




'''simple docstring'''



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_							:	Optional[Any]	     =		'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_							:	Any	     =		importlib.util.spec_from_file_location(
    '''transformers''',
    os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase_							:	Union[str, Any]	     =		spec.loader.load_module()

UpperCAmelCase_							:	str	     =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase_							:	Optional[Any]	     =		re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


UpperCAmelCase_							:	int	     =		{
    '''CLIPConfigMixin''',
    '''DecisionTransformerConfigMixin''',
    '''EncoderDecoderConfigMixin''',
    '''RagConfigMixin''',
    '''SpeechEncoderDecoderConfigMixin''',
    '''VisionEncoderDecoderConfigMixin''',
    '''VisionTextDualEncoderConfigMixin''',
}







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	[]

     for config_class in list(CONFIG_MAPPING.values()						):
          __snake_case            =	False

          # source code of `config_class`
          __snake_case            =	inspect.getsource(_lowerCamelCase						)
          __snake_case            =	_re_checkpoint.findall(_lowerCamelCase						)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __snake_case       ,     __snake_case            =	checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __snake_case            =	f'''https://huggingface.co/{ckpt_name}'''
               if ckpt_link == ckpt_link_from_name:
                    __snake_case            =	True
                    break

          __snake_case            =	config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(_lowerCamelCase						)

     if len(_lowerCamelCase						) > 0:
          __snake_case            =	'''\n'''.join(sorted(_lowerCamelCase						)						)
          raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}'''						)


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()












import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			torch.save(model.state_dict()  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



