"""simple docstring"""





from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
   import timm


if is_torch_available():
   from torch import Tensor




class  _snake_case		(    __snake_case   ,       __snake_case ):







    """simple docstring"""






    a			=						"pixel_values"
    a			=						False
    a			=						TimmBackboneConfig





    def __init__(      self							:							Optional[Any]      ,    _A							:							Dict      ,    **_A							:							Any):



           """simple docstring"""







           requires_backends(self      ,    """timm""")
           super().__init__(UpperCAmelCase__)
           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    config

           if config.backbone is None:
                  raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""")

           if config.backbone not in timm.list_models():
                  raise ValueError(f"""backbone {config.backbone} is not supported by timm.""")

           if hasattr(UpperCAmelCase__      ,    """out_features""") and config.out_features is not None:
                  raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""")

           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    getattr(UpperCAmelCase__      ,    """use_pretrained_backbone"""      ,    UpperCAmelCase__)
           if pretrained is None:
                  raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""")

           # We just take the final layer by default. This matches the default for the transformers models.
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    config.out_indices if getattr(UpperCAmelCase__      ,    """out_indices"""      ,    UpperCAmelCase__) is not None else (-1,)

           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    timm.create_model(
               config.backbone      ,    pretrained=UpperCAmelCase__      ,    features_only=config.features_only      ,    in_chans=config.num_channels      ,    out_indices=UpperCAmelCase__      ,    **UpperCAmelCase__      ,    )
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           _SCREAMING_SNAKE_CASE		:						str			  =    self._backbone.return_layers
           _SCREAMING_SNAKE_CASE		:						str			  =    {layer["""module"""]: str(UpperCAmelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
           super()._init_backbone(UpperCAmelCase__)





    @classmethod
    def 		_lowerCAmelCase		(      cls							:							List[str]      ,    _A							:							Optional[int]      ,    *_A							:							Union[str, Any]      ,    **_A							:							List[Any]):



           """simple docstring"""







           requires_backends(cls      ,    ["""vision""", """timm"""])
           from ...models.timm_backbone import TimmBackboneConfig

           _SCREAMING_SNAKE_CASE		:						Dict			  =    kwargs.pop("""config"""      ,    TimmBackboneConfig())

           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    kwargs.pop("""use_timm_backbone"""      ,    UpperCAmelCase__)
           if not use_timm:
                  raise ValueError("""use_timm_backbone must be True for timm backbones""")

           _SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs.pop("""num_channels"""      ,    config.num_channels)
           _SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs.pop("""features_only"""      ,    config.features_only)
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    kwargs.pop("""use_pretrained_backbone"""      ,    config.use_pretrained_backbone)
           _SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs.pop("""out_indices"""      ,    config.out_indices)
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TimmBackboneConfig(
               backbone=UpperCAmelCase__      ,    num_channels=UpperCAmelCase__      ,    features_only=UpperCAmelCase__      ,    use_pretrained_backbone=UpperCAmelCase__      ,    out_indices=UpperCAmelCase__      ,    )
           return super()._from_config(UpperCAmelCase__      ,    **UpperCAmelCase__)





    def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



           """simple docstring"""







           pass





    def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							str      ,    _A							:							int=None      ,    _A							:							Union[str, Any]=None      ,    _A							:							Any=None      ,    **_A							:							List[str]):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Dict			  =    return_dict if return_dict is not None else self.config.use_return_dict
           _SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _SCREAMING_SNAKE_CASE		:						List[Any]			  =    output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                  raise ValueError("""Cannot output attentions for timm backbones at the moment""")

           if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self._all_layers
                  _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self._backbone(UpperCAmelCase__      ,    **UpperCAmelCase__)
                  _SCREAMING_SNAKE_CASE		:						List[str]			  =    self._return_layers
                  _SCREAMING_SNAKE_CASE		:						List[str]			  =    tuple(hidden_states[i] for i in self.out_indices)
           else:
                  _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self._backbone(UpperCAmelCase__      ,    **UpperCAmelCase__)
                  _SCREAMING_SNAKE_CASE		:						List[str]			  =    None

           _SCREAMING_SNAKE_CASE		:						Any			  =    tuple(UpperCAmelCase__)
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tuple(UpperCAmelCase__) if hidden_states is not None else None

           if not return_dict:
                  _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (feature_maps,)
                  if output_hidden_states:
                         _SCREAMING_SNAKE_CASE		:						Tuple			  =    output + (hidden_states,)
                  return output

           return BackboneOutput(feature_maps=UpperCAmelCase__      ,    hidden_states=UpperCAmelCase__      ,    attentions=UpperCAmelCase__)



