




import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase_									=	random.Random()




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					Union[str, Any]=1.0  ,			A__       :					int=None  ,			A__       :					Union[str, Any]=None	):





       '''simple docstring'''



       if rng is None:
              __lowerCamelCase   =						global_rng

       __lowerCamelCase   =						[]
       for batch_idx in range(shape[0]	):
              values.append([]	)
              for _ in range(shape[1]	):
                     values[-1].append(rng.random() * scale	)

       return values





@require_torch
@require_torchaudio
class     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Tuple    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Union[str, Any]=7    ,       UpperCamelCase_: List[str]=4_00    ,       UpperCamelCase_: Union[str, Any]=20_00    ,       UpperCamelCase_: Union[str, Any]=24    ,       UpperCamelCase_: List[Any]=24    ,       UpperCamelCase_: Union[str, Any]=0.0    ,       UpperCamelCase_: List[str]=1_60_00    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Union[str, Any]=True    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						min_seq_length
             __lowerCamelCase   =						max_seq_length
             __lowerCamelCase   =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             __lowerCamelCase   =						feature_size
             __lowerCamelCase   =						num_mel_bins
             __lowerCamelCase   =						padding_value
             __lowerCamelCase   =						sampling_rate
             __lowerCamelCase   =						return_attention_mask
             __lowerCamelCase   =						do_normalize
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             return {
                 "feature_size": self.feature_size,
                 "num_mel_bins": self.num_mel_bins,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: List[Any]=False    ,       UpperCamelCase_: Tuple=False   ):
             def _flatten(UpperCamelCase_: int   ):
                    return list(itertools.chain(*UpperCamelCase_   )   )

             if equal_length:
                    __lowerCamelCase   =						[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
             else:
                    # make sure that inputs increase in size
                    __lowerCamelCase   =						[
                        floats_list((x, self.feature_size)   )
                        for x in range(self.min_seq_length    ,       self.max_seq_length    ,       self.seq_length_diff   )
                    ]
             if numpify:
                    __lowerCamelCase   =						[np.asarray(UpperCamelCase_   ) for x in speech_inputs]
             return speech_inputs





@require_torch
@require_torchaudio
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	str    		=   SpeechaTextFeatureExtractor if is_speech_available() else None
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						SpeechaTextFeatureExtractionTester(self   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Any   ):
             self.assertTrue(np.all(np.mean(UpperCamelCase_    ,       axis=0   ) < 1E-3   )   )
             self.assertTrue(np.all(np.abs(np.var(UpperCamelCase_    ,       axis=0   ) - 1   ) < 1E-3   )   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             # Tests that all call wrap to encode_plus and batch_encode_plus
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             # create three inputs of length 800, 1000, and 1200
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]
             __lowerCamelCase   =						[np.asarray(UpperCamelCase_   ) for speech_input in speech_inputs]

             # Test feature size
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       padding=UpperCamelCase_    ,       return_tensors="""np"""   ).input_features
             self.assertTrue(input_features.ndim == 3   )
             self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

             # Test not batched input
             __lowerCamelCase   =						feature_extractor(speech_inputs[0]    ,       return_tensors="""np"""   ).input_features
             __lowerCamelCase   =						feature_extractor(np_speech_inputs[0]    ,       return_tensors="""np"""   ).input_features
             self.assertTrue(np.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-3   )   )

             # Test batched
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       return_tensors="""np"""   ).input_features
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       return_tensors="""np"""   ).input_features
             for enc_seq_a, enc_seq_a in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                    self.assertTrue(np.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-3   )   )

             # Test 2-D numpy arrays are batched.
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
             __lowerCamelCase   =						np.asarray(UpperCamelCase_   )
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       return_tensors="""np"""   ).input_features
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       return_tensors="""np"""   ).input_features
             for enc_seq_a, enc_seq_a in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                    self.assertTrue(np.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-3   )   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]

             __lowerCamelCase   =						["""longest""", """max_length""", """do_not_pad"""]
             __lowerCamelCase   =						[None, 16, None]
             for max_length, padding in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						feature_extractor(
                        UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       return_attention_mask=UpperCamelCase_   )
                    __lowerCamelCase   =						inputs.input_features
                    __lowerCamelCase   =						inputs.attention_mask
                    __lowerCamelCase   =						[np.sum(UpperCamelCase_   ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]

