



"""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()
A:     Tuple										=	logging.get_logger(__name__)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Tuple=False		):
 UpperCAmelCase			:      Dict			=			[]

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

def    _snake_case		(		UpperCamelCase		:	Any		):
 UpperCAmelCase			:      Union[str, Any]			=			["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase    ,	UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Dict		):
 UpperCAmelCase			:      Any			=			dct.pop(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			val

def    _snake_case		(		):
 UpperCAmelCase			:      Union[str, Any]			=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase			:      Union[str, Any]			=			Image.open(requests.get(UpperCamelCase    ,	stream=UpperCamelCase		).raw		)
 return im

@torch.no_grad()
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	Any=False		):
 UpperCAmelCase			:      str			=			BitConfig(
     global_padding="""same"""    ,	layer_type="""bottleneck"""    ,	depths=(3, 4, 9)    ,	out_features=["""stage3"""]    ,	embedding_dynamic_padding=UpperCamelCase    ,	)
 UpperCAmelCase			:      List[Any]			=			ViTHybridConfig(backbone_config=UpperCamelCase    ,	image_size=384    ,	num_labels=1000		)
 UpperCAmelCase			:      str			=			False

 # load original model from timm
 UpperCAmelCase			:      str			=			timm.create_model(UpperCamelCase    ,	pretrained=UpperCamelCase		)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 UpperCAmelCase			:      Union[str, Any]			=			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			create_rename_keys(UpperCamelCase    ,	UpperCamelCase		)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)
 read_in_q_k_v(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 UpperCAmelCase			:      Optional[int]			=			"""huggingface/label-files"""
 UpperCAmelCase			:      Any			=			"""imagenet-1k-id2label.json"""
 UpperCAmelCase			:      List[Any]			=			json.load(open(hf_hub_download(UpperCamelCase    ,	UpperCamelCase    ,	repo_type="""dataset"""		)    ,	"""r"""		)		)
 UpperCAmelCase			:      Dict			=			{int(UpperCamelCase		): v for k, v in idalabel.items()}
 UpperCAmelCase			:      Union[str, Any]			=			idalabel
 UpperCAmelCase			:      Tuple			=			{v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  UpperCAmelCase			:      List[Any]			=			ViTHybridModel(UpperCamelCase		).eval()
 else:
  UpperCAmelCase			:      Optional[int]			=			ViTHybridForImageClassification(UpperCamelCase		).eval()
 model.load_state_dict(UpperCamelCase		)

 # create image processor
 UpperCAmelCase			:      int			=			create_transform(**resolve_data_config({}    ,	model=UpperCamelCase		)		)
 UpperCAmelCase			:      Union[str, Any]			=			transform.transforms

 UpperCAmelCase			:      Union[str, Any]			=			{
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

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

 UpperCAmelCase			:      Optional[int]			=			prepare_img()
 UpperCAmelCase			:      Optional[int]			=			transform(UpperCamelCase		).unsqueeze(0		)
 UpperCAmelCase			:      str			=			processor(UpperCamelCase    ,	return_tensors="""pt"""		).pixel_values

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

 # verify logits
 with torch.no_grad():
  UpperCAmelCase			:      Union[str, Any]			=			model(UpperCamelCase		)
  UpperCAmelCase			:      Optional[Any]			=			outputs.logits

 print("""Predicted class:"""    ,	logits.argmax(-1		).item()		)
 if base_model:
  UpperCAmelCase			:      Dict			=			timm_model.forward_features(UpperCamelCase		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCamelCase    ,	outputs.pooler_output    ,	atol=1e-3		)
 else:
  UpperCAmelCase			:      Union[str, Any]			=			timm_model(UpperCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase    ,	outputs.logits    ,	atol=1e-3		)
 print("""Looks ok!"""		)

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

 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__":
   A:     Optional[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."
   )

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





"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A:     List[Any]										=	(3, 9, -1_1, 0, 7, 5, 1, -1)
A:     Union[str, Any]										=	(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     int
   __lowerCAmelCase						:     Node | None



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Node | None			=			None
    for i in sorted(_SCREAMING_SNAKE_CASE   ,  reverse=_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Union[str, Any]			=			Node(_SCREAMING_SNAKE_CASE   ,  self.head		)

   def __iter__(     self		)						->   Iterator[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.head
    while node:
     yield node.data
     UpperCAmelCase			:      List[str]			=			node.next_node

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return sum(1 for _ in self		)




   def __str__(     self		)						->   str:





    '''simple docstring'''




    return " -> ".join([str(_SCREAMING_SNAKE_CASE		) for node in self]		)

def    _snake_case		(		UpperCamelCase		:	SortedLinkedList    ,	UpperCamelCase		:	SortedLinkedList		):
 return SortedLinkedList(list(UpperCamelCase		) + list(UpperCamelCase		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      A:     Dict										=	SortedLinkedList
      print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
