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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "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  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'yoso'


	def __init__(   self       ,  lowerCAmelCase_=50265       ,  lowerCAmelCase_=768       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=3072       ,  lowerCAmelCase_="gelu"       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=4096       ,  lowerCAmelCase_=1       ,  lowerCAmelCase_=0.0_2       ,  lowerCAmelCase_=1e-12       ,  lowerCAmelCase_="absolute"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=9       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=1       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=2       ,  **lowerCAmelCase_       ,  ):
		super().__init__(pad_token_id=lowerCAmelCase_       ,  bos_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  **lowerCAmelCase_       )

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








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__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=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}'							)
     __UpperCamelCase       				=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)





