
"""simple docstring"""




import inspect
import re

from transformers.utils import direct_transformers_import


# 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
_A			:       Tuple   		=       """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_A			:       Optional[Any]   		=       direct_transformers_import(PATH_TO_TRANSFORMERS)

_A			:       Union[str, Any]   		=       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)`
_A			:       Dict   		=       re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_A			:       Any   		=       {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Optional[int]:
       lowercase    :				List[str]       		=							None

       # source code of `config_class`
       lowercase    :				Optional[int]       		=							inspect.getsource(__snake_case  )
       lowercase    :				Union[str, Any]       		=							_re_checkpoint.findall(__snake_case  )

       # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
       # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
       for ckpt_name, ckpt_link in checkpoints:
              # allow the link to end with `/`
              if ckpt_link.endswith("/"  ):
                     lowercase    :				Dict       		=							ckpt_link[:-1]

              # verify the checkpoint name corresponds to the checkpoint link
              lowercase    :				List[Any]       		=							f"""https://huggingface.co/{ckpt_name}"""
              if ckpt_link == ckpt_link_from_name:
                     lowercase    :				Optional[int]       		=							ckpt_name
                     break

       return checkpoint


def   __magic_name__	(      ) ->				List[Any]:
       lowercase    :				Union[str, Any]       		=							[]

       for config_class in list(CONFIG_MAPPING.values()  ):
              # Skip deprecated models
              if "models.deprecated" in config_class.__module__:
                     continue
              lowercase    :				int       		=							get_checkpoint_from_config_class(__snake_case  )

              lowercase    :				Optional[int]       		=							config_class.__name__
              if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
                     configs_without_checkpoint.append(__snake_case  )

       if len(__snake_case  ) > 0:
              lowercase    :				Union[str, Any]       		=							"\n".join(sorted(__snake_case  )  )
              raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}"""  )


if __name__ == "__main__":
   check_config_docstrings_have_checkpoints()




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


