




'''simple docstring'''


import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case      :      Optional[Any]   							=		logging.get_logger(__name__)

_snake_case      :      List[Any]   							=		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_snake_case      :      Optional[int]   							=		{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

_snake_case      :      Tuple   							=		{
    'microsoft/biogpt': 1024,
}
def 		snake_case_    (UpperCamelCase       :  str      ):





							'''simple docstring'''


							_a	     =				set()
							_a	     =				word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)      )
														_a	     =				char
							return pairs

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']







							def __init__(  self       :  List[str]							,			lowerCAmelCase_       :  Dict							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  int="<unk>"							,			lowerCAmelCase_       :  int="<s>"							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Dict="</s>"							,			lowerCAmelCase_       :  int="<pad>"							,			**lowerCAmelCase_       :  Any							,			)			->							List[Any]:


														"""simple docstring"""


														super().__init__(
														    bos_token=lowerCAmelCase_							,			eos_token=lowerCAmelCase_							,			sep_token=lowerCAmelCase_							,			unk_token=lowerCAmelCase_							,			pad_token=lowerCAmelCase_							,			**lowerCAmelCase_							,			)

														try:
																					import sacremoses
														except ImportError:
																					raise ImportError(
																					    '''You need to install sacremoses to use BioGptTokenizer. '''
																					    '''See https://pypi.org/project/sacremoses/ for installation.'''					)

														_a	     =				'''en'''
														_a	     =				sacremoses
														# cache of sm.MosesTokenizer instance
														_a	     =				{}
														_a	     =				{}
														with open(lowerCAmelCase_							,			encoding='''utf-8'''					) as vocab_handle:
																					_a	     =				json.load(lowerCAmelCase_					)
														_a	     =				{v: k for k, v in self.encoder.items()}
														with open(lowerCAmelCase_							,			encoding='''utf-8'''					) as merges_handle:
																					_a	     =				merges_handle.read().split('''\n'''					)[:-1]
														_a	     =				[tuple(merge.split()[:2]					) for merge in merges]
														_a	     =				dict(zip(lowerCAmelCase_							,			range(len(lowerCAmelCase_					)					)					)					)
														_a	     =				{}







							@property
							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							Optional[int]:


														"""simple docstring"""


														return len(self.encoder					)







							def    __lowerCAmelCase			(  self       :  Tuple					)			->							int:


														"""simple docstring"""


														return dict(self.encoder							,			**self.added_tokens_encoder					)







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Dict					)			->							str:


														"""simple docstring"""


														if lang not in self.cache_moses_tokenizer:
																					_a	     =				self.sm.MosesTokenizer(lang=lowerCAmelCase_					)
																					_a	     =				moses_tokenizer
														return self.cache_moses_tokenizer[lang].tokenize(
														    lowerCAmelCase_							,			aggressive_dash_splits=lowerCAmelCase_							,			return_str=lowerCAmelCase_							,			escape=lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  Optional[int]							,			lowerCAmelCase_       :  Optional[Any]					)			->							List[Any]:


														"""simple docstring"""


														if lang not in self.cache_moses_detokenizer:
																					_a	     =				self.sm.MosesDetokenizer(lang=lowerCAmelCase_					)
																					_a	     =				moses_detokenizer
														return self.cache_moses_detokenizer[lang].detokenize(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  Optional[int]					)			->							Tuple:


														"""simple docstring"""


														_a	     =				tuple(token[:-1]					) + (token[-1] + '''</w>''',)
														if token in self.cache:
																					return self.cache[token]
														_a	     =				get_pairs(lowerCAmelCase_					)

														if not pairs:
																					return token + "</w>"

														while True:
																					_a	     =				min(lowerCAmelCase_							,			key=lambda lowerCAmelCase_					: self.bpe_ranks.get(lowerCAmelCase_							,			float('''inf'''					)					)					)
																					if bigram not in self.bpe_ranks:
																												break
																					_a     ,							_a	     =				bigram
																					_a	     =				[]
																					_a	     =				0
																					while i < len(lowerCAmelCase_					):
																												try:
																																			_a	     =				word.index(lowerCAmelCase_							,			lowerCAmelCase_					)
																												except ValueError:
																																			new_word.extend(word[i:]					)
																																			break
																												else:
																																			new_word.extend(word[i:j]					)
																																			_a	     =				j

