





"""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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a		:				Union[str, Any]  			=    logging.get_logger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]					, _lowercase		:					str=False      )				->Tuple:






 '''simple docstring'''






 a   :	Dict			=       []
 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""")      )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("cls_token", "vit.embeddings.cls_token"),
         ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
         ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
         ("pos_embed", "vit.embeddings.position_embeddings"),
     ]      )

 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   :	int			=       [(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"),
      ]      )

 return rename_keys






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					Optional[int]					, _lowercase		:					Dict=False      )				->str:






 '''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->Dict:






 '''simple docstring'''






 a   :	Optional[int]			=       ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(__snake_case					, __snake_case      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					str					, _lowercase		:					Tuple      )				->Any:






 '''simple docstring'''






 a   :	Optional[Any]			=       dct.pop(__snake_case      )
 a   :	List[str]			=       val






def    _SCREAMING_SNAKE_CASE	(			)				->Dict:






 '''simple docstring'''






 a   :	int			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
 a   :	Dict			=       Image.open(requests.get(__snake_case					, stream=__snake_case      ).raw      )
 return im






@torch.no_grad()
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					Any      )				->Union[str, Any]:






 '''simple docstring'''






 a   :	List[str]			=       ViTConfig()
 a   :	Any			=       False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  a   :	Union[str, Any]			=       True
  a   :	int			=       int(vit_name[-12:-10]      )
  a   :	str			=       int(vit_name[-9:-6]      )
 else:
  a   :	Any			=       1000
  a   :	str			=       "huggingface/label-files"
  a   :	Optional[Any]			=       "imagenet-1k-id2label.json"
  a   :	str			=       json.load(open(hf_hub_download(__snake_case					, __snake_case					, repo_type="dataset"      )					, "r"      )      )
  a   :	str			=       {int(__snake_case      ): v for k, v in idalabel.items()}
  a   :	Tuple			=       idalabel
  a   :	Any			=       {v: k for k, v in idalabel.items()}
  a   :	List[str]			=       int(vit_name[-6:-4]      )
  a   :	Tuple			=       int(vit_name[-3:]      )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"      ):
   a   :	Union[str, Any]			=       192
   a   :	Any			=       768
   a   :	int			=       12
   a   :	List[str]			=       3
  elif vit_name[9:].startswith("small"      ):
   a   :	List[Any]			=       384
   a   :	Dict			=       1536
   a   :	Optional[Any]			=       12
   a   :	Optional[int]			=       6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"      ):
   a   :	Any			=       768
   a   :	int			=       2304
   a   :	Optional[int]			=       8
   a   :	Any			=       8
  elif vit_name[4:].startswith("base"      ):
   pass
  elif vit_name[4:].startswith("large"      ):
   a   :	List[Any]			=       1024
   a   :	Any			=       4096
   a   :	Dict			=       24
   a   :	Any			=       16
  elif vit_name[4:].startswith("huge"      ):
   a   :	int			=       1280
   a   :	Optional[Any]			=       5120
   a   :	Any			=       32
   a   :	Dict			=       16

    # load original model from timm
 a   :	List[Any]			=       timm.create_model(__snake_case					, pretrained=__snake_case      )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 a   :	Union[str, Any]			=       timm_model.state_dict()
 if base_model:
  remove_classification_head_(__snake_case      )
 a   :	str			=       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      )

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  a   :	Optional[Any]			=       ViTModel(__snake_case      ).eval()
 else:
  a   :	str			=       ViTForImageClassification(__snake_case      ).eval()
 model.load_state_dict(__snake_case      )

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  a   :	Optional[Any]			=       DeiTImageProcessor(size=config.image_size      )
 else:
  a   :	str			=       ViTImageProcessor(size=config.image_size      )
 a   :	Union[str, Any]			=       image_processor(images=prepare_img()					, return_tensors="pt"      )
 a   :	str			=       encoding["pixel_values"]
 a   :	Union[str, Any]			=       model(__snake_case      )

 if base_model:
  a   :	Union[str, Any]			=       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:
  a   :	Dict			=       timm_model(__snake_case      )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__snake_case					, outputs.logits					, atol=1E-3      )

 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 image processor to {pytorch_dump_folder_path}"""      )
 image_processor.save_pretrained(__snake_case      )


if __name__ == "__main__":
  a		:				List[Any]  			=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--vit_name''',
      default='''vit_base_patch16_224''',
      type=str,
      help='''Name of the 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.'''
  )

  a		:				List[str]  			=    parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









"""simple docstring"""




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


