




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case      :      str   							=		logging.get_logger(__name__)


if is_vision_available():
				import PIL

class 			A    (		_a    ):
							lowercase_							=				['pixel_values']







							def __init__(  self       :  Dict							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  Dict[str, int] = None							,			lowerCAmelCase_       :  PILImageResampling = PILImageResampling.BILINEAR							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  bool = False							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  Union[int, float] = 1 / 2_55							,			lowerCAmelCase_       :  bool = True							,			lowerCAmelCase_       :  Optional[Union[float, List[float]]] = None							,			lowerCAmelCase_       :  Optional[Union[float, List[float]]] = None							,			**lowerCAmelCase_       :  str							,			)			->							None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_					)

														_a	     =				size if size is not None else {'''height''': 25_60, '''width''': 19_20}
														if isinstance(lowerCAmelCase_							,			(tuple, list)					):
																					# The previous feature extractor size parameter was in (width, height) format
																					_a	     =				size[::-1]
														_a	     =				get_size_dict(lowerCAmelCase_					)

														_a	     =				do_resize
														_a	     =				size
														_a	     =				resample
														_a	     =				do_thumbnail
														_a	     =				do_align_long_axis
														_a	     =				do_pad
														_a	     =				do_rescale
														_a	     =				rescale_factor
														_a	     =				do_normalize
														_a	     =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a	     =				image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Dict[str, int]							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None					)			->							np.ndarray:


														"""simple docstring"""


														_a     ,							_a	     =				get_image_size(lowerCAmelCase_					)
														_a     ,							_a	     =				size['''height'''], size['''width''']

														if (output_width < output_height and input_width > input_height) or (
														    output_width > output_height and input_width < input_height
														):
																					_a	     =				np.rotaa(lowerCAmelCase_							,			3					)

														if data_format is not None:
																					_a	     =				to_channel_dimension_format(lowerCAmelCase_							,			lowerCAmelCase_					)

														return image







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Dict[str, int]							,			lowerCAmelCase_       :  bool = False							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None							,			)			->							np.ndarray:


														"""simple docstring"""


														_a     ,							_a	     =				size['''height'''], size['''width''']
														_a     ,							_a	     =				get_image_size(lowerCAmelCase_					)

														_a	     =				output_width - input_width
														_a	     =				output_height - input_height

														if random_padding:
																					_a	     =				np.random.randint(low=0							,			high=delta_height + 1					)
																					_a	     =				np.random.randint(low=0							,			high=delta_width + 1					)
														else:
																					_a	     =				delta_height // 2
																					_a	     =				delta_width // 2

														_a	     =				delta_height - pad_top
														_a	     =				delta_width - pad_left

														_a	     =				((pad_top, pad_bottom), (pad_left, pad_right))
														return pad(lowerCAmelCase_							,			lowerCAmelCase_							,			data_format=lowerCAmelCase_					)







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


														"""simple docstring"""


														logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''					)
														return self.pad_image(*lowerCAmelCase_							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Dict[str, int]							,			lowerCAmelCase_       :  PILImageResampling = PILImageResampling.BICUBIC							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None							,			**lowerCAmelCase_       :  int							,			)			->							np.ndarray:


														"""simple docstring"""


														_a     ,							_a	     =				get_image_size(lowerCAmelCase_					)
														_a     ,							_a	     =				size['''height'''], size['''width''']

														# We always resize to the smallest of either the input or output size.
														_a	     =				min(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				min(lowerCAmelCase_							,			lowerCAmelCase_					)

														if height == input_height and width == input_width:
																					return image

														if input_height > input_width:
																					_a	     =				int(input_width * height / input_height					)
														elif input_width > input_height:
																					_a	     =				int(input_height * width / input_width					)

														return resize(
														    lowerCAmelCase_							,			size=(height, width)							,			resample=lowerCAmelCase_							,			reducing_gap=2.0							,			data_format=lowerCAmelCase_							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Dict[str, int]							,			lowerCAmelCase_       :  PILImageResampling = PILImageResampling.BICUBIC							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None							,			**lowerCAmelCase_       :  Optional[int]							,			)			->							np.ndarray:


														"""simple docstring"""


