



import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase    	=  logging.get_logger()


@dataclass
class 						lowerCamelCase__			:
						_lowerCAmelCase             =			42
						_lowerCAmelCase             =			field(default_factory=_lowerCamelCase )
						_lowerCAmelCase             =			field(default_factory=_lowerCamelCase )
						_lowerCAmelCase             =			field(default_factory=_lowerCamelCase )


						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Union[str, Any]   ,							_a    :	Tensor   ,							_a    :	Tensor   ,							_a    :	str      ):
													a__:							Optional[Any]									=len(list(m.modules()      )      ) == 1 or isinstance(_UpperCamelCase   ,							nn.Convad      ) or isinstance(_UpperCamelCase   ,							nn.BatchNormad      )
													if has_not_submodules:
																				self.traced.append(_UpperCamelCase      )
																				a__:							List[Any]									=m


						def __call__(			self    :	Dict   ,							_a    :	Tensor      ):
													for name, m in self.module.named_modules():
																				self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,							name=_UpperCamelCase      )      )      )
													self.module(_UpperCamelCase      )
													[x.remove() for x in self.handles]
													return self







						@property
						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													# check the len of the state_dict keys to see if we have learnable params
													return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()      )      ) > 0}


class 						lowerCamelCase__			(		nn.Module ):


						def __init__(			self    :	Tuple   ,							_a    :	nn.Module      ):
													super().__init__()

													a__:							List[str]									=[]
													# - get the stem
													feature_blocks.append(("conv1", model.stem)      )
													# - get all the feature blocks
													for k, v in model.trunk_output.named_children():
																				assert k.startswith("block"      ), F"Unexpected layer name {k}"
																				a__:							str									=len(_UpperCamelCase      ) + 1
																				feature_blocks.append((F"res{block_index}", v)      )

													a__:							List[Any]									=nn.ModuleDict(_UpperCamelCase      )







						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	Tensor      ):
													return get_trunk_forward_outputs(
													    _UpperCamelCase   ,							out_feat_keys=_UpperCamelCase   ,							feature_blocks=self._feature_blocks   ,							)


class 						lowerCamelCase__			(		_lowerCamelCase ):


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]





def     __lowerCamelCase							(   __magic_name__						:	str    ):
							a__:							Dict									=RegNetConfig(depths=[2, 7, 17, 1]  ,			hidden_sizes=[8, 8, 8, 8]  ,			groups_width=8    )
							if "in1k" in model_name:
														a__:							List[Any]									=RegNetForImageClassification(__magic_name__    )
							else:
														a__:							Union[str, Any]									=RegNetModel(__magic_name__    )
							# create from model (with small weights)
							a__:							List[Any]									=FakeRegNetVisslWrapper(
							    RegNet(FakeRegNetParams(depth=27  ,			group_width=1_010  ,			w_a=1_744  ,			w_a=620.83  ,			w_m=2.52    )    )    )

							with torch.no_grad():
														a__:							Optional[int]									=from_model.eval()
														a__:							List[Any]									=our_model.eval()

														a__:							Optional[Any]									=torch.randn((1, 3, 32, 32)    )
														# trace both
														a__:							List[str]									=Tracker(__magic_name__    )
														a__:							int									=dest_tracker(__magic_name__    ).parametrized

														pprint(dest_tracker.nameamodule    )
														a__:							Tuple									=Tracker(__magic_name__    )
														a__:							int									=src_tracker(__magic_name__    ).parametrized

							# convert the keys -> module dict to keys -> params
							def to_params_dict(__magic_name__						:	Optional[Any]    ):
														a__:							Dict									=OrderedDict()
														for name, module in dict_with_modules.items():
																					for param_name, param in module.state_dict().items():
																												a__:							Optional[Any]									=param
														return params_dict

							a__:							Union[str, Any]									={}

							a__:							Any									=to_params_dict(__magic_name__    )
							a__:							Optional[Any]									=to_params_dict(__magic_name__    )

