
"""simple docstring"""


lowerCAmelCase								=						[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






'''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)



