"""simple docstring"""





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

lowerCAmelCase_     =	random.Random()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None  )->    List[Any]:
       if rng is None:
              _SCREAMING_SNAKE_CASE		:						List[Any]			  =    global_rng

       _SCREAMING_SNAKE_CASE		:						List[str]			  =    []
       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  _snake_case		(    unittest.TestCase ):







    """simple docstring"""






    def __init__(      self							:							List[Any]      ,    _A							:							Tuple      ,    _A							:							int=7      ,    _A							:							List[Any]=4_0_0      ,    _A							:							str=2_0_0_0      ,    _A							:							List[Any]=2_4      ,    _A							:							Tuple=2_4      ,    _A							:							Union[str, Any]=0.0      ,    _A							:							int=1_6_0_0_0      ,    _A							:							List[str]=True      ,    _A							:							Union[str, Any]=True      ,    ):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Any			  =    parent
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    batch_size
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    min_seq_length
           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    max_seq_length
           _SCREAMING_SNAKE_CASE		:						int			  =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _SCREAMING_SNAKE_CASE		:						int			  =    feature_size
           _SCREAMING_SNAKE_CASE		:						str			  =    num_mel_bins
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    padding_value
           _SCREAMING_SNAKE_CASE		:						List[str]			  =    sampling_rate
           _SCREAMING_SNAKE_CASE		:						int			  =    return_attention_mask
           _SCREAMING_SNAKE_CASE		:						Dict			  =    do_normalize





    def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



           """simple docstring"""







           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							:							List[Any]      ,    _A							:							Dict=False      ,    _A							:							List[str]=False):



           """simple docstring"""







           def _flatten(_A							:							Dict):
                  return list(itertools.chain(*_A))

           if equal_length:
                  _SCREAMING_SNAKE_CASE		:						Any			  =    [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
           else:
                  # make sure that inputs increase in size
                  _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [
                      floats_list((x, self.feature_size))
                      for x in range(self.min_seq_length      ,    self.max_seq_length      ,    self.seq_length_diff)
                  ]
           if numpify:
                  _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [np.asarray(_A) for x in speech_inputs]
           return speech_inputs




@require_torch
@require_torchaudio
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







    """simple docstring"""






    a			=						SpeechaTextFeatureExtractor if is_speech_available() else None





    def 		_lowerCAmelCase		(      self							:							Dict):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Dict			  =    SpeechaTextFeatureExtractionTester(self)





    def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Tuple):



           """simple docstring"""







           self.assertTrue(np.all(np.mean(_A      ,    axis=0) < 1e-3))
           self.assertTrue(np.all(np.abs(np.var(_A      ,    axis=0) - 1) < 1e-3))





    def 		_lowerCAmelCase		(      self							:							Any):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						str			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           _SCREAMING_SNAKE_CASE		:						Dict			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]
           _SCREAMING_SNAKE_CASE		:						Dict			  =    [np.asarray(_A) for speech_input in speech_inputs]

           # Test feature size
           _SCREAMING_SNAKE_CASE		:						Any			  =    feature_extractor(_A      ,    padding=_A      ,    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
           _SCREAMING_SNAKE_CASE		:						str			  =    feature_extractor(speech_inputs[0]      ,    return_tensors="""np""").input_features
           _SCREAMING_SNAKE_CASE		:						str			  =    feature_extractor(np_speech_inputs[0]      ,    return_tensors="""np""").input_features
           self.assertTrue(np.allclose(_A      ,    _A      ,    atol=1e-3))

           # Test batched
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    feature_extractor(_A      ,    return_tensors="""np""").input_features
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    feature_extractor(_A      ,    return_tensors="""np""").input_features
           for enc_seq_a, enc_seq_a in zip(_A      ,    _A):
                  self.assertTrue(np.allclose(_A      ,    _A      ,    atol=1e-3))

           # Test 2-D numpy arrays are batched.
           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.asarray(_A)
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    feature_extractor(_A      ,    return_tensors="""np""").input_features
           _SCREAMING_SNAKE_CASE		:						Dict			  =    feature_extractor(_A      ,    return_tensors="""np""").input_features
           for enc_seq_a, enc_seq_a in zip(_A      ,    _A):
                  self.assertTrue(np.allclose(_A      ,    _A      ,    atol=1e-3))





    def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						int			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]

           _SCREAMING_SNAKE_CASE		:						int			  =    ["""longest""", """max_length""", """do_not_pad"""]
           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [None, 1_6, None]
           for max_length, padding in zip(_A      ,    _A):
                  _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feature_extractor(
                      _A      ,    padding=_A      ,    max_length=_A      ,    return_attention_mask=_A)
                  _SCREAMING_SNAKE_CASE		:						List[str]			  =    inputs.input_features
                  _SCREAMING_SNAKE_CASE		:						Tuple			  =    inputs.attention_mask
                  _SCREAMING_SNAKE_CASE		:						List[str]			  =    [np.sum(_A) 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							:							str):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Any			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						Any			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]

