


'''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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[str]				,       A       :      Any					):




      '''simple docstring'''






      if gpta_config_file == "":
            UpperCAmelCase					    =     GPTaConfig()
      else:
            UpperCAmelCase					    =     GPTaConfig.from_json_file(A					)
      UpperCAmelCase					    =     GPTaModel(A					)

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

      # Save pytorch-model
      UpperCAmelCase					    =     pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      UpperCAmelCase					    =     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__":
 _lowercase      :      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."""
     ),
 )
 _lowercase      :      Dict            =					parser.parse_args()
 convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def __call__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





				def 					a__(		self      :			Any				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