             __lowerCamelCase   =						["""longest""", """max_length""", """do_not_pad"""]
             __lowerCamelCase   =						[None, 16, None]
             for max_length, padding in zip(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						feature_extractor(
                        UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       return_tensors="""np"""    ,       return_attention_mask=UpperCamelCase_   )
                    __lowerCamelCase   =						inputs.input_features
                    __lowerCamelCase   =						inputs.attention_mask
                    __lowerCamelCase   =						[np.sum(UpperCamelCase_   ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
                    self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6   )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
                    self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6   )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]
             __lowerCamelCase   =						feature_extractor(
                 UpperCamelCase_    ,       padding="""max_length"""    ,       max_length=4    ,       truncation=UpperCamelCase_    ,       return_tensors="""np"""    ,       return_attention_mask=UpperCamelCase_    ,       )
             __lowerCamelCase   =						inputs.input_features
             __lowerCamelCase   =						inputs.attention_mask
             __lowerCamelCase   =						np.sum(attention_mask == 1    ,       axis=1   )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
             self._check_zero_mean_unit_variance(input_features[1]   )
             self._check_zero_mean_unit_variance(input_features[2]   )
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]
             __lowerCamelCase   =						feature_extractor(
                 UpperCamelCase_    ,       padding="""longest"""    ,       max_length=4    ,       truncation=UpperCamelCase_    ,       return_tensors="""np"""    ,       return_attention_mask=UpperCamelCase_    ,       )
             __lowerCamelCase   =						inputs.input_features
             __lowerCamelCase   =						inputs.attention_mask
             __lowerCamelCase   =						np.sum(attention_mask == 1    ,       axis=1   )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
             self._check_zero_mean_unit_variance(input_features[2]   )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape    ,       (3, 4, 24)   )

             __lowerCamelCase   =						[floats_list((1, x)   )[0] for x in range(8_00    ,       14_00    ,       2_00   )]
             __lowerCamelCase   =						feature_extractor(
                 UpperCamelCase_    ,       padding="""longest"""    ,       max_length=16    ,       truncation=UpperCamelCase_    ,       return_tensors="""np"""    ,       return_attention_mask=UpperCamelCase_    ,       )
             __lowerCamelCase   =						inputs.input_features
             __lowerCamelCase   =						inputs.attention_mask
             __lowerCamelCase   =						np.sum(attention_mask == 1    ,       axis=1   )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
             self._check_zero_mean_unit_variance(input_features[2]   )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape    ,       (3, 6, 24)   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             import torch

             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						np.random.rand(1_00    ,       32   ).astype(np.floataa   )
             __lowerCamelCase   =						np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    __lowerCamelCase   =						feature_extractor.pad([{"""input_features""": inputs}]    ,       return_tensors="""np"""   )
                    self.assertTrue(np_processed.input_features.dtype == np.floataa   )
                    __lowerCamelCase   =						feature_extractor.pad([{"""input_features""": inputs}]    ,       return_tensors="""pt"""   )
                    self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Tuple   ):
             from datasets import load_dataset

             __lowerCamelCase   =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,       """clean"""    ,       split="""validation"""   )
             # automatic decoding with librispeech
             __lowerCamelCase   =						ds.sort("""id"""   ).select(range(UpperCamelCase_   )   )[:num_samples]["""audio"""]

             return [x["array"] for x in speech_samples]




      def 			lowerCAmelCase__						(				self: Any   ):
             # fmt: off
             __lowerCamelCase   =						np.array([
                 -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
                 -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
                 -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
             ]   )
             # fmt: on

             __lowerCamelCase   =						self._load_datasamples(1   )
             __lowerCamelCase   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
             __lowerCamelCase   =						feature_extractor(UpperCamelCase_    ,       return_tensors="""pt"""   ).input_features
             self.assertEquals(input_features.shape    ,       (1, 5_84, 24)   )
             self.assertTrue(np.allclose(input_features[0, 0, :30]    ,       UpperCamelCase_    ,       atol=1E-4   )   )






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


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

transformers_logging.set_verbosity_info()






def     a(      A       :    Optional[Any]       )		->       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(      A       :    Dict ,     A       :    List[Any] ,     A       :    str       )		->       List[str]:
					"""simple docstring"""





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






def     a(      A       :    str ,     A       :    Optional[Any] ,     A       :    Optional[Any]       )		->       Optional[int]:
					"""simple docstring"""





					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]
					a					     =   []

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

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

					a					     =   100.0 * em / total
					a					     =   100.0 * fa / total

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






def     a(      A       :    Dict ,     A       :    str ,     A       :    List[str]       )		->       List[Any]:
					"""simple docstring"""





					a					     =   args.k
					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]
					a					     =   [line.strip() for line in open(A ,     "r"       ).readlines()]

					a					     =   a					     =   0
					for hypo, reference in zip(A ,     A       ):
										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(      A       :    Dict ,     A       :    Any ,     A       :    List[Any]       )		->       Any:
					"""simple docstring"""





					def strip_title(A       :    Any       ):
										if title.startswith("\""       ):
															a					     =   title[1:]
										if title.endswith("\""       ):
															a					     =   title[:-1]
										return title

					a					     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    A ,     return_tensors="pt" ,     padding=A ,     truncation=A ,     )["input_ids"].to(args.device       )

					a					     =   rag_model.rag.question_encoder(A       )
					a					     =   question_enc_outputs[0]

					a					     =   rag_model.retriever(
					    A ,     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(A       ) for title in docs["title"]]
										provenance_strings.append("\t".join(A       )       )
					return provenance_strings






def     a(      A       :    Union[str, Any] ,     A       :    Optional[int] ,     A       :    Tuple       )		->       Tuple:
					"""simple docstring"""





					with torch.no_grad():
										a					     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    A ,     return_tensors="pt" ,     padding=A ,     truncation=A       )

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

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

										return answers






def     a(      )		->       Any:
					"""simple docstring"""





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

					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(A ,     args.predictions_path ,     args.gold_data_path       )
															continue

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

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


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