





'''simple docstring'''




import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							Tuple=()      ,    __lowerCAmelCase       :							Optional[int]=None      ,    __lowerCAmelCase       :							List[str]="no"      ,    __lowerCAmelCase       :							List[Any]="29500"		)		->  int:
   snake_case 						=   False
   snake_case 						=   False
   if any(key.startswith("""KAGGLE"""		) for key in os.environ.keys()		):
      snake_case 						=   True
   elif "IPython" in sys.modules:
      snake_case 						=   """google.colab""" in str(sys.modules["""IPython"""].get_ipython()		)

   try:
      snake_case 						=   PrecisionType(mixed_precision.lower()		)
   except ValueError:
      raise ValueError(
          F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''		)

   if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""      ,    __lowerCAmelCase		) is not None):
      # TPU launch
      import torch_xla.distributed.xla_multiprocessing as xmp

      if len(AcceleratorState._shared_state		) > 0:
         raise ValueError(
             """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
             """your training function. Restart your notebook and make sure no cells initializes an """
             """`Accelerator`."""		)
      if num_processes is None:
         snake_case 						=   8

      snake_case 						=   PrepareForLaunch(__lowerCAmelCase      ,    distributed_type="""TPU"""		)
      print(F'''Launching a training on {num_processes} TPU cores.'''		)
      xmp.spawn(__lowerCAmelCase      ,    args=__lowerCAmelCase      ,    nprocs=__lowerCAmelCase      ,    start_method="""fork"""		)
   elif in_colab:
      # No need for a distributed launch otherwise as it's either CPU or one GPU.
      if torch.cuda.is_available():
         print("""Launching training on one GPU."""		)
      else:
         print("""Launching training on one CPU."""		)
      function(*__lowerCAmelCase		)
   else:
      if num_processes is None:
         raise ValueError(
             """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""		)

      if num_processes > 1:
         # Multi-GPU launch
         from torch.multiprocessing import start_processes
         from torch.multiprocessing.spawn import ProcessRaisedException

         if len(AcceleratorState._shared_state		) > 0:
            raise ValueError(
                """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
                """inside your training function. Restart your notebook and make sure no cells initializes an """
                """`Accelerator`."""		)

         if torch.cuda.is_initialized():
            raise ValueError(
                """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
                """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
                """function."""		)

         # torch.distributed will expect a few environment variable to be here. We set the ones common to each
         # process here (the other ones will be set be the launcher).
         with patch_environment(
             world_size=__lowerCAmelCase      ,    master_addr="""127.0.01"""      ,    master_port=__lowerCAmelCase      ,    mixed_precision=__lowerCAmelCase		):
            snake_case 						=   PrepareForLaunch(__lowerCAmelCase      ,    distributed_type="""MULTI_GPU"""		)
            print(F'''Launching training on {num_processes} GPUs.'''		)
            try:
               start_processes(__lowerCAmelCase      ,    args=__lowerCAmelCase      ,    nprocs=__lowerCAmelCase      ,    start_method="""fork"""		)
            except ProcessRaisedException as e:
               if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                  raise RuntimeError(
                      """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
                      """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
                      """Please review your imports and test them when running the `notebook_launcher()` to identify """
                      """which one is problematic."""		) from e

      else:
         # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
         if is_mps_available():
            snake_case 						=   """1"""
            print("""Launching training on MPS."""		)
         elif torch.cuda.is_available():
            print("""Launching training on one GPU."""		)
         else:
            print("""Launching training on CPU."""		)
         function(*__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Optional[int]=()      ,    __lowerCAmelCase       :							Optional[int]=2		)		->  Union[str, Any]:
   from torch.multiprocessing import start_processes

   with tempfile.NamedTemporaryFile() as tmp_file:
      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=__lowerCAmelCase      ,    master_addr="""127.0.01"""      ,    master_port="""29500"""      ,    accelerate_mixed_precision="""no"""      ,    accelerate_debug_rdv_file=tmp_file.name      ,    accelerate_use_cpu="""yes"""      ,    ):
         snake_case 						=   PrepareForLaunch(__lowerCAmelCase      ,    debug=__lowerCAmelCase		)
         start_processes(__lowerCAmelCase      ,    args=__lowerCAmelCase      ,    nprocs=__lowerCAmelCase      ,    start_method="""fork"""		)








'''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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   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=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).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(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

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

   snake_case 						=   name
   print(__lowerCAmelCase		)

   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 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   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(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

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

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			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)

