

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__A						   =			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Any         =  """albert"""

				def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[Any]=3_0_0_0_0     ,  UpperCamelCase__							:				int=1_2_8     ,  UpperCamelCase__							:				List[Any]=4_0_9_6     ,  UpperCamelCase__							:				Any=1_2     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Union[str, Any]=6_4     ,  UpperCamelCase__							:				int=1_6_3_8_4     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				Tuple="gelu_new"     ,  UpperCamelCase__							:				List[str]=0     ,  UpperCamelCase__							:				List[str]=0     ,  UpperCamelCase__							:				Tuple=5_1_2     ,  UpperCamelCase__							:				Any=2     ,  UpperCamelCase__							:				Dict=0.02     ,  UpperCamelCase__							:				List[str]=1e-12     ,  UpperCamelCase__							:				List[str]=0.1     ,  UpperCamelCase__							:				int="absolute"     ,  UpperCamelCase__							:				List[str]=0     ,  UpperCamelCase__							:				List[str]=2     ,  UpperCamelCase__							:				str=3     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[str]:



						'''simple docstring'''


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

						__lowerCAmelCase:       Union[str, Any]          =			vocab_size
						__lowerCAmelCase:       str          =			embedding_size
						__lowerCAmelCase:       Union[str, Any]          =			hidden_size
						__lowerCAmelCase:       Optional[Any]          =			num_hidden_layers
						__lowerCAmelCase:       List[Any]          =			num_hidden_groups
						__lowerCAmelCase:       Dict          =			num_attention_heads
						__lowerCAmelCase:       List[Any]          =			inner_group_num
						__lowerCAmelCase:       str          =			hidden_act
						__lowerCAmelCase:       str          =			intermediate_size
						__lowerCAmelCase:       str          =			hidden_dropout_prob
						__lowerCAmelCase:       Tuple          =			attention_probs_dropout_prob
						__lowerCAmelCase:       Tuple          =			max_position_embeddings
						__lowerCAmelCase:       List[str]          =			type_vocab_size
						__lowerCAmelCase:       Tuple          =			initializer_range
						__lowerCAmelCase:       List[Any]          =			layer_norm_eps
						__lowerCAmelCase:       int          =			classifier_dropout_prob
						__lowerCAmelCase:       int          =			position_embedding_type





class     snake_case	(				__snake_case  ):

				@property
				def 							lowercase_     (	self							:				List[Any])->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						if self.task == "multiple-choice":
								__lowerCAmelCase:       Optional[Any]          =			{0: "batch", 1: "choice", 2: "sequence"}
						else:
								__lowerCAmelCase:       List[Any]          =			{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ])





"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

		# Load weights from numpy
		load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		# Save pytorch-model
		__lowerCAmelCase:       str          =			pytorch_dump_folder_path + "/" + WEIGHTS_NAME
		__lowerCAmelCase:       List[Any]          =			pytorch_dump_folder_path + "/" + CONFIG_NAME
		print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
		torch.save(model.state_dict()					, __SCREAMING_SNAKE_CASE )
		print(F"Save configuration file to {pytorch_config_dump_path}" )
		with open(__SCREAMING_SNAKE_CASE					, "w"					, encoding="utf-8" ) as f:
				f.write(config.to_json_string() )


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--gpt2_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
			        "This specifies the model architecture."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


