



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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Union[str, Any]     		="""gpt_neox"""




				def __init__(						self					:						Optional[int],					__lowercase					:						int=5_0432,					__lowercase					:						Tuple=6144,					__lowercase					:						Optional[int]=44,					__lowercase					:						Any=64,					__lowercase					:						Dict=2_4576,					__lowercase					:						Any="gelu",					__lowercase					:						List[Any]=0.25,					__lowercase					:						Tuple=1_0000,					__lowercase					:						Optional[int]=0.0,					__lowercase					:						List[str]=0.0,					__lowercase					:						List[str]=0.1,					__lowercase					:						Tuple=2048,					__lowercase					:						str=0.02,					__lowercase					:						Union[str, Any]=1e-5,					__lowercase					:						Optional[int]=True,					__lowercase					:						Any=0,					__lowercase					:						Union[str, Any]=2,					__lowercase					:						Dict=False,					__lowercase					:						Dict=True,					__lowercase					:						Optional[Any]=None,					**__lowercase					:						int,					):
							super().__init__(bos_token_id=__lowercase,					eos_token_id=__lowercase,					**__lowercase						)
							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__					  =				rotary_pct
							lowercase__					  =				rotary_emb_base
							lowercase__					  =				attention_dropout
							lowercase__					  =				hidden_dropout
							lowercase__					  =				classifier_dropout
							lowercase__					  =				initializer_range
							lowercase__					  =				layer_norm_eps
							lowercase__					  =				use_cache
							lowercase__					  =				tie_word_embeddings
							lowercase__					  =				use_parallel_residual
							lowercase__					  =				rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
										raise ValueError(
										    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"						)




				def 							A__      (						self					:						str						):
							if self.rope_scaling is None:
										return

							if not isinstance(self.rope_scaling,					__lowercase						) or len(self.rope_scaling						) != 2:
										raise ValueError(
										    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
										    F'''got {self.rope_scaling}'''						)
							lowercase__					  =				self.rope_scaling.get("type",					__lowercase						)
							lowercase__					  =				self.rope_scaling.get("factor",					__lowercase						)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
										raise ValueError(
										    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''						)
							if rope_scaling_factor is None or not isinstance(__lowercase,					__lowercase						) or rope_scaling_factor <= 1.0:
										raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''						)









import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			lowercase__					  =				(
			    ("layer.", "layer_"),
			    ("word_embeddings.weight", "word_embeddings"),
			    ("position_embeddings.weight", "position_embeddings"),
			    ("token_type_embeddings.weight", "token_type_embeddings"),
			    (".", "/"),
			    ("LayerNorm/weight", "LayerNorm/gamma"),
			    ("LayerNorm/bias", "LayerNorm/beta"),
			    ("weight", "kernel"),
			)

			if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
						os.makedirs(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				model.state_dict()

			def to_tf_var_name(SCREAMING_SNAKE_CASE_  ):
						for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
									lowercase__					  =				name.replace(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
						return f'''bert/{name}'''

			def create_tf_var(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
						lowercase__					  =				tf.dtypes.as_dtype(tensor.dtype  )
						lowercase__					  =				tf.get_variable(dtype=SCREAMING_SNAKE_CASE_						,  shape=tensor.shape						,  name=SCREAMING_SNAKE_CASE_						,  initializer=tf.zeros_initializer()  )
						session.run(tf.variables_initializer([tf_var]  )  )
						session.run(SCREAMING_SNAKE_CASE_  )
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									lowercase__					  =				to_tf_var_name(SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose  ):
												lowercase__					  =				torch_tensor.T
									lowercase__					  =				create_tf_var(tensor=SCREAMING_SNAKE_CASE_						,  name=SCREAMING_SNAKE_CASE_						,  session=SCREAMING_SNAKE_CASE_  )
									tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				session.run(SCREAMING_SNAKE_CASE_  )
									print(f'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )}'''  )

						lowercase__					  =				tf.train.Saver(tf.trainable_variables()  )
						saver.save(SCREAMING_SNAKE_CASE_						,  os.path.join(SCREAMING_SNAKE_CASE_						,  model_name.replace("-"						,  "_"  ) + ".ckpt"  )  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=None  ):
			lowercase__					  =				argparse.ArgumentParser()
			parser.add_argument("--model_name"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="model name e.g. bert-base-uncased"  )
			parser.add_argument(
			    "--cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory containing pytorch model"  )
			parser.add_argument("--pytorch_model_path"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="/path/to/<pytorch-model-name>.bin"  )
			parser.add_argument("--tf_cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory in which to save tensorflow model"  )
			lowercase__					  =				parser.parse_args(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name						,  state_dict=torch.load(args.pytorch_model_path  )						,  cache_dir=args.cache_dir						,  )

			convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_						,  ckpt_dir=args.tf_cache_dir						,  model_name=args.model_name  )


if __name__ == "__main__":
							main()




