



'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :						int    =			logging.get_logger()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = True       ):







  '''simple docstring'''



  print(f'''Converting {name}...'''       )

  with torch.no_grad():
    if hidden_sizes == 1_28:
      if name[-1] == "S":
        _snake_case     		=				timm.create_model("levit_128s"					,	pretrained=SCREAMING_SNAKE_CASE__       )
      else:
        _snake_case     		=				timm.create_model("levit_128"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 1_92:
      _snake_case     		=				timm.create_model("levit_192"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 2_56:
      _snake_case     		=				timm.create_model("levit_256"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 3_84:
      _snake_case     		=				timm.create_model("levit_384"					,	pretrained=SCREAMING_SNAKE_CASE__       )

    from_model.eval()
    _snake_case     		=				LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__       ).eval()
    _snake_case     		=				OrderedDict()

    _snake_case     		=				from_model.state_dict()
    _snake_case     		=				list(from_model.state_dict().keys()       )
    _snake_case     		=				list(our_model.state_dict().keys()       )
    print(len(SCREAMING_SNAKE_CASE__       )					,	len(SCREAMING_SNAKE_CASE__       )       )
    for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
      _snake_case     		=				weights[og_keys[i]]
    our_model.load_state_dict(SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				torch.randn((2, 3, 2_24, 2_24)       )
    _snake_case     		=				from_model(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				our_model(SCREAMING_SNAKE_CASE__       ).logits

  assert torch.allclose(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ), "The model logits don't match the original one."

  _snake_case     		=				name
  print(SCREAMING_SNAKE_CASE__       )

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name       )
    _snake_case     		=				LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name       )

    print(f'''Pushed {checkpoint_name}'''       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = True       ):







  '''simple docstring'''



  _snake_case     		=				"imagenet-1k-id2label.json"
  _snake_case     		=				10_00
  _snake_case     		=				(1, num_labels)

  _snake_case     		=				"huggingface/label-files"
  _snake_case     		=				num_labels
  _snake_case     		=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       )       )
  _snake_case     		=				{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}

  _snake_case     		=				idalabel
  _snake_case     		=				{v: k for k, v in idalabel.items()}

  _snake_case     		=				partial(SCREAMING_SNAKE_CASE__					,	num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				{
      "levit-128S": 1_28,
      "levit-128": 1_28,
      "levit-192": 1_92,
      "levit-256": 2_56,
      "levit-384": 3_84,
  }

  _snake_case     		=				{
      "levit-128S": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 6, 8]					,	depths=[2, 3, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
      "levit-128": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 8, 12]					,	depths=[4, 4, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
      "levit-192": ImageNetPreTrainedConfig(
          hidden_sizes=[1_92, 2_88, 3_84]					,	num_attention_heads=[3, 5, 6]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
      "levit-256": ImageNetPreTrainedConfig(
          hidden_sizes=[2_56, 3_84, 5_12]					,	num_attention_heads=[4, 6, 8]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
      "levit-384": ImageNetPreTrainedConfig(
          hidden_sizes=[3_84, 5_12, 7_68]					,	num_attention_heads=[6, 9, 12]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0.1					,	),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE__					,	names_to_config[model_name]					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  return config, expected_shape


if __name__ == "__main__":
 __magic_name__   :						List[Any]    =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""levit-dump-folder/""",
     type=Path,
     required=False,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
 parser.add_argument(
     """--no-push_to_hub""",
     dest="""push_to_hub""",
     action="""store_false""",
     help="""Do not push model and image processor to the hub""",
 )

 __magic_name__   :						str    =			parser.parse_args()
 __magic_name__   :						Path    =			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


