






"""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()
A__					: List[str]=	logging.get_logger()







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = True					)					->	Optional[Any]:
    """simple docstring"""

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

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

        from_model.eval()
        UpperCamelCase__         =    LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE					).eval()
        UpperCamelCase__         =    OrderedDict()

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

        UpperCamelCase__         =    torch.randn((2, 3, 2_24, 2_24)					)
        UpperCamelCase__         =    from_model(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    our_model(SCREAMING_SNAKE_CASE					).logits

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

    UpperCamelCase__         =    name
    print(SCREAMING_SNAKE_CASE					)

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

        print(F'Pushed {checkpoint_name}'					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = True					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    'imagenet-1k-id2label.json'
    UpperCamelCase__         =    10_00
    UpperCamelCase__         =    (1, num_labels)

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

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

    UpperCamelCase__         =    partial(SCREAMING_SNAKE_CASE ,	num_labels=SCREAMING_SNAKE_CASE ,	idalabel=SCREAMING_SNAKE_CASE ,	labelaid=SCREAMING_SNAKE_CASE					)

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

    UpperCamelCase__         =    {
        '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__":
  A__					: 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""",
  )

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





import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


A__					: Union[str, Any]=	logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests."""  )
@require_torch
@require_tf
@slow
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = True ,				)		->       Tuple:
       UpperCamelCase__         =    [file for file in os.listdir(snake_case_      ) if os.path.isfile(os.path.join(snake_case_ ,				snake_case_      )      )]

       if identifier is not None:
           UpperCamelCase__         =    [file for file in files if identifier in file]

       if n_identifier is not None:
           if isinstance(snake_case_ ,				snake_case_      ):
               for n_ in n_identifier:
                   UpperCamelCase__         =    [file for file in files if n_ not in file]
           else:
               UpperCamelCase__         =    [file for file in files if n_identifier not in file]

       UpperCamelCase__         =    ignore_files or []
       ignore_files.append('__init__.py'      )
       UpperCamelCase__         =    [file for file in files if file not in ignore_files]

       for file in files:
           # Open all files
           print('Testing' ,				snake_case_      )

           if only_modules:
               UpperCamelCase__         =    file.split('.'      )[0]
               try:
                   UpperCamelCase__         =    getattr(snake_case_ ,				snake_case_      )
                   UpperCamelCase__         =    doctest.DocTestSuite(snake_case_      )
                   UpperCamelCase__         =    unittest.TextTestRunner().run(snake_case_      )
                   self.assertIs(len(result.failures      ) ,				0      )
               except AttributeError:
                   logger.info(F'{module_identifier} is not a module.'      )
           else:
               UpperCamelCase__         =    doctest.testfile(str('..' / directory / file      ) ,				optionflags=doctest.ELLIPSIS      )
               self.assertIs(result.failed ,				0      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'modeling'
       UpperCamelCase__         =    [
           'modeling_ctrl.py',
           'modeling_tf_ctrl.py',
       ]
       self.analyze_directory(snake_case_ ,				identifier=snake_case_ ,				ignore_files=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'tokenization'
       self.analyze_directory(snake_case_ ,				identifier=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    'configuration'
       self.analyze_directory(snake_case_ ,				identifier=snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    Path('src/transformers'      )
       UpperCamelCase__         =    ['configuration', 'modeling', 'tokenization']
       self.analyze_directory(snake_case_ ,				n_identifier=snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    Path('docs/source'      )
       UpperCamelCase__         =    ['favicon.ico']
       self.analyze_directory(snake_case_ ,				ignore_files=snake_case_ ,				only_modules=snake_case_      )




