






"""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()
UpperCamelCase_	:				Tuple										= logging.get_logger()



def     __lowercase					(    a :				int       ,		a :				str       ,		a :				LevitConfig       ,		a :				Path       ,		a :				bool = True )					->					Any:
      print(f'''Converting {name}...''' )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        __snake_case      :    Tuple  			=timm.create_model('''levit_128s'''       ,		pretrained=a )
                  else:
                        __snake_case      :    int  			=timm.create_model('''levit_128'''       ,		pretrained=a )
            if hidden_sizes == 192:
                  __snake_case      :    str  			=timm.create_model('''levit_192'''       ,		pretrained=a )
            if hidden_sizes == 256:
                  __snake_case      :    str  			=timm.create_model('''levit_256'''       ,		pretrained=a )
            if hidden_sizes == 384:
                  __snake_case      :    List[str]  			=timm.create_model('''levit_384'''       ,		pretrained=a )

            from_model.eval()
            __snake_case      :    Optional[int]  			=LevitForImageClassificationWithTeacher(a ).eval()
            __snake_case      :    Dict  			=OrderedDict()

            __snake_case      :    int  			=from_model.state_dict()
            __snake_case      :    List[str]  			=list(from_model.state_dict().keys() )
            __snake_case      :    List[str]  			=list(our_model.state_dict().keys() )
            print(len(a )       ,		len(a ) )
            for i in range(len(a ) ):
                  __snake_case      :    List[Any]  			=weights[og_keys[i]]
            our_model.load_state_dict(a )

            __snake_case      :    Optional[Any]  			=torch.randn((2, 3, 224, 224) )
            __snake_case      :    Tuple  			=from_model(a )
            __snake_case      :    Optional[int]  			=our_model(a ).logits

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

      __snake_case      :    List[str]  			=name
      print(a )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name )
            __snake_case      :    Optional[Any]  			=LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name )

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



def     __lowercase					(    a :				Path       ,		a :				str = None       ,		a :				bool = True )					->					Optional[Any]:
      __snake_case      :    Dict  			='''imagenet-1k-id2label.json'''
      __snake_case      :    Optional[int]  			=1_000
      __snake_case      :    Optional[int]  			=(1, num_labels)

      __snake_case      :    List[Any]  			='''huggingface/label-files'''
      __snake_case      :    List[str]  			=num_labels
      __snake_case      :    List[str]  			=json.load(open(hf_hub_download(a       ,		a       ,		repo_type='''dataset''' )       ,		'''r''' ) )
      __snake_case      :    int  			={int(a ): v for k, v in idalabel.items()}

      __snake_case      :    Any  			=idalabel
      __snake_case      :    Optional[Any]  			={v: k for k, v in idalabel.items()}

      __snake_case      :    int  			=partial(a       ,		num_labels=a       ,		idalabel=a       ,		labelaid=a )

      __snake_case      :    List[str]  			={
          '''levit-128S''': 128,
          '''levit-128''': 128,
          '''levit-192''': 192,
          '''levit-256''': 256,
          '''levit-384''': 384,
      }

      __snake_case      :    str  			={
          '''levit-128S''': ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[2, 3, 4]       ,		key_dim=[16, 16, 16]       ,		drop_path_rate=0       ,		),
          '''levit-128''': ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]       ,		num_attention_heads=[4, 8, 12]       ,		depths=[4, 4, 4]       ,		key_dim=[16, 16, 16]       ,		drop_path_rate=0       ,		),
          '''levit-192''': ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]       ,		num_attention_heads=[3, 5, 6]       ,		depths=[4, 4, 4]       ,		key_dim=[32, 32, 32]       ,		drop_path_rate=0       ,		),
          '''levit-256''': ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[4, 4, 4]       ,		key_dim=[32, 32, 32]       ,		drop_path_rate=0       ,		),
          '''levit-384''': ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]       ,		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]       ,		a       ,		names_to_config[model_name]       ,		a       ,		a )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]       ,		a       ,		a       ,		a       ,		a )
      return config, expected_shape


if __name__ == "__main__":
    UpperCamelCase_	:				Optional[int]										= 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""",
    )

    UpperCamelCase_	:				Any										= parser.parse_args()
    UpperCamelCase_	:				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"""





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase_	:				int										= subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCamelCase_	:				Optional[int]										= (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

UpperCamelCase_	:				List[Any]										= """|""".join(sys.argv[1:])
UpperCamelCase_	:				List[str]										= re.compile(rF'''^({joined_dirs}).*?\.py$''')

UpperCamelCase_	:				str										= [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


