





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__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  # Initialise PyTorch model
  __snake_case:		int					    =		json.load(open(SCREAMING_SNAKE_CASE__   ,   """r"""))
  __snake_case:		Tuple					    =		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:		List[Any]					    =		GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__)

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

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}''')
  model.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase			:  List[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(
          "--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."
      )
      __UpperCAmelCase			:  int										=				parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase			:  Optional[Any]										=				{"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SEWDForCTC",
          "SEWDForSequenceClassification",
          "SEWDModel",
          "SEWDPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_sew_d import (
                SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                SEWDForCTC,
                SEWDForSequenceClassification,
                SEWDModel,
                SEWDPreTrainedModel,
            )


else:
      import sys

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






