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

def  __snake_case							(	__magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


				lowercase         =      []

				# 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"
								lowercase         =      [(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							(	__magic_name__		,    __magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__magic_name__		,    __magic_name__						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      dct.pop(__magic_name__						)
				lowercase         =      val

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase         =      Image.open(requests.get(__magic_name__		,    stream=__magic_name__						).raw						)
				return im

@torch.no_grad()
def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


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

				# load original model from timm
				lowercase         =      timm.create_model(__magic_name__		,    pretrained=__magic_name__						)
				timm_model.eval()

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

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

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

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

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

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

				lowercase         =      prepare_img()
				lowercase         =      transform(__magic_name__						).unsqueeze(0						)
				lowercase         =      processor(__magic_name__		,    return_tensors="pt"						).pixel_values

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

				# verify logits
				with torch.no_grad():
								lowercase         =      model(__magic_name__						)
								lowercase         =      outputs.logits

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

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

				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__":
						_snake_case      :							Union[str, Any]     	=    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."
						)

						_snake_case      :							int     	=    parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase         =      [], []
				while len(__magic_name__						) > 1:
								lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
								start.append(__magic_name__						)
								end.append(__magic_name__						)
								collection.remove(__magic_name__						)
								collection.remove(__magic_name__						)
				end.reverse()
				return start + collection + end


if __name__ == "__main__":
						_snake_case      :							int     	=    input("Enter numbers separated by a comma:\n").strip()
						_snake_case      :							Dict     	=    [int(item) for item in user_input.split(",")]
						print(*merge_sort(unsorted), sep=",")
