
from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase_  :	Union[str, Any]						=					logging.get_logger(__name__)  # pylint: disable=invalid-name







def     A__   (  lowerCamelCase  )					->		Optional[Any]:



				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(A__  ):
												return ext

				raise Exception(
				    F'''Unable to determine file format from file extension {path}. '''
				    F'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''  )







def     A__   (  lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							Tuple		=  pipeline(
				    task=args.task       ,      model=args.model if args.model else None       ,      config=args.config       ,      tokenizer=args.tokenizer       ,      device=args.device       ,      )
				UpperCamelCase_:							str		=  try_infer_format_from_ext(args.input  ) if args.format == """infer""" else args.format
				UpperCamelCase_:							Tuple		=  PipelineDataFormat.from_str(
				    format=A__       ,      output_path=args.output       ,      input_path=args.input       ,      column=args.column if args.column else nlp.default_input_names       ,      overwrite=args.overwrite       ,      )
				return RunCommand(A__       ,      A__  )





class     _UpperCamelCase			(   lowerCamelCase__					):






			'''simple docstring'''

			def __init__(    self	:    Optional[Any]  ,		snake_case_	:    Pipeline  ,		snake_case_	:    PipelineDataFormat ):
							UpperCamelCase_:							int		=  nlp
							UpperCamelCase_:							Union[str, Any]		=  reader

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    ArgumentParser ):
							UpperCamelCase_:							Any		=  parser.add_parser("""run"""  ,		help="""Run a pipeline through the CLI""" )
							run_parser.add_argument("""--task"""  ,		choices=get_supported_tasks()  ,		help="""Task to run""" )
							run_parser.add_argument("""--input"""  ,		type=lowercase__  ,		help="""Path to the file to use for inference""" )
							run_parser.add_argument("""--output"""  ,		type=lowercase__  ,		help="""Path to the file that will be used post to write results.""" )
							run_parser.add_argument("""--model"""  ,		type=lowercase__  ,		help="""Name or path to the model to instantiate.""" )
							run_parser.add_argument("""--config"""  ,		type=lowercase__  ,		help="""Name or path to the model\'s config to instantiate.""" )
							run_parser.add_argument(
							    """--tokenizer"""  ,		type=lowercase__  ,		help="""Name of the tokenizer to use. (default: same as the model name)""" )
							run_parser.add_argument(
							    """--column"""  ,		type=lowercase__  ,		help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""  ,		)
							run_parser.add_argument(
							    """--format"""  ,		type=lowercase__  ,		default="""infer"""  ,		choices=PipelineDataFormat.SUPPORTED_FORMATS  ,		help="""Input format to read from"""  ,		)
							run_parser.add_argument(
							    """--device"""  ,		type=lowercase__  ,		default=-1  ,		help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""  ,		)
							run_parser.add_argument("""--overwrite"""  ,		action="""store_true"""  ,		help="""Allow overwriting the output file.""" )
							run_parser.set_defaults(func=lowercase__ )




			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self._nlp, []

							for entry in self._reader:
											UpperCamelCase_:							Union[str, Any]		=  nlp(**lowercase__ ) if self._reader.is_multi_columns else nlp(lowercase__ )
											if isinstance(lowercase__  ,		lowercase__ ):
															outputs.append(lowercase__ )
											else:
															outputs += output

															   # Saving data
							if self._nlp.binary_output:
											UpperCamelCase_:							Dict		=  self._reader.save_binary(lowercase__ )
											logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}''' )
							else:
											self._reader.save(lowercase__ )








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__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

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

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


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					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."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






