
'''simple docstring'''
import torch

from accelerate import Accelerator, DistributedType






class  UpperCAmelCase_					:





 def __enter__(     self):
     if self.enabled:
         snake_case_			:     str        =    self.model.no_sync()
         self.model_sync_obj.__enter__()

     return self





 def __exit__(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     if self.enabled:
         # Average all models on exit
         self._sync_and_avg_model_params()
         self.model_sync_obj.__exit__(lowercase_			,							lowercase_			,							lowercase_)





 def __init__(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_ = True):
     if accelerator.distributed_type not in [
         DistributedType.NO,
         DistributedType.MULTI_CPU,
         DistributedType.MULTI_GPU,
     ]:
         raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)")
     snake_case_			:     List[str]        =    enabled and accelerator.distributed_type != DistributedType.NO
     snake_case_			:     int        =    0
     if self.enabled:
         snake_case_			:     List[Any]        =    accelerator
         snake_case_			:     Dict        =    model
         snake_case_			:     Optional[Any]        =    local_sgd_steps





 def 							snake_case__		(     self):
     self.num_steps += 1
     if not self.enabled:
         return

     if self.num_steps % self.local_sgd_steps == 0:
         self._sync_and_avg_model_params()





 def 							snake_case__		(     self):
     self.accelerator.wait_for_everyone()
     with self.accelerator.autocast():
         for param in self.model.parameters():
             snake_case_			:     Optional[Any]        =    self.accelerator.reduce(param.data			,							reduction="mean")






'''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     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    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_			:     Optional[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__":
  a_           =    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_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