																												if word[i] == first and i < len(lowerCAmelCase_					) - 1 and word[i + 1] == second:
																																			new_word.append(first + second					)
																																			i += 2
																												else:
																																			new_word.append(word[i]					)
																																			i += 1
																					_a	     =				tuple(lowerCAmelCase_					)
																					_a	     =				new_word
																					if len(lowerCAmelCase_					) == 1:
																												break
																					else:
																												_a	     =				get_pairs(lowerCAmelCase_					)
														_a	     =				''' '''.join(lowerCAmelCase_					)
														if word == "\n  </w>":
																					_a	     =				'''\n</w>'''
														_a	     =				word
														return word







							def    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Union[str, Any]=False					)			->							Any:


														"""simple docstring"""


														if bypass_tokenizer:
																					_a	     =				text.split()
														else:
																					_a	     =				self.moses_tokenize(lowerCAmelCase_							,			self.lang					)

														_a	     =				[]
														for token in text:
																					if token:
																												split_tokens.extend(list(self.bpe(lowerCAmelCase_					).split(''' '''					)					)					)

														return split_tokens







							def    __lowerCAmelCase			(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  Optional[Any]					)			->							Tuple:


														"""simple docstring"""


														return self.encoder.get(lowerCAmelCase_							,			self.encoder.get(self.unk_token					)					)







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  str					)			->							int:


														"""simple docstring"""


														return self.decoder.get(lowerCAmelCase_							,			self.unk_token					)







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				[t.replace(''' '''							,			''''''					).replace('''</w>'''							,			''' '''					) for t in tokens]
														_a	     =				''''''.join(lowerCAmelCase_					).split()
														# detokenize
														_a	     =				self.moses_detokenize(lowerCAmelCase_							,			self.lang					)
														return text







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  List[int]							,			lowerCAmelCase_       :  Optional[List[int]] = None					)			->							List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_a	     =				[self.sep_token_id]
														return sep + token_ids_a + sep + token_ids_a







							def    __lowerCAmelCase			(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  List[int]							,			lowerCAmelCase_       :  Optional[List[int]] = None							,			lowerCAmelCase_       :  bool = False					)			->							List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase_							,			token_ids_a=lowerCAmelCase_							,			already_has_special_tokens=lowerCAmelCase_					)
														# no bos used in fairseq
														if token_ids_a is not None:
																					return [1] + ([0] * len(lowerCAmelCase_					)) + [1] + ([0] * len(lowerCAmelCase_					))
														return [1] + ([0] * len(lowerCAmelCase_					))







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  List[int]							,			lowerCAmelCase_       :  Optional[List[int]] = None					)			->							List[int]:


														"""simple docstring"""


														_a	     =				[self.sep_token_id]

														# no bos used in fairseq
														if token_ids_a is None:
																					return len(token_ids_a + sep					) * [0]
														return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""simple docstring"""


														if not os.path.isdir(lowerCAmelCase_					):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
																					return
														_a	     =				os.path.join(
														    lowerCAmelCase_							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
														_a	     =				os.path.join(
														    lowerCAmelCase_							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

														with open(lowerCAmelCase_							,			'''w'''							,			encoding='''utf-8'''					) as f:
																					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=lowerCAmelCase_							,			ensure_ascii=lowerCAmelCase_					) + '''\n'''					)

