






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


UpperCAmelCase__						:Tuple          =			{
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:int          =			[
       """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LongT5EncoderModel""",
       """LongT5ForConditionalGeneration""",
       """LongT5Model""",
       """LongT5PreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Optional[Any]          =			[
       """FlaxLongT5ForConditionalGeneration""",
       """FlaxLongT5Model""",
       """FlaxLongT5PreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longta import (
          LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongTaEncoderModel,
          LongTaForConditionalGeneration,
          LongTaModel,
          LongTaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_longta import (
          FlaxLongTaForConditionalGeneration,
          FlaxLongTaModel,
          FlaxLongTaPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:Optional[Any]          =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











'''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     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # Initialise PyTorch model
     __lowerCamelCase						:       str				   =				RemBertConfig.from_json_file(_lowercase     )
     print("""Building PyTorch model from configuration: {}""".format(str(_lowercase     )     )     )
     __lowerCamelCase						:       List[Any]				   =				RemBertModel(_lowercase     )

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

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


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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."""
   )
   UpperCAmelCase__						:List[Any]          =			parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