"""simple docstring"""





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCAmelCase_     =	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							if "token" in model_name_or_path:
														return "rag_token"
							if "sequence" in model_name_or_path:
														return "rag_sequence"
							if "bart" in model_name_or_path:
														return "bart"
							return None



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							return max(metric_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for gt in ground_truths  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

							if args.gold_data_mode == "qa":
														_SCREAMING_SNAKE_CASE		:						int			  =    pd.read_csv(__SCREAMING_SNAKE_CASE     ,	sep="""\t"""     ,	header=__SCREAMING_SNAKE_CASE  )
														for answer_list in data[1]:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ast.literal_eval(__SCREAMING_SNAKE_CASE  )
																					answers.append(__SCREAMING_SNAKE_CASE  )
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [[reference] for reference in references]

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0
							for prediction, ground_truths in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														total += 1
														em += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														fa += metric_max_over_ground_truths(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    1_00.0 * em / total
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_00.0 * fa / total

							logger.info(F"""F1: {fa:.2f}"""  )
							logger.info(F"""EM: {em:.2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    args.k
							_SCREAMING_SNAKE_CASE		:						int			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Any			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0
							for hypo, reference in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    set(hypo.split("""\t"""  )[:k]  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    set(reference.split("""\t"""  )  )
														total += 1
														em += len(hypo_provenance & ref_provenance  ) / k

							_SCREAMING_SNAKE_CASE		:						int			  =    1_00.0 * em / total
							logger.info(F"""Precision@{k}: {em: .2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							def strip_title(__SCREAMING_SNAKE_CASE  ):
														if title.startswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    title[1:]
														if title.endswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    title[:-1]
														return title

							_SCREAMING_SNAKE_CASE		:						Dict			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)["""input_ids"""].to(args.device  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    question_enc_outputs[0]

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rag_model.retriever(
							    __SCREAMING_SNAKE_CASE     ,	question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()     ,	prefix=rag_model.rag.generator.config.prefix     ,	n_docs=rag_model.config.n_docs     ,	return_tensors="""pt"""     ,	)
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for docs in all_docs:
														_SCREAMING_SNAKE_CASE		:						str			  =    [strip_title(__SCREAMING_SNAKE_CASE  ) for title in docs["""title"""]]
														provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE  )  )
							return provenance_strings



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.input_ids.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.attention_mask.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.generate(  # rag_model overwrites generate
														    __SCREAMING_SNAKE_CASE     ,	attention_mask=__SCREAMING_SNAKE_CASE     ,	num_beams=args.num_beams     ,	min_length=args.min_length     ,	max_length=args.max_length     ,	early_stopping=__SCREAMING_SNAKE_CASE     ,	num_return_sequences=1     ,	bad_words_ids=[[0, 0]]     ,	)
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    rag_model.retriever.generator_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,	skip_special_tokens=__SCREAMING_SNAKE_CASE  )

														if args.print_predictions:
																					for q, a in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																												logger.info("""Q: {} - A: {}""".format(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