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,			dst_state_dict.items()    ):
														a__:							List[str]									=dest_key
														logger.info(F"{src_key} -> {dest_key}"    )
							# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
							if "in1k" in model_name:
														a__:							str									="classifier.1.weight"
														a__:							Any									="classifier.1.bias"

							return from_to_ours_keys





def     __lowerCamelCase							(   __magic_name__						:	Path  ,			__magic_name__						:	str = None  ,			__magic_name__						:	bool = True    ):
							a__:							List[Any]									="imagenet-1k-id2label.json"
							a__:							Dict									=1_000

							a__:							str									="huggingface/label-files"
							a__:							Any									=num_labels
							a__:							Tuple									=json.load(open(cached_download(hf_hub_url(__magic_name__  ,			__magic_name__  ,			repo_type="dataset"    )    )  ,			"r"    )    )
							a__:							Optional[Any]									={int(__magic_name__    ): v for k, v in idalabel.items()}

							a__:							str									=idalabel
							a__:							List[str]									={v: k for k, v in idalabel.items()}

							a__:							Tuple									=partial(__magic_name__  ,			num_labels=__magic_name__  ,			idalabel=__magic_name__  ,			labelaid=__magic_name__    )

							a__:							List[Any]									={
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010    ),
							    # finetuned on imagenet
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]  ,			hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,			groups_width=1_010    ),
							}

							# add seer weights logic
							def load_using_classy_vision(__magic_name__						:	str    ) -> Tuple[Dict, Dict]:
														a__:							str									=torch.hub.load_state_dict_from_url(__magic_name__  ,			model_dir=str(__magic_name__    )  ,			map_location="cpu"    )
														# check if we have a head, if yes add it
														a__:							Tuple									=files["classy_state_dict"]["base_model"]["model"]
														return model_state_dict["trunk"], model_state_dict["heads"]

							a__:							Optional[Any]									={
							    "regnet-y-10b-seer": partial(
							        __magic_name__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"  ,			),
							    "regnet-y-10b-seer-in1k": partial(
							        __magic_name__  ,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"  ,			),
							}

							a__:							Any									=get_from_to_our_keys(__magic_name__    )

							if not (save_directory / F"{model_name}.pth").exists():
														logger.info("Loading original state_dict."    )
														a__				,      a__:							str									=names_to_from_model[model_name]()
														a__:							Any									=from_state_dict_trunk
														if "in1k" in model_name:
																					# add the head
																					a__:							int									={**from_state_dict_trunk, **from_state_dict_head}
														logger.info("Done!"    )

														a__:							List[Any]									={}

														a__:							Optional[Any]									=list(from_state_dict.keys()    )
														a__:							List[Any]									=R"\.block.-part."
														# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
														for key in from_state_dict.keys():
																					# remove the weird "block[0,1]-part" from the key
																					a__:							List[str]									=re.sub(__magic_name__  ,			""  ,			__magic_name__    )
																					# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																					a__:							int									=from_to_ours_keys[src_key]
																					# store the parameter with our key
																					a__:							Optional[int]									=from_state_dict[key]
																					not_used_keys.remove(__magic_name__    )
														# check that all keys have been updated
														assert len(__magic_name__    ) == 0, F"Some keys where not used {','.join(__magic_name__    )}"

														logger.info(F"The following keys were not used: {','.join(__magic_name__    )}"    )

														# save our state dict to disk
														torch.save(__magic_name__  ,			save_directory / F"{model_name}.pth"    )

														del converted_state_dict
							else:
														logger.info("The state_dict was already stored on disk."    )
							if push_to_hub:
														logger.info(F"Token is {os.environ['HF_TOKEN']}"    )
														logger.info("Loading our model."    )
														# create our model
														a__:							int									=names_to_config[model_name]
														a__:							Optional[int]									=RegNetModel
														if "in1k" in model_name:
																					a__:							Any									=RegNetForImageClassification
														a__:							int									=our_model_func(__magic_name__    )
														# place our model to the meta device (so remove all the weights)
														our_model.to(torch.device("meta"    )    )
														logger.info("Loading state_dict in our model."    )
														# load state dict
														a__:							List[str]									=our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    __magic_name__  ,			__magic_name__  ,			[save_directory / F"{model_name}.pth"]    )
														logger.info("Finally, pushing!"    )
														# push it to hub
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / model_name  ,			commit_message="Add model"  ,			output_dir=save_directory / model_name  ,			)
														a__:							int									=384
														# we can use the convnext one
														a__:							str									=AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  ,			size=__magic_name__    )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / model_name  ,			commit_message="Add image processor"  ,			output_dir=save_directory / model_name  ,			)


if __name__ == "__main__":
		__UpperCAmelCase    	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default=None,
		    type=str,
		    help=(
		        '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
		        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=Path,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    default=True,
		    type=bool,
		    required=False,
		    help='''If True, push model and image processor to the hub.''',
		)

