
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_=None		,   a_=None						)		->	Any:







				"""simple docstring"""



				return field(default_factory=lambda: default		,   metadata=a_						)





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     list_field(
	    default=[]			,			metadata={
	        "help": (
	            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
	            " of all available models"
	        )
	    }			,			)

	snake_case_					    =     list_field(
	    default=[8]			,			metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}  )

	snake_case_					    =     list_field(
	    default=[8, 32, 128, 512]			,			metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}			,			)

	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Use FP16 to accelerate inference."}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Benchmark training of model"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Verbose memory tracing"}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }			,			)
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Trace memory line by line"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Save result to a CSV file"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Save all print statements in a log file"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to print environment information"}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={
	        "help": (
	            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
	            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
	            " for debugging / testing and on TPU."
	        )
	    }			,			)
	snake_case_					    =     field(
	    default=F"""inference_time_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving time results to csv."}			,			)
	snake_case_					    =     field(
	    default=F"""inference_memory_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving memory results to csv."}			,			)
	snake_case_					    =     field(
	    default=F"""train_time_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving time results to csv for training."}			,			)
	snake_case_					    =     field(
	    default=F"""train_memory_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving memory results to csv for training."}			,			)
	snake_case_					    =     field(
	    default=F"""env_info_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving environment information."}			,			)
	snake_case_					    =     field(
	    default=F"""log_{round(time()  )}.csv"""			,			metadata={"help": "Log filename used if print statements are saved in log."}			,			)
	snake_case_					    =     field(default=3			,			metadata={"help": "Times an experiment will be run."}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }			,			)






	def 				UpperCamelCase_  (       self					: List[str] ):
					warnings.warn(
					    f'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
					    " are deprecated in general and it is advised to use external Benchmarking libraries "
					    " to benchmark Transformer models."							,A							,)






	def 				UpperCamelCase_  (       self					: Tuple ):
					return json.dumps(dataclasses.asdict(self )							,indent=2 )






	@property
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					if len(self.models ) <= 0:
									raise ValueError(
									    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
									    " bert-base-cased` or `args.models = ['bert-base-cased']." )
					return self.models







	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					if not self.multi_process:
									return False
					elif self.is_tpu:
									logger.info("Multiprocessing is currently not possible on TPU." )
									return False
					else:
									return True








"""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			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

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

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


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= 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."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


