

'''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()
_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)


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


    """simple docstring"""


    lowerCAmelCase 						=							[]

    # 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"
        lowerCAmelCase 						=							[(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       snake_case   (	snake_case      : List[str]					,		snake_case      : Optional[int]					,		snake_case      : Optional[int]=False			) ->  Optional[int]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Optional[int]			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							['head.weight', 'head.bias']
    for k in ignore_keys:
        state_dict.pop(snake_case					,		snake_case			)


def       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : Union[str, Any]					,		snake_case      : str			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							dct.pop(snake_case			)
    lowerCAmelCase 						=							val


def       snake_case   (	) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCAmelCase 						=							Image.open(requests.get(snake_case					,		stream=snake_case			).raw			)
    return im


@torch.no_grad()
def       snake_case   (	snake_case      : List[str]					,		snake_case      : Union[str, Any]					,		snake_case      : List[str]=False			) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							BitConfig(
        global_padding='same'					,		layer_type='bottleneck'					,		depths=(3, 4, 9)					,		out_features=['stage3']					,		embedding_dynamic_padding=snake_case					,		)
    lowerCAmelCase 						=							ViTHybridConfig(backbone_config=snake_case					,		image_size=384					,		num_labels=1000			)
    lowerCAmelCase 						=							False

    # load original model from timm
    lowerCAmelCase 						=							timm.create_model(snake_case					,		pretrained=snake_case			)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    lowerCAmelCase 						=							timm_model.state_dict()
    if base_model:
        remove_classification_head_(snake_case			)
    lowerCAmelCase 						=							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			)

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

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

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

    lowerCAmelCase 						=							{
        'bilinear': PILImageResampling.BILINEAR,
        'bicubic': PILImageResampling.BICUBIC,
        'nearest': PILImageResampling.NEAREST,
    }

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

    lowerCAmelCase 						=							prepare_img()
    lowerCAmelCase 						=							transform(snake_case			).unsqueeze(0			)
    lowerCAmelCase 						=							processor(snake_case					,		return_tensors='pt'			).pixel_values

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

    # verify logits
    with torch.no_grad():
        lowerCAmelCase 						=							model(snake_case			)
        lowerCAmelCase 						=							outputs.logits

    print('Predicted class:'					,		logits.argmax(-1			).item()			)
    if base_model:
        lowerCAmelCase 						=							timm_model.forward_features(snake_case			)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(snake_case					,		outputs.pooler_output					,		atol=1e-3			)
    else:
        lowerCAmelCase 						=							timm_model(snake_case			)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(snake_case					,		outputs.logits					,		atol=1e-3			)
    print('Looks ok!'			)

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

    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__":
     _UpperCamelCase						:  Tuple							       =				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."
     )

     _UpperCamelCase						:  Union[str, Any]							       =				parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





