



'''simple docstring'''

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BridgeTowerImageProcessor

class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_ = True     ,	A_ = None     ,	A_ = 32     ,	A_ = True     ,	A_ = 1 / 255     ,	A_ = True     ,	A_ = True     ,	A_ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,	A_ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,	A_ = True     ,	A_=7     ,	A_=30     ,	A_=400     ,	A_=3     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size if size is not None else {"""shortest_edge""": 288}
							lowerCAmelCase   =     size_divisor
							lowerCAmelCase   =     do_rescale
							lowerCAmelCase   =     rescale_factor
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     do_center_crop
							lowerCAmelCase   =     image_mean
							lowerCAmelCase   =     image_std
							lowerCAmelCase   =     do_pad
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     min_resolution
							lowerCAmelCase   =     max_resolution


						def     __snake_case   ( self				)     ->     List[str]:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}




						def     __snake_case   ( self     ,	A_     ,	A_=False				)     ->     int:







							if not batched:
								lowerCAmelCase   =     self.size["""shortest_edge"""]
								lowerCAmelCase   =     image_inputs[0]
								if isinstance(A_     ,	Image.Image				):
									lowerCAmelCase,       lowerCAmelCase   =     image.size
								else:
									lowerCAmelCase,       lowerCAmelCase   =     image.shape[1], image.shape[2]
								lowerCAmelCase   =     size / min(A_     ,	A_				)
								if h < w:
									lowerCAmelCase,       lowerCAmelCase   =     size, scale * w
								else:
									lowerCAmelCase,       lowerCAmelCase   =     scale * h, size

								lowerCAmelCase   =     int((1333 / 800) * size				)
								if max(A_     ,	A_				) > max_size:
									lowerCAmelCase   =     max_size / max(A_     ,	A_				)
									lowerCAmelCase   =     newh * scale
									lowerCAmelCase   =     neww * scale

								lowerCAmelCase,       lowerCAmelCase   =     int(newh + 0.5				), int(neww + 0.5				)
								lowerCAmelCase,       lowerCAmelCase   =     (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

							else:
								lowerCAmelCase   =     []
								for image in image_inputs:
									lowerCAmelCase,       lowerCAmelCase   =     self.get_expected_values([image]				)
									expected_values.append((expected_height, expected_width)				)
								lowerCAmelCase   =     max(A_     ,	key=lambda A_				: item[0]				)[0]
								lowerCAmelCase   =     max(A_     ,	key=lambda A_				: item[1]				)[1]

							return expected_height, expected_width







@require_torch
@require_vision
class __snake_case(				lowerCamelCase__		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[Any]									=		BridgeTowerImageProcessor if is_vision_available() else None


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     BridgeTowerImageProcessingTester(self				)


						@property
						def     __snake_case   ( self				)     ->     List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(A_     ,	"""image_mean"""				)				)
							self.assertTrue(hasattr(A_     ,	"""image_std"""				)				)
							self.assertTrue(hasattr(A_     ,	"""do_normalize"""				)				)
							self.assertTrue(hasattr(A_     ,	"""do_resize"""				)				)
							self.assertTrue(hasattr(A_     ,	"""size"""				)				)
							self.assertTrue(hasattr(A_     ,	"""size_divisor"""				)				)


						def     __snake_case   ( self				)     ->     Dict:
							pass


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	Image.Image				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_     ,	batched=A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )     ,	)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_     ,	numpify=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	np.ndarray				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_     ,	batched=A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )     ,	)




						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_     ,	torchify=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	torch.Tensor				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values

							lowerCAmelCase,       lowerCAmelCase   =     self.image_processor_tester.get_expected_values(A_     ,	batched=A_				)
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )     ,	)




'''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


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

transformers_logging.set_verbosity_info()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[Any]:







	"""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		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	return max(metric_fn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for gt in ground_truths							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     []

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

	lowerCAmelCase   =     lowerCAmelCase   =     lowerCAmelCase   =     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							)

	lowerCAmelCase   =     100.0 * em / total
	lowerCAmelCase   =     100.0 * fa / total

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     args.k
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]

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

	lowerCAmelCase   =     100.0 * em / total
	logger.info(f'Precision@{k}: {em: .2f}'							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Any:







	"""simple docstring"""
	def strip_title(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
		if title.startswith("""\""""							):
			lowerCAmelCase   =     title[1:]
		if title.endswith("""\""""							):
			lowerCAmelCase   =     title[:-1]
		return title

	lowerCAmelCase   =     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							)

	lowerCAmelCase   =     rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     question_enc_outputs[0]

	lowerCAmelCase   =     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"""							,		)
	lowerCAmelCase   =     rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
	lowerCAmelCase   =     []
	for docs in all_docs:
		lowerCAmelCase   =     [strip_title(_SCREAMING_SNAKE_CASE							) for title in docs["""title"""]]
		provenance_strings.append("""\t""".join(_SCREAMING_SNAKE_CASE							)							)
	return provenance_strings






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Tuple:







	"""simple docstring"""
	with torch.no_grad():
		lowerCAmelCase   =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _SCREAMING_SNAKE_CASE							,		return_tensors="""pt"""							,		padding=_SCREAMING_SNAKE_CASE							,		truncation=_SCREAMING_SNAKE_CASE							)

		lowerCAmelCase   =     inputs_dict.input_ids.to(args.device							)
		lowerCAmelCase   =     inputs_dict.attention_mask.to(args.device							)
		lowerCAmelCase   =     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]]							,		)
		lowerCAmelCase   =     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 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     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."""							,		)
	lowerCAmelCase   =     parser.parse_args()
	lowerCAmelCase   =     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)
	return args






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Optional[Any]:







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

	lowerCAmelCase   =     (
	    [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							)

	lowerCAmelCase   =     get_scores if args.eval_mode == """e2e""" else get_precision_at_k
	lowerCAmelCase   =     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"""							):
			lowerCAmelCase   =     RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase   =     model_class.from_pretrained(_SCREAMING_SNAKE_CASE							,		retriever=_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
			model.retriever.init_retrieval()
		else:
			lowerCAmelCase   =     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:
			lowerCAmelCase   =     []
			for line in tqdm(_SCREAMING_SNAKE_CASE							):
				questions.append(line.strip()							)
				if len(_SCREAMING_SNAKE_CASE							) == args.eval_batch_size:
					lowerCAmelCase   =     evaluate_batch_fn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
					preds_file.write("""\n""".join(_SCREAMING_SNAKE_CASE							) + """\n"""							)
					preds_file.flush()
					lowerCAmelCase   =     []
			if len(_SCREAMING_SNAKE_CASE							) > 0:
				lowerCAmelCase   =     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__":
				UpperCAmelCase     		=   get_args()
				main(args)