														_a	     =				0
														with open(lowerCAmelCase_							,			'''w'''							,			encoding='''utf-8'''					) as writer:
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda lowerCAmelCase_					: kv[1]					):
																												if index != token_index:
																																			logger.warning(
																																			    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																																			    ''' Please check that the tokenizer is not corrupted!'''					)
																																			_a	     =				token_index
																												writer.write(''' '''.join(lowerCAmelCase_					) + '''\n'''					)
																												index += 1

														return vocab_file, merge_file







							def __getstate__(  self       :  Tuple					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				self.__dict__.copy()
														_a	     =				None
														return state







							def __setstate__(  self       :  Any							,			lowerCAmelCase_       :  Dict					)			->							Dict:


														"""simple docstring"""


														_a	     =				d

														try:
																					import sacremoses
														except ImportError:
																					raise ImportError(
																					    '''You need to install sacremoses to use XLMTokenizer. '''
																					    '''See https://pypi.org/project/sacremoses/ for installation.'''					)

														_a	     =				sacremoses







'''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


_snake_case      :      Optional[Any]   							=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 		snake_case_    (UpperCamelCase       :  Optional[int]      ):





							'''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 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Optional[Any]      ):





							'''simple docstring'''


							return max(metric_fn(UpperCamelCase ,						UpperCamelCase      ) for gt in ground_truths      )
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  Optional[int] ,						UpperCamelCase       :  str      ):





							'''simple docstring'''


							_a	     =				[line.strip() for line in open(UpperCamelCase ,						'''r'''      ).readlines()]
							_a	     =				[]

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

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

							_a	     =				100.0 * em / total
							_a	     =				100.0 * fa / total

							logger.info(f'F1: {fa:.2f}'      )
							logger.info(f'EM: {em:.2f}'      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


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

							_a	     =				_a	     =				0
							for hypo, reference in zip(UpperCamelCase ,						UpperCamelCase      ):
														_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 		snake_case_    (UpperCamelCase       :  str ,						UpperCamelCase       :  Any ,						UpperCamelCase       :  Dict      ):





							'''simple docstring'''


							def strip_title(UpperCamelCase       :  Tuple      ):
														if title.startswith('''"'''      ):
																					_a	     =				title[1:]
														if title.endswith('''"'''      ):
																					_a	     =				title[:-1]
														return title

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

							_a	     =				rag_model.rag.question_encoder(UpperCamelCase      )
							_a	     =				question_enc_outputs[0]

							_a	     =				rag_model.retriever(
							    UpperCamelCase ,						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(UpperCamelCase      ) for title in docs['''title''']]
														provenance_strings.append('''\t'''.join(UpperCamelCase      )      )
							return provenance_strings
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  Tuple      ):





							'''simple docstring'''


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

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

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

														return answers
def 		snake_case_    ():





							'''simple docstring'''


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

							_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(UpperCamelCase ,						args.predictions_path ,						args.gold_data_path      )
																					continue

														logger.info('''***** Running evaluation for {} *****'''.format(UpperCamelCase      )      )
														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(UpperCamelCase ,						**UpperCamelCase      )
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						retriever=UpperCamelCase ,						**UpperCamelCase      )
																					model.retriever.init_retrieval()
														else:
																					_a	     =				model_class.from_pretrained(UpperCamelCase ,						**UpperCamelCase      )
														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(UpperCamelCase      ):
																												questions.append(line.strip()      )
																												if len(UpperCamelCase      ) == args.eval_batch_size:
																																			_a	     =				evaluate_batch_fn(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
																																			preds_file.write('''\n'''.join(UpperCamelCase      ) + '''\n'''      )
																																			preds_file.flush()
																																			_a	     =				[]
																					if len(UpperCamelCase      ) > 0:
																												_a	     =				evaluate_batch_fn(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
																												preds_file.write('''\n'''.join(UpperCamelCase      )      )
																												preds_file.flush()

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


if __name__ == "__main__":
				_snake_case      :      Optional[int]   							=		get_args()
				main(args)

