





"""simple docstring"""


import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)     ->	Union[str, Any]:

 '''simple docstring'''






 snake_case__     				=					json.load(open(_UpperCAmelCase						,  "r"						)						)
 snake_case__     				=					GPTNeoConfig(
     hidden_size=config_json["n_embd"]						,  num_layers=config_json["n_layer"]						,  num_heads=config_json["n_head"]						,  attention_types=config_json["attention_types"]						,  max_position_embeddings=config_json["n_positions"]						,  resid_dropout=config_json["res_dropout"]						,  embed_dropout=config_json["embed_dropout"]						,  attention_dropout=config_json["attn_dropout"]						,  )
 print(f"Building PyTorch model from configuration: {config}"						)
 snake_case__     				=					GPTNeoForCausalLM(_UpperCAmelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_gpt_neo(_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"						)
 model.save_pretrained(_UpperCAmelCase						)


if __name__ == "__main__":
   a__       :  Optional[int]       		=					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 mesh-tf 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__       :  Optional[int]       		=					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""


from typing import TYPE_CHECKING

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


a__       :  List[Any]       		=					{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Union[str, Any]       		=					["""RealmTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Dict       		=					[
       """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RealmEmbedder""",
       """RealmForOpenQA""",
       """RealmKnowledgeAugEncoder""",
       """RealmPreTrainedModel""",
       """RealmReader""",
       """RealmScorer""",
       """load_tf_weights_in_realm""",
   ]
   a__       :  Any       		=					["""RealmRetriever"""]


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

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

