





'''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_					:    str      	=			logging.get_logger(__name__)






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      Union[str, Any]=False						)	->	Optional[Any]:
				_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  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Tuple    ,							lowercase						:      List[str]=False						)	->	List[Any]:
				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  _lowerCamelCase				( lowercase						:      str						)	->	List[str]:
				_a								= ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowercase    ,							lowercase						)






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      List[str]    ,							lowercase						:      Union[str, Any]						)	->	int:
				_a								= dct.pop(lowercase						)
				_a								= val






def  _lowerCamelCase				( )	->	Dict:
				_a								= "http://images.cocodataset.org/val2017/000000039769.jpg"
				_a								= Image.open(requests.get(lowercase    ,							stream=lowercase						).raw						)
				return im






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

				# load original model from timm
				_a								= timm.create_model(lowercase    ,							pretrained=lowercase						)
				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_(lowercase						)
				_a								= create_rename_keys(lowercase    ,							lowercase						)
				for src, dest in rename_keys:
								rename_key(lowercase    ,							lowercase    ,							lowercase						)
				read_in_q_k_v(lowercase    ,							lowercase    ,							lowercase						)

				_a								= "huggingface/label-files"
				_a								= "imagenet-1k-id2label.json"
				_a								= json.load(open(hf_hub_download(lowercase    ,							lowercase    ,							repo_type="dataset"						)    ,							"r"						)						)
				_a								= {int(lowercase						): 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(lowercase						).eval()
				else:
								_a								= ViTHybridForImageClassification(lowercase						).eval()
				model.load_state_dict(lowercase						)

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

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

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

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

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

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

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

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

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



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






