






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    :   Any        =			logging.get_logger(__name__)


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


def 				snake_case__							(							UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  int       ):
    lowerCAmelCase__   :Optional[int]      							=				dct.pop(UpperCAmelCase       )
    lowerCAmelCase__   :Union[str, Any]      							=				val


def 				snake_case__							(							):
    lowerCAmelCase__   :List[str]      							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
    lowerCAmelCase__   :Dict      							=				Image.open(requests.get(UpperCAmelCase    ,			stream=UpperCAmelCase       ).raw       )
    return im


@torch.no_grad()
def 				snake_case__							(							UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  Dict       ):
    lowerCAmelCase__   :int      							=				DeiTConfig()
    # all deit models have fine-tuned heads
    lowerCAmelCase__   :Any      							=				False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    lowerCAmelCase__   :int      							=				1_0_0_0
    lowerCAmelCase__   :List[str]      							=				"huggingface/label-files"
    lowerCAmelCase__   :int      							=				"imagenet-1k-id2label.json"
    lowerCAmelCase__   :Optional[Any]      							=				json.load(open(hf_hub_download(UpperCAmelCase    ,			UpperCAmelCase    ,			repo_type="dataset"       )    ,			"r"       )       )
    lowerCAmelCase__   :Optional[Any]      							=				{int(UpperCAmelCase       ): v for k, v in idalabel.items()}
    lowerCAmelCase__   :Optional[int]      							=				idalabel
    lowerCAmelCase__   :Dict      							=				{v: k for k, v in idalabel.items()}
    lowerCAmelCase__   :int      							=				int(deit_name[-6:-4]       )
    lowerCAmelCase__   :Any      							=				int(deit_name[-3:]       )
    # size of the architecture
    if deit_name[9:].startswith("tiny"       ):
        lowerCAmelCase__   :Tuple      							=				1_9_2
        lowerCAmelCase__   :List[str]      							=				7_6_8
        lowerCAmelCase__   :List[str]      							=				1_2
        lowerCAmelCase__   :Optional[Any]      							=				3
    elif deit_name[9:].startswith("small"       ):
        lowerCAmelCase__   :Dict      							=				3_8_4
        lowerCAmelCase__   :Union[str, Any]      							=				1_5_3_6
        lowerCAmelCase__   :Union[str, Any]      							=				1_2
        lowerCAmelCase__   :str      							=				6
    if deit_name[9:].startswith("base"       ):
        pass
    elif deit_name[4:].startswith("large"       ):
        lowerCAmelCase__   :Optional[Any]      							=				1_0_2_4
        lowerCAmelCase__   :List[Any]      							=				4_0_9_6
        lowerCAmelCase__   :List[str]      							=				2_4
        lowerCAmelCase__   :Optional[int]      							=				1_6

    # load original model from timm
    lowerCAmelCase__   :Union[str, Any]      							=				timm.create_model(UpperCAmelCase    ,			pretrained=UpperCAmelCase       )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    lowerCAmelCase__   :List[str]      							=				timm_model.state_dict()
    lowerCAmelCase__   :Optional[int]      							=				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
    lowerCAmelCase__   :Dict      							=				DeiTForImageClassificationWithTeacher(UpperCAmelCase       ).eval()
    model.load_state_dict(UpperCAmelCase       )

    # Check outputs on an image, prepared by DeiTImageProcessor
    lowerCAmelCase__   :List[Any]      							=				int(
        (2_5_6 / 2_2_4) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    lowerCAmelCase__   :Union[str, Any]      							=				DeiTImageProcessor(size=UpperCAmelCase    ,			crop_size=config.image_size       )
    lowerCAmelCase__   :int      							=				image_processor(images=prepare_img()    ,			return_tensors="pt"       )
    lowerCAmelCase__   :List[Any]      							=				encoding["pixel_values"]
    lowerCAmelCase__   :Optional[Any]      							=				model(UpperCAmelCase       )

    lowerCAmelCase__   :str      							=				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 {deit_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    :   Tuple        =			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    :   Any        =			parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)











from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_a    :   Optional[Any]        =			[
    """python""",
    """tqdm""",
    """regex""",
    """requests""",
    """packaging""",
    """filelock""",
    """numpy""",
    """tokenizers""",
    """huggingface-hub""",
    """safetensors""",
    """accelerate""",
    """pyyaml""",
]

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed
  elif pkg == "accelerate":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_accelerate_available

   # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
   # Transformers with PyTorch
   if not is_accelerate_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")


def 				snake_case__							(							UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  Union[str, Any]=None       ):
    require_version(deps[pkg]    ,			UpperCAmelCase       )



