    # load original model from timm
    _lowerCAmelCase						      =   timm.create_model(SCREAMING_SNAKE_CASE_		,							pretrained=SCREAMING_SNAKE_CASE_     )
    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_(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   create_rename_keys(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
    read_in_q_k_v(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

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

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

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

    Path(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''     )
    model.save_pretrained(SCREAMING_SNAKE_CASE_     )
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
    image_processor.save_pretrained(SCREAMING_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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-12      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


