




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 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)			->     List[str]:
     # Initialise PyTorch model
     a      =      json.load(open(__UpperCamelCase		,					"r"))
     a      =      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}''')
     a      =      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__":
   lowercase__      :			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(
       "--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."
   )
   lowercase__      :			List[str]           =					parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