		__UpperCAmelCase    	=  parser.parse_args()

		__UpperCAmelCase    	=  args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












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


snake_case__   :				Tuple						=	logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def _a							(      lowerCamelCase:  int					)      ->		List[str]:







      '''simple docstring'''






      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 _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Tuple   ,							lowerCamelCase:  Any					)      ->		str:







      '''simple docstring'''






      return max(metric_fn(lowerCamelCase   ,							lowerCamelCase					) for gt in ground_truths					)



def _a							(      lowerCamelCase:  Any   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  List[str]					)      ->		Any:







      '''simple docstring'''






      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]
      __A			  =							[]

      if args.gold_data_mode == "qa":
            __A			  =							pd.read_csv(lowerCamelCase   ,							sep='''\t'''   ,							header=lowerCamelCase					)
            for answer_list in data[1]:
                  __A			  =							ast.literal_eval(lowerCamelCase					)
                  answers.append(lowerCamelCase					)
      else:
            __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]
            __A			  =							[[reference] for reference in references]

      __A			  =							__A			  =							__A			  =							0
      for prediction, ground_truths in zip(lowerCamelCase   ,							lowerCamelCase					):
            total += 1
            em += metric_max_over_ground_truths(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)
            fa += metric_max_over_ground_truths(lowerCamelCase   ,							lowerCamelCase   ,							lowerCamelCase					)

      __A			  =							100.0 * em / total
      __A			  =							100.0 * fa / total

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



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  int   ,							lowerCamelCase:  Tuple					)      ->		List[str]:







      '''simple docstring'''






      __A			  =							args.k
      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]
      __A			  =							[line.strip() for line in open(lowerCamelCase   ,							'''r'''					).readlines()]

      __A			  =							__A			  =							0
      for hypo, reference in zip(lowerCamelCase   ,							lowerCamelCase					):
            __A			  =							set(hypo.split('''\t'''					)[:k]					)
            __A			  =							set(reference.split('''\t'''					)					)
            total += 1
            em += len(hypo_provenance & ref_provenance					) / k

      __A			  =							100.0 * em / total
      logger.info(F"""Precision@{k}: {em: .2f}"""					)



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  List[Any]   ,							lowerCamelCase:  int					)      ->		int:







      '''simple docstring'''






      def strip_title(lowerCamelCase:  int					):
            if title.startswith('''"'''					):
                  __A			  =							title[1:]
            if title.endswith('''"'''					):
                  __A			  =							title[:-1]
            return title

      __A			  =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
          lowerCamelCase   ,							return_tensors='''pt'''   ,							padding=lowerCamelCase   ,							truncation=lowerCamelCase   ,							)['''input_ids'''].to(args.device					)

      __A			  =							rag_model.rag.question_encoder(lowerCamelCase					)
      __A			  =							question_enc_outputs[0]

      __A			  =							rag_model.retriever(
          lowerCamelCase   ,							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'''   ,							)
      __A			  =							rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
      __A			  =							[]
      for docs in all_docs:
            __A			  =							[strip_title(lowerCamelCase					) for title in docs['''title''']]
            provenance_strings.append('''\t'''.join(lowerCamelCase					)					)
      return provenance_strings



def _a							(      lowerCamelCase:  Tuple   ,							lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Union[str, Any]					)      ->		Dict:







      '''simple docstring'''






      with torch.no_grad():
            __A			  =							rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                lowerCamelCase   ,							return_tensors='''pt'''   ,							padding=lowerCamelCase   ,							truncation=lowerCamelCase					)