														return answers



def      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    argparse.ArgumentParser()
							parser.add_argument(
							    """--model_type"""     ,	choices=["""rag_sequence""", """rag_token""", """bart"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
							        """ model_name_or_path"""
							    )     ,	)
							parser.add_argument(
							    """--index_name"""     ,	default=__SCREAMING_SNAKE_CASE     ,	choices=["""exact""", """compressed""", """legacy"""]     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""RAG model retriever type"""     ,	)
							parser.add_argument(
							    """--index_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Path to the retrieval index"""     ,	)
							parser.add_argument("""--n_docs"""     ,	default=5     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Number of retrieved docs"""  )
							parser.add_argument(
							    """--model_name_or_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""     ,	)
							parser.add_argument(
							    """--eval_mode"""     ,	choices=["""e2e""", """retrieval"""]     ,	default="""e2e"""     ,	type=__SCREAMING_SNAKE_CASE     ,	help=(
							        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
							        """ precision@k."""
							    )     ,	)
							parser.add_argument("""--k"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""k for the precision@k calculation"""  )
							parser.add_argument(
							    """--evaluation_set"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a file containing evaluation samples"""     ,	)
							parser.add_argument(
							    """--gold_data_path"""     ,	default=__SCREAMING_SNAKE_CASE     ,	type=__SCREAMING_SNAKE_CASE     ,	required=__SCREAMING_SNAKE_CASE     ,	help="""Path to a tab-separated file with gold samples"""     ,	)
							parser.add_argument(
							    """--gold_data_mode"""     ,	default="""qa"""     ,	type=__SCREAMING_SNAKE_CASE     ,	choices=["""qa""", """ans"""]     ,	help=(
							        """Format of the gold data file"""
							        """qa - a single line in the following format: question [tab] answer_list"""
							        """ans - a single line of the gold file contains the expected answer string"""
							    )     ,	)
							parser.add_argument(
							    """--predictions_path"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default="""predictions.txt"""     ,	help="""Name of the predictions file, to be stored in the checkpoints directory"""     ,	)
							parser.add_argument(
							    """--eval_all_checkpoints"""     ,	action="""store_true"""     ,	help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""     ,	)
							parser.add_argument(
							    """--eval_batch_size"""     ,	default=8     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Batch size per GPU/CPU for evaluation."""     ,	)
							parser.add_argument(
							    """--recalculate"""     ,	help="""Recalculate predictions even if the prediction file exists"""     ,	action="""store_true"""     ,	)
							parser.add_argument(
							    """--num_beams"""     ,	default=4     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Number of beams to be used when generating answers"""     ,	)
							parser.add_argument("""--min_length"""     ,	default=1     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Min length of the generated answers"""  )
							parser.add_argument("""--max_length"""     ,	default=50     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""Max length of the generated answers"""  )

							parser.add_argument(
							    """--print_predictions"""     ,	action="""store_true"""     ,	help="""If True, prints predictions while evaluating."""     ,	)
							parser.add_argument(
							    """--print_docs"""     ,	action="""store_true"""     ,	help="""If True, prints docs retried while generating."""     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
							return args



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							if args.model_type is None:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    infer_model_type(args.model_name_or_path  )
														assert args.model_type is not None
							if args.model_type.startswith("""rag"""  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.n_docs
														if args.index_name is not None:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.index_name
														if args.index_path is not None:
																					_SCREAMING_SNAKE_CASE		:						Any			  =    args.index_path
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    BartForConditionalGeneration

							_SCREAMING_SNAKE_CASE		:						int			  =    (
							    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
							    if args.eval_all_checkpoints
							    else [args.model_name_or_path]
							)

							logger.info("""Evaluate the following checkpoints: %s"""     ,	__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_scores if args.eval_mode == """e2e""" else get_precision_at_k
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

							for checkpoint in checkpoints:
														if os.path.exists(args.predictions_path  ) and (not args.recalculate):
																					logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path  )  )
																					score_fn(__SCREAMING_SNAKE_CASE     ,	args.predictions_path     ,	args.gold_data_path  )
																					continue

														logger.info("""***** Running evaluation for {} *****""".format(__SCREAMING_SNAKE_CASE  )  )
														logger.info("""  Batch size = %d"""     ,	args.eval_batch_size  )
														logger.info("""  Predictions will be stored under {}""".format(args.predictions_path  )  )

														if args.model_type.startswith("""rag"""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	retriever=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
																					model.retriever.init_retrieval()
														else:
																					_SCREAMING_SNAKE_CASE		:						str			  =    model_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE  )
														model.to(args.device  )

														with open(args.evaluation_set     ,	"""r"""  ) as eval_file, open(args.predictions_path     ,	"""w"""  ) as preds_file:
																					_SCREAMING_SNAKE_CASE		:						str			  =    []
																					for line in tqdm(__SCREAMING_SNAKE_CASE  ):
																												questions.append(line.strip()  )
																												if len(__SCREAMING_SNAKE_CASE  ) == args.eval_batch_size:
																																			_SCREAMING_SNAKE_CASE		:						str			  =    evaluate_batch_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																																			preds_file.write("""\n""".join(__SCREAMING_SNAKE_CASE  ) + """\n"""  )
																																			preds_file.flush()
																																			_SCREAMING_SNAKE_CASE		:						Any			  =    []
																					if len(__SCREAMING_SNAKE_CASE  ) > 0:
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate_batch_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
																												preds_file.write("""\n""".join(__SCREAMING_SNAKE_CASE  )  )
																												preds_file.flush()

																					score_fn(__SCREAMING_SNAKE_CASE     ,	args.predictions_path     ,	args.gold_data_path  )


if __name__ == "__main__":
			lowerCAmelCase_     =	get_args()
			main(args)


