






from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE__		:							List[Any] 				=				logging.get_logger(__name__)







class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  42
     __lowerCamelCase      				=  None




     @staticmethod
     def   __UpperCAmelCase  (				):
          raise NotImplementedError




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          raise NotImplementedError




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          raise NotImplementedError




     def   __UpperCAmelCase  (				self		):
          if not self.is_available():
               raise RuntimeError(
                   f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)




     @classmethod
     def   __UpperCAmelCase  (				cls		):
          return f"`pip install {cls.pip_package or cls.name}`"







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'optuna'




     @staticmethod
     def   __UpperCAmelCase  (				):
          return is_optuna_available()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          return run_hp_search_optuna(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return default_hp_space_optuna(_lowerCAmelCase		)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'ray'
     __lowerCamelCase      				=  '\'ray[tune]\''




     @staticmethod
     def   __UpperCAmelCase  (				):
          return is_ray_available()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          return run_hp_search_ray(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return default_hp_space_ray(_lowerCAmelCase		)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'sigopt'




     @staticmethod
     def   __UpperCAmelCase  (				):
          return is_sigopt_available()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          return run_hp_search_sigopt(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return default_hp_space_sigopt(_lowerCAmelCase		)







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'wandb'




     @staticmethod
     def   __UpperCAmelCase  (				):
          return is_wandb_available()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		):
          return run_hp_search_wandb(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return default_hp_space_wandb(_lowerCAmelCase		)



SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}





def  _lowerCamelCase						(						) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Tuple				      =			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowerCamelCase       ) > 0:
          UpperCAmelCase__      :  str				      =			available_backends[0].name
          if len(__lowerCamelCase       ) > 1:
               logger.info(
                   F"{len(__lowerCamelCase       )} hyperparameter search backends available. Using {name} as the default."       )
          return name
     raise RuntimeError(
         """No hyperparameter search backend available.\n"""
         + """\n""".join(
             F" - To install {backend.name} run {backend.pip_install()}"
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )







'''simple docstring'''


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	# Initialise PyTorch model
	__UpperCAmelCase      					=      BertConfig.from_json_file(UpperCamelCase__							)
	print(f"""Building PyTorch model from configuration: {config}"""							)
	__UpperCAmelCase      					=      BertForPreTraining(UpperCamelCase__							)

	# Load weights from tf checkpoint
	load_tf_weights_in_bert(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
	torch.save(model.state_dict()     ,       UpperCamelCase__							)


if __name__ == "__main__":
					__lowerCAmelCase						:							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(
					    "--bert_config_file",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained BERT 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."
					)
					__lowerCAmelCase						:							Optional[Any]    							=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