            __A			  =							inputs_dict.input_ids.to(args.device					)
            __A			  =							inputs_dict.attention_mask.to(args.device					)
            __A			  =							rag_model.generate(  # rag_model overwrites generate
                lowerCamelCase   ,							attention_mask=lowerCamelCase   ,							num_beams=args.num_beams   ,							min_length=args.min_length   ,							max_length=args.max_length   ,							early_stopping=lowerCamelCase   ,							num_return_sequences=1   ,							bad_words_ids=[[0, 0]]   ,							)
            __A			  =							rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase   ,							skip_special_tokens=lowerCamelCase					)

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

            return answers



def _a							(      )      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							argparse.ArgumentParser()
      parser.add_argument(
          '''--model_type'''   ,							choices=['''rag_sequence''', '''rag_token''', '''bart''']   ,							type=lowerCamelCase   ,							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=lowerCamelCase   ,							choices=['''exact''', '''compressed''', '''legacy''']   ,							type=lowerCamelCase   ,							help='''RAG model retriever type'''   ,							)
      parser.add_argument(
          '''--index_path'''   ,							default=lowerCamelCase   ,							type=lowerCamelCase   ,							help='''Path to the retrieval index'''   ,							)
      parser.add_argument('''--n_docs'''   ,							default=5   ,							type=lowerCamelCase   ,							help='''Number of retrieved docs'''					)
      parser.add_argument(
          '''--model_name_or_path'''   ,							default=lowerCamelCase   ,							type=lowerCamelCase   ,							required=lowerCamelCase   ,							help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''   ,							)
      parser.add_argument(
          '''--eval_mode'''   ,							choices=['''e2e''', '''retrieval''']   ,							default='''e2e'''   ,							type=lowerCamelCase   ,							help=(
              '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
              ''' precision@k.'''
          )   ,							)
      parser.add_argument('''--k'''   ,							default=1   ,							type=lowerCamelCase   ,							help='''k for the precision@k calculation'''					)
      parser.add_argument(
          '''--evaluation_set'''   ,							default=lowerCamelCase   ,							type=lowerCamelCase   ,							required=lowerCamelCase   ,							help='''Path to a file containing evaluation samples'''   ,							)
      parser.add_argument(
          '''--gold_data_path'''   ,							default=lowerCamelCase   ,							type=lowerCamelCase   ,							required=lowerCamelCase   ,							help='''Path to a tab-separated file with gold samples'''   ,							)
      parser.add_argument(
          '''--gold_data_mode'''   ,							default='''qa'''   ,							type=lowerCamelCase   ,							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=lowerCamelCase   ,							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=lowerCamelCase   ,							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=lowerCamelCase   ,							help='''Number of beams to be used when generating answers'''   ,							)
      parser.add_argument('''--min_length'''   ,							default=1   ,							type=lowerCamelCase   ,							help='''Min length of the generated answers'''					)
      parser.add_argument('''--max_length'''   ,							default=50   ,							type=lowerCamelCase   ,							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.'''   ,							)
      __A			  =							parser.parse_args()
      __A			  =							torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''					)
      return args



def _a							(      lowerCamelCase:  int					)      ->		Dict:







      '''simple docstring'''






      __A			  =							{}
      if args.model_type is None:
            __A			  =							infer_model_type(args.model_name_or_path					)
            assert args.model_type is not None
      if args.model_type.startswith('''rag'''					):
            __A			  =							RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
            __A			  =							args.n_docs
            if args.index_name is not None:
                  __A			  =							args.index_name
            if args.index_path is not None:
                  __A			  =							args.index_path
      else:
            __A			  =							BartForConditionalGeneration

      __A			  =							(
          [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'''   ,							lowerCamelCase					)

      __A			  =							get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
      __A			  =							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(lowerCamelCase   ,							args.predictions_path   ,							args.gold_data_path					)
                  continue

            logger.info('''***** Running evaluation for {} *****'''.format(lowerCamelCase					)					)
            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'''					):
                  __A			  =							RagRetriever.from_pretrained(lowerCamelCase   ,							**lowerCamelCase					)
                  __A			  =							model_class.from_pretrained(lowerCamelCase   ,							retriever=lowerCamelCase   ,							**lowerCamelCase					)
                  model.retriever.init_retrieval()
            else:
                  __A			  =							model_class.from_pretrained(lowerCamelCase   ,							**lowerCamelCase					)
            model.to(args.device					)

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

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


if __name__ == "__main__":
       snake_case__   :				List[str]						=	get_args()
       main(args)





