





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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_			=      logging.get_logger(__name__)







def 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  Dict=False						)       ->Any:



    '''simple docstring'''







    __A   :						str									=					[]
    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"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('''cls_token''', '''deit.embeddings.cls_token'''),
            ('''dist_token''', '''deit.embeddings.distillation_token'''),
            ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
            ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
            ('''pos_embed''', '''deit.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 "deit" from all keys that start with "deit"
        __A   :						Optional[Any]									=					[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''						) else pair for pair in rename_keys]
    else:
        # layernorm + classification heads
        rename_keys.extend(
            [
                ('''norm.weight''', '''deit.layernorm.weight'''),
                ('''norm.bias''', '''deit.layernorm.bias'''),
                ('''head.weight''', '''cls_classifier.weight'''),
                ('''head.bias''', '''cls_classifier.bias'''),
                ('''head_dist.weight''', '''distillation_classifier.weight'''),
                ('''head_dist.bias''', '''distillation_classifier.bias'''),
            ]						)

    return rename_keys







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Optional[Any]=False						)       ->List[Any]:



    '''simple docstring'''







    for i in range(config.num_hidden_layers						):
        if base_model:
            __A   :						Tuple									=					''''''
        else:
            __A   :						Optional[Any]									=					'''deit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        __A   :						Union[str, Any]									=					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   :						Any									=					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   :						List[Any]									=					in_proj_weight[
            -config.hidden_size :, :
        ]
        __A   :						Union[str, Any]									=					in_proj_bias[-config.hidden_size :]







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Tuple						)       ->Any:



    '''simple docstring'''







    __A   :						int									=					dct.pop(snake_case_						)
    __A   :						Union[str, Any]									=					val







def 		__lowercase					(       )       ->int:



    '''simple docstring'''







    __A   :						Any									=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A   :						int									=					Image.open(requests.get(snake_case_    ,stream=snake_case_						).raw						)
    return im







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  List[str]						)       ->Tuple:



    '''simple docstring'''







    __A   :						Optional[Any]									=					DeiTConfig()
    # all deit models have fine-tuned heads
    __A   :						List[Any]									=					False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    __A   :						Dict									=					1000
    __A   :						List[Any]									=					'''huggingface/label-files'''
    __A   :						Union[str, Any]									=					'''imagenet-1k-id2label.json'''
    __A   :						Tuple									=					json.load(open(hf_hub_download(snake_case_    ,snake_case_    ,repo_type='''dataset'''						)    ,'''r'''						)						)
    __A   :						str									=					{int(snake_case_						): v for k, v in idalabel.items()}
    __A   :						str									=					idalabel
    __A   :						Tuple									=					{v: k for k, v in idalabel.items()}
    __A   :						List[Any]									=					int(deit_name[-6:-4]						)
    __A   :						List[Any]									=					int(deit_name[-3:]						)
    # size of the architecture
    if deit_name[9:].startswith('''tiny'''						):
        __A   :						int									=					192
        __A   :						Optional[Any]									=					768
        __A   :						Optional[int]									=					12
        __A   :						Tuple									=					3
    elif deit_name[9:].startswith('''small'''						):
        __A   :						int									=					384
        __A   :						str									=					1536
        __A   :						Any									=					12
        __A   :						List[str]									=					6
    if deit_name[9:].startswith('''base'''						):
        pass
    elif deit_name[4:].startswith('''large'''						):
        __A   :						List[str]									=					1024
        __A   :						Union[str, Any]									=					4096
        __A   :						str									=					24
        __A   :						str									=					16

    # load original model from timm
    __A   :						Tuple									=					timm.create_model(snake_case_    ,pretrained=snake_case_						)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __A   :						Union[str, Any]									=					timm_model.state_dict()
    __A   :						Dict									=					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
    __A   :						Optional[int]									=					DeiTForImageClassificationWithTeacher(snake_case_						).eval()
    model.load_state_dict(snake_case_						)

    # Check outputs on an image, prepared by DeiTImageProcessor
    __A   :						int									=					int(
        (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    __A   :						List[str]									=					DeiTImageProcessor(size=snake_case_    ,crop_size=config.image_size						)
    __A   :						Tuple									=					image_processor(images=prepare_img()    ,return_tensors='''pt'''						)
    __A   :						Optional[int]									=					encoding['''pixel_values''']
    __A   :						Union[str, Any]									=					model(snake_case_						)

    __A   :						Any									=					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 {deit_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__":
       a_			=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--deit_name""",
           default="""vit_deit_base_distilled_patch16_224""",
           type=str,
           help="""Name of the DeiT 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_			=      parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




