




'''simple docstring'''







import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       NystromformerForMaskedLM,
       NystromformerForMultipleChoice,
       NystromformerForQuestionAnswering,
       NystromformerForSequenceClassification,
       NystromformerForTokenClassification,
       NystromformerModel,
   )
   from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


class 			_a  :
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	int   ,				lowercase						:	str=13   ,				lowercase						:	Any=7   ,				lowercase						:	str=True   ,				lowercase						:	int=True   ,				lowercase						:	int=True   ,				lowercase						:	Any=True   ,				lowercase						:	Any=99   ,				lowercase						:	Any=32   ,				lowercase						:	Dict=5   ,				lowercase						:	Optional[int]=4   ,				lowercase						:	Dict=37   ,				lowercase						:	int="gelu"   ,				lowercase						:	Union[str, Any]=0.1   ,				lowercase						:	Union[str, Any]=0.1   ,				lowercase						:	str=512   ,				lowercase						:	Tuple=16   ,				lowercase						:	List[str]=2   ,				lowercase						:	str=0.02   ,				lowercase						:	str=3   ,				lowercase						:	Dict=4   ,				lowercase						:	int=None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		seq_length
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		use_input_mask
           UpperCAmelCase			       =		use_token_type_ids
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		hidden_size
           UpperCAmelCase			       =		num_hidden_layers
           UpperCAmelCase			       =		num_attention_heads
           UpperCAmelCase			       =		intermediate_size
           UpperCAmelCase			       =		hidden_act
           UpperCAmelCase			       =		hidden_dropout_prob
           UpperCAmelCase			       =		attention_probs_dropout_prob
           UpperCAmelCase			       =		max_position_embeddings
           UpperCAmelCase			       =		type_vocab_size
           UpperCAmelCase			       =		type_sequence_label_size
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		num_choices
           UpperCAmelCase			       =		scope
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size				)

           UpperCAmelCase			       =		None
           if self.use_input_mask:
               UpperCAmelCase			       =		random_attention_mask([self.batch_size, self.seq_length]				)

           UpperCAmelCase			       =		None
           if self.use_token_type_ids:
               UpperCAmelCase			       =		ids_tensor([self.batch_size, self.seq_length]   ,				self.type_vocab_size				)

           UpperCAmelCase			       =		None
           UpperCAmelCase			       =		None
           UpperCAmelCase			       =		None
           if self.use_labels:
               UpperCAmelCase			       =		ids_tensor([self.batch_size]   ,				self.type_sequence_label_size				)
               UpperCAmelCase			       =		ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels				)
               UpperCAmelCase			       =		ids_tensor([self.batch_size]   ,				self.num_choices				)

           UpperCAmelCase			       =		self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           return NystromformerConfig(
               vocab_size=self.vocab_size   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				type_vocab_size=self.type_vocab_size   ,				is_decoder=lowercase   ,				initializer_range=self.initializer_range   ,				)
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[int]   ,				lowercase						:	List[Any]   ,				lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		NystromformerModel(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase				)
           UpperCAmelCase			       =		model(lowercase   ,				token_type_ids=lowercase				)
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Any   ,				lowercase						:	str   ,				lowercase						:	int   ,				lowercase						:	int   ,				lowercase						:	Dict   ,				lowercase						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		NystromformerForMaskedLM(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)				)
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Dict   ,				lowercase						:	Tuple   ,				lowercase						:	int   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[Any]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		NystromformerForQuestionAnswering(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(
               lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase   ,				start_positions=lowercase   ,				end_positions=lowercase   ,				)
           self.parent.assertEqual(result.start_logits.shape   ,				(self.batch_size, self.seq_length)				)
           self.parent.assertEqual(result.end_logits.shape   ,				(self.batch_size, self.seq_length)				)
       def     A      (	self						:	List[str]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[str]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	str   ,				lowercase						:	Optional[int]   ,				lowercase						:	Tuple   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		NystromformerForSequenceClassification(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)				)
       def     A      (	self						:	Any   ,				lowercase						:	str   ,				lowercase						:	List[Any]   ,				lowercase						:	str   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		NystromformerForTokenClassification(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	int   ,				lowercase						:	Optional[int]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Any   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[str]   ,				lowercase						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_choices
           UpperCAmelCase			       =		NystromformerForMultipleChoice(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		input_ids.unsqueeze(1				).expand(-1   ,				self.num_choices   ,				-1				).contiguous()
           UpperCAmelCase			       =		token_type_ids.unsqueeze(1				).expand(-1   ,				self.num_choices   ,				-1				).contiguous()
           UpperCAmelCase			       =		input_mask.unsqueeze(1				).expand(-1   ,				self.num_choices   ,				-1				).contiguous()
           UpperCAmelCase			       =		model(
               lowercase   ,				attention_mask=lowercase   ,				token_type_ids=lowercase   ,				labels=lowercase   ,				)
           self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)				)







       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     (
               UpperCAmelCase
           )       ,     
           )			       =		config_and_inputs
           UpperCAmelCase			       =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_torch
class 			_a  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Optional[int]   				=					(
           (
               NystromformerModel,
               NystromformerForMaskedLM,
               NystromformerForMultipleChoice,
               NystromformerForQuestionAnswering,
               NystromformerForSequenceClassification,
               NystromformerForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __a				:     List[Any]   				=					(
           {
               """feature-extraction""": NystromformerModel,
               """fill-mask""": NystromformerForMaskedLM,
               """question-answering""": NystromformerForQuestionAnswering,
               """text-classification""": NystromformerForSequenceClassification,
               """token-classification""": NystromformerForTokenClassification,
               """zero-shot""": NystromformerForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __a				:     Optional[int]   				=					False
       __a				:     int   				=					False
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		NystromformerModelTester(self				)
           UpperCAmelCase			       =		ConfigTester(self   ,				config_class=lowercase   ,				hidden_size=37				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               UpperCAmelCase			       =		type
               self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowercase				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowercase				)







       @slow
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase			       =		NystromformerModel.from_pretrained(lowercase				)
               self.assertIsNotNone(lowercase				)






@require_torch
class 			_a  (	unittest.TestCase ):
       @slow
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''				)
           UpperCAmelCase			       =		torch.tensor([[0, 1, 2, 3, 4, 5]]				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(lowercase				)[0]

           UpperCAmelCase			       =		torch.Size((1, 6, 768)				)
           self.assertEqual(output.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]				)

           self.assertTrue(torch.allclose(output[:, :3, :3]   ,				lowercase   ,				atol=1E-4				)				)







       @slow
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		'''the [MASK] of Belgium is Brussels'''

           UpperCAmelCase			       =		AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''				)
           UpperCAmelCase			       =		NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''				)

           UpperCAmelCase			       =		tokenizer(lowercase   ,				return_tensors='''pt'''				)

           with torch.no_grad():
               UpperCAmelCase			       =		model(encoding.input_ids				).logits

           UpperCAmelCase			       =		token_logits[:, 2, :].argmax(-1				)[0]

           self.assertEqual(tokenizer.decode(lowercase				)   ,				'''capital'''				)









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)