           _SCREAMING_SNAKE_CASE		:						str			  =    ["""longest""", """max_length""", """do_not_pad"""]
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    [None, 1_6, None]
           for max_length, padding in zip(_A      ,    _A):
                  _SCREAMING_SNAKE_CASE		:						Tuple			  =    feature_extractor(
                      _A      ,    max_length=_A      ,    padding=_A      ,    return_tensors="""np"""      ,    return_attention_mask=_A)
                  _SCREAMING_SNAKE_CASE		:						List[Any]			  =    inputs.input_features
                  _SCREAMING_SNAKE_CASE		:						str			  =    inputs.attention_mask
                  _SCREAMING_SNAKE_CASE		:						int			  =    [np.sum(_A) 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							:							str):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						List[str]			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]
           _SCREAMING_SNAKE_CASE		:						Dict			  =    feature_extractor(
               _A      ,    padding="""max_length"""      ,    max_length=4      ,    truncation=_A      ,    return_tensors="""np"""      ,    return_attention_mask=_A      ,    )
           _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs.input_features
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    inputs.attention_mask
           _SCREAMING_SNAKE_CASE		:						Any			  =    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							:							Optional[int]):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Dict			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						int			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]
           _SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feature_extractor(
               _A      ,    padding="""longest"""      ,    max_length=4      ,    truncation=_A      ,    return_tensors="""np"""      ,    return_attention_mask=_A      ,    )
           _SCREAMING_SNAKE_CASE		:						List[Any]			  =    inputs.input_features
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    inputs.attention_mask
           _SCREAMING_SNAKE_CASE		:						Dict			  =    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, 2_4))

           _SCREAMING_SNAKE_CASE		:						Dict			  =    [floats_list((1, x))[0] for x in range(8_0_0      ,    1_4_0_0      ,    2_0_0)]
           _SCREAMING_SNAKE_CASE		:						Tuple			  =    feature_extractor(
               _A      ,    padding="""longest"""      ,    max_length=1_6      ,    truncation=_A      ,    return_tensors="""np"""      ,    return_attention_mask=_A      ,    )
           _SCREAMING_SNAKE_CASE		:						List[str]			  =    inputs.input_features
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    inputs.attention_mask
           _SCREAMING_SNAKE_CASE		:						str			  =    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, 2_4))





    def 		_lowerCAmelCase		(      self							:							List[str]):



           """simple docstring"""







           import torch

           _SCREAMING_SNAKE_CASE		:						Any			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						Dict			  =    np.random.rand(1_0_0      ,    3_2).astype(np.floataa)
           _SCREAMING_SNAKE_CASE		:						Any			  =    np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                  _SCREAMING_SNAKE_CASE		:						int			  =    feature_extractor.pad([{"""input_features""": inputs}]      ,    return_tensors="""np""")
                  self.assertTrue(np_processed.input_features.dtype == np.floataa)
                  _SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    feature_extractor.pad([{"""input_features""": inputs}]      ,    return_tensors="""pt""")
                  self.assertTrue(pt_processed.input_features.dtype == torch.floataa)





    def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							List[Any]):



           """simple docstring"""







           from datasets import load_dataset

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

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





    def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



           """simple docstring"""







           _SCREAMING_SNAKE_CASE		:						Dict			  =    np.array([
               -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
               -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
               -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
           ])
           # fmt: on

           _SCREAMING_SNAKE_CASE		:						Dict			  =    self._load_datasamples(1)
           _SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    feature_extractor(_A      ,    return_tensors="""pt""").input_features
           self.assertEquals(input_features.shape      ,    (1, 5_8_4, 2_4))
           self.assertTrue(np.allclose(input_features[0, 0, :3_0]      ,    _A      ,    atol=1e-4))



"""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


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

transformers_logging.set_verbosity_info()



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							return max(metric_fn(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) for gt in ground_truths  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []

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

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    1_00.0 * em / total
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_00.0 * fa / total

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    args.k
							_SCREAMING_SNAKE_CASE		:						int			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]
							_SCREAMING_SNAKE_CASE		:						Any			  =    [line.strip() for line in open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ).readlines()]

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

							_SCREAMING_SNAKE_CASE		:						int			  =    1_00.0 * em / total
							logger.info(F"""Precision@{k}: {em: .2f}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							def strip_title(__SCREAMING_SNAKE_CASE  ):
														if title.startswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    title[1:]
														if title.endswith("""\""""  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    title[:-1]
														return title

							_SCREAMING_SNAKE_CASE		:						Dict			  =    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  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    question_enc_outputs[0]

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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"""     ,	)
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							for docs in all_docs:
														_SCREAMING_SNAKE_CASE		:						str			  =    [strip_title(__SCREAMING_SNAKE_CASE  ) for title in docs["""title"""]]
														provenance_strings.append("""\t""".join(__SCREAMING_SNAKE_CASE  )  )
							return provenance_strings



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    __SCREAMING_SNAKE_CASE     ,	return_tensors="""pt"""     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.input_ids.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inputs_dict.attention_mask.to(args.device  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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]]     ,	)
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    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      lowerCamelCase_()->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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."""     ,	)
							_SCREAMING_SNAKE_CASE		:						Dict			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
							return args



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {}
							if args.model_type is None:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    infer_model_type(args.model_name_or_path  )
														assert args.model_type is not None
							if args.model_type.startswith("""rag"""  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.n_docs
														if args.index_name is not None:
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    args.index_name
														if args.index_path is not None:
																					_SCREAMING_SNAKE_CASE		:						Any			  =    args.index_path
							else:
														_SCREAMING_SNAKE_CASE		:						Any			  =    BartForConditionalGeneration

							_SCREAMING_SNAKE_CASE		:						int			  =    (
							    [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  )

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


