


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__    							= logging.get_logger()




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A = True							)					->    Dict:







      '''simple docstring'''


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

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        UpperCAmelCase__								=  timm.create_model("levit_128s",  pretrained=_a							)
                  else:
                        UpperCAmelCase__								=  timm.create_model("levit_128",  pretrained=_a							)
            if hidden_sizes == 192:
                  UpperCAmelCase__								=  timm.create_model("levit_192",  pretrained=_a							)
            if hidden_sizes == 256:
                  UpperCAmelCase__								=  timm.create_model("levit_256",  pretrained=_a							)
            if hidden_sizes == 384:
                  UpperCAmelCase__								=  timm.create_model("levit_384",  pretrained=_a							)

            from_model.eval()
            UpperCAmelCase__								=  LevitForImageClassificationWithTeacher(_a							).eval()
            UpperCAmelCase__								=  OrderedDict()

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

            UpperCAmelCase__								=  torch.randn((2, 3, 224, 224)							)
            UpperCAmelCase__								=  from_model(_a							)
            UpperCAmelCase__								=  our_model(_a							).logits

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

      UpperCAmelCase__								=  name
      print(_a							)

      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_			(					__A,  __A = None,  __A = True							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  (1, num_labels)

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

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

      UpperCAmelCase__								=  partial(_a,  num_labels=_a,  idalabel=_a,  labelaid=_a							)

      UpperCAmelCase__								=  {
          "levit-128S": 128,
          "levit-128": 128,
          "levit-192": 192,
          "levit-256": 256,
          "levit-384": 384,
      }

      UpperCAmelCase__								=  {
          "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__    							= 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__    							= parser.parse_args()
      UpperCamelCase__    							= 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)




from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



