





'''simple docstring'''

import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version





def 						UpperCamelCase				(							*_lowerCamelCase					:					str						,       _lowerCamelCase					:					Optional[Union[Dict, Any]] = None						,       _lowerCamelCase					:					Union[str, Any]=True						,       _lowerCamelCase					:					Dict=2       ):
    from .. import __version__

    A__				=			take_from
    A__				=			()
    if not isinstance(args[0]						,       _lowerCamelCase       ):
        A__				=			(args,)

    for attribute, version_name, message in args:
        if version.parse(version.parse(_lowerCamelCase       ).base_version       ) >= version.parse(_lowerCamelCase       ):
            raise ValueError(
                F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                F" version {__version__} is >= {version_name}"       )

        A__				=			None
        if isinstance(_lowerCamelCase						,       _lowerCamelCase       ) and attribute in deprecated_kwargs:
            values += (deprecated_kwargs.pop(_lowerCamelCase       ),)
            A__				=			F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
        elif hasattr(_lowerCamelCase						,       _lowerCamelCase       ):
            values += (getattr(_lowerCamelCase						,       _lowerCamelCase       ),)
            A__				=			F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
        elif deprecated_kwargs is None:
            A__				=			F"`{attribute}` is deprecated and will be removed in version {version_name}."

        if warning is not None:
            A__				=			warning + " " if standard_warn else ""
            warnings.warn(warning + message						,       _lowerCamelCase						,       stacklevel=_lowerCamelCase       )

    if isinstance(_lowerCamelCase						,       _lowerCamelCase       ) and len(_lowerCamelCase       ) > 0:
        A__				=			inspect.getouterframes(inspect.currentframe()       )[1]
        A__				=			call_frame.filename
        A__				=			call_frame.lineno
        A__				=			call_frame.function
        A__,	A__				=			next(iter(deprecated_kwargs.items()       )       )
        raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"       )

    if len(_lowerCamelCase       ) == 0:
        return
    elif len(_lowerCamelCase       ) == 1:
        return values[0]
    return values










'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:	List[Any]			=logging.get_logger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Union[str, Any]=False       ):
    A__				=			[]

    # fmt: off
    # stem:
    rename_keys.append(("cls_token", "vit.embeddings.cls_token")       )
    rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")       )

    rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")       )

    # backbone
    rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")       )
    rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")       )

    for stage_idx in range(len(config.backbone_config.depths       )       ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")       )
            rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")       )

        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")       )
        rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")       )

    # transformer encoder
    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")       )

    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"
        A__				=			[(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"),
            ]       )
    # fmt: on

    return rename_keys





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





def 						UpperCamelCase				(							_lowerCamelCase					:					int       ):
    A__				=			["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,       _lowerCamelCase       )





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			dct.pop(_lowerCamelCase       )
    A__				=			val





def 						UpperCamelCase				(							):
    A__				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
    A__				=			Image.open(requests.get(_lowerCamelCase						,       stream=_lowerCamelCase       ).raw       )
    return im





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					Optional[Any]=False       ):
    A__				=			BitConfig(
        global_padding="same"						,       layer_type="bottleneck"						,       depths=(3, 4, 9)						,       out_features=["stage3"]						,       embedding_dynamic_padding=_lowerCamelCase						,       )
    A__				=			ViTHybridConfig(backbone_config=_lowerCamelCase						,       image_size=3_84						,       num_labels=10_00       )
    A__				=			False

    # load original model from timm
    A__				=			timm.create_model(_lowerCamelCase						,       pretrained=_lowerCamelCase       )
    timm_model.eval()

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

    A__				=			"huggingface/label-files"
    A__				=			"imagenet-1k-id2label.json"
    A__				=			json.load(open(hf_hub_download(_lowerCamelCase						,       _lowerCamelCase						,       repo_type="dataset"       )						,       "r"       )       )
    A__				=			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}
    A__				=			idalabel
    A__				=			{v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__				=			ViTHybridModel(_lowerCamelCase       ).eval()
    else:
        A__				=			ViTHybridForImageClassification(_lowerCamelCase       ).eval()
    model.load_state_dict(_lowerCamelCase       )

    # create image processor
    A__				=			create_transform(**resolve_data_config({}						,       model=_lowerCamelCase       )       )
    A__				=			transform.transforms

    A__				=			{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    A__				=			ViTHybridImageProcessor(
        do_resize=_lowerCamelCase						,       size={"shortest_edge": timm_transforms[0].size}						,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,       do_center_crop=_lowerCamelCase						,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}						,       do_normalize=_lowerCamelCase						,       image_mean=timm_transforms[-1].mean.tolist()						,       image_std=timm_transforms[-1].std.tolist()						,       )

    A__				=			prepare_img()
    A__				=			transform(_lowerCamelCase       ).unsqueeze(0       )
    A__				=			processor(_lowerCamelCase						,       return_tensors="pt"       ).pixel_values

    # verify pixel values
    assert torch.allclose(_lowerCamelCase						,       _lowerCamelCase       )

    # verify logits
    with torch.no_grad():
        A__				=			model(_lowerCamelCase       )
        A__				=			outputs.logits

    print("Predicted class:"						,       logits.argmax(-1       ).item()       )
    if base_model:
        A__				=			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:
        A__				=			timm_model(_lowerCamelCase       )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(_lowerCamelCase						,       outputs.logits						,       atol=1e-3       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
        print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"       )
        model.save_pretrained(_lowerCamelCase       )
        print(F"Saving processor to {pytorch_dump_folder_path}"       )
        processor.save_pretrained(_lowerCamelCase       )

    if push_to_hub:
        print(F"Pushing model and processor to the hub {vit_name}"       )
        model.push_to_hub(F"ybelkada/{vit_name}"       )
        processor.push_to_hub(F"ybelkada/{vit_name}"       )


if __name__ == "__main__":
  __lowerCAmelCase							:	List[str]			=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_r50_s16_384",
      type=str,
      help="Name of the hybrid 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."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
  )

  __lowerCAmelCase							:	Optional[int]			=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



