





'''simple docstring'''

import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase							:     List[str]		     ="src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase							:     Tuple		     =direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCAmelCase							:     Dict		     =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__lowerCAmelCase							:     Optional[Any]		     =re.compile(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


__lowerCAmelCase							:     int		     ={
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      None

    # source code of `config_class`
    A__    =      inspect.getsource(_lowerCamelCase	)
    A__    =      _re_checkpoint.findall(_lowerCamelCase	)

    # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
    # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
    for ckpt_name, ckpt_link in checkpoints:
        # allow the link to end with `/`
        if ckpt_link.endswith("/"	):
            A__    =      ckpt_link[:-1]

        # verify the checkpoint name corresponds to the checkpoint link
        A__    =      F"https://huggingface.co/{ckpt_name}"
        if ckpt_link == ckpt_link_from_name:
            A__    =      ckpt_name
            break

    return checkpoint





def 						UpperCamelCase    (							):
    A__    =      []

    for config_class in list(CONFIG_MAPPING.values()	):
        # Skip deprecated models
        if "models.deprecated" in config_class.__module__:
            continue
        A__    =      get_checkpoint_from_config_class(_lowerCamelCase	)

        A__    =      config_class.__name__
        if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
            configs_without_checkpoint.append(_lowerCamelCase	)

    if len(_lowerCamelCase	) > 0:
        A__    =      "\n".join(sorted(_lowerCamelCase	)	)
        raise ValueError(F"The following configurations don't contain any valid checkpoint:\n{message}"	)


if __name__ == "__main__":
       check_config_docstrings_have_checkpoints()










'''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							:     Any		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple=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 : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Union[str, 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 : Any	):
    A__    =      ["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, 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 : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : int=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							:     Dict		     =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[Any]		     =parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