														_a	     =				get_size_dict(lowerCAmelCase_					)
														_a	     =				min(size['''height''']							,			size['''width''']					)
														_a	     =				get_resize_output_image_size(lowerCAmelCase_							,			size=lowerCAmelCase_							,			default_to_square=lowerCAmelCase_					)
														_a	     =				resize(lowerCAmelCase_							,			size=lowerCAmelCase_							,			resample=lowerCAmelCase_							,			data_format=lowerCAmelCase_							,			**lowerCAmelCase_					)
														return resized_image







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Union[int, float]							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None							,			**lowerCAmelCase_       :  Union[str, Any]							,			)			->							Dict:


														"""simple docstring"""


														return rescale(lowerCAmelCase_							,			scale=lowerCAmelCase_							,			data_format=lowerCAmelCase_							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  np.ndarray							,			lowerCAmelCase_       :  Union[float, List[float]]							,			lowerCAmelCase_       :  Union[float, List[float]]							,			lowerCAmelCase_       :  Optional[Union[str, ChannelDimension]] = None							,			**lowerCAmelCase_       :  Optional[Any]							,			)			->							np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_							,			mean=lowerCAmelCase_							,			std=lowerCAmelCase_							,			data_format=lowerCAmelCase_							,			**lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  ImageInput							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  Dict[str, int] = None							,			lowerCAmelCase_       :  PILImageResampling = None							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  bool = False							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  float = None							,			lowerCAmelCase_       :  bool = None							,			lowerCAmelCase_       :  Optional[Union[float, List[float]]] = None							,			lowerCAmelCase_       :  Optional[Union[float, List[float]]] = None							,			lowerCAmelCase_       :  Optional[Union[str, TensorType]] = None							,			lowerCAmelCase_       :  Optional[ChannelDimension] = ChannelDimension.FIRST							,			**lowerCAmelCase_       :  str							,			)			->							PIL.Image.Image:


														"""simple docstring"""


														_a	     =				do_resize if do_resize is not None else self.do_resize
														_a	     =				size if size is not None else self.size
														if isinstance(lowerCAmelCase_							,			(tuple, list)					):
																					# Previous feature extractor had size in (width, height) format
																					_a	     =				size[::-1]
														_a	     =				get_size_dict(lowerCAmelCase_					)
														_a	     =				resample if resample is not None else self.resample
														_a	     =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
														_a	     =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
														_a	     =				do_pad if do_pad is not None else self.do_pad
														_a	     =				do_rescale if do_rescale is not None else self.do_rescale
														_a	     =				rescale_factor if rescale_factor is not None else self.rescale_factor
														_a	     =				do_normalize if do_normalize is not None else self.do_normalize
														_a	     =				image_mean if image_mean is not None else self.image_mean
														_a	     =				image_std if image_std is not None else self.image_std

														_a	     =				make_list_of_images(lowerCAmelCase_					)

														if not valid_images(lowerCAmelCase_					):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

														if do_resize and size is None:
																					raise ValueError('''Size must be specified if do_resize is True.'''					)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

														if do_pad and size is None:
																					raise ValueError('''Size must be specified if do_pad is True.'''					)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

														# All transformations expect numpy arrays.
														_a	     =				[to_numpy_array(lowerCAmelCase_					) for image in images]

														if do_align_long_axis:
																					_a	     =				[self.align_long_axis(lowerCAmelCase_							,			size=lowerCAmelCase_					) for image in images]

														if do_resize:
																					_a	     =				[self.resize(image=lowerCAmelCase_							,			size=lowerCAmelCase_							,			resample=lowerCAmelCase_					) for image in images]

														if do_thumbnail:
																					_a	     =				[self.thumbnail(image=lowerCAmelCase_							,			size=lowerCAmelCase_					) for image in images]

														if do_pad:
																					_a	     =				[self.pad_image(image=lowerCAmelCase_							,			size=lowerCAmelCase_							,			random_padding=lowerCAmelCase_					) for image in images]

														if do_rescale:
																					_a	     =				[self.rescale(image=lowerCAmelCase_							,			scale=lowerCAmelCase_					) for image in images]

														if do_normalize:
																					_a	     =				[self.normalize(image=lowerCAmelCase_							,			mean=lowerCAmelCase_							,			std=lowerCAmelCase_					) for image in images]

														_a	     =				[to_channel_dimension_format(lowerCAmelCase_							,			lowerCAmelCase_					) for image in images]

														_a	     =				{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_							,			tensor_type=lowerCAmelCase_					)







'''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)

