
"""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__ :       Any						     =			random.Random()





def __A						(   a_ :							int		,		a_ :							Optional[int]=1.0		,		a_ :							Optional[Any]=None		,		a_ :							Tuple=None     )->							Union[str, Any]:




		'''simple docstring'''






		if rng is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= 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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def __init__(						self    :Dict		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :int=7		, lowerCamelCase_    :Optional[Any]=4_00		, lowerCamelCase_    :List[Any]=20_00		, lowerCamelCase_    :str=24		, lowerCamelCase_    :Optional[int]=24		, lowerCamelCase_    :str=0.0		, lowerCamelCase_    :int=1_60_00		, lowerCamelCase_    :Optional[int]=True		, lowerCamelCase_    :Dict=True		, )							->			Optional[Any]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			List[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[str]		, lowerCamelCase_    :Optional[Any]=False		, lowerCamelCase_    :List[str]=False					)							->			Union[str, Any]:







			'''simple docstring'''




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

			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       :     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       :     List[str]				= [np.asarray(lowerCamelCase_					) for x in speech_inputs]
			return speech_inputs





@require_torch
@require_torchaudio
class 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  SpeechaTextFeatureExtractor if is_speech_available() else None





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= SpeechaTextFeatureExtractionTester(self					)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Tuple					)							->			str:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :str					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			# create three inputs of length 800, 1000, and 1200
			SCREAMING_SNAKE_CASE       :     List[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     Tuple				= [np.asarray(lowerCamelCase_					) for speech_input in speech_inputs]

			# Test feature size
			SCREAMING_SNAKE_CASE       :     str				= feature_extractor(lowerCamelCase_		, padding=lowerCamelCase_		, 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       :     Optional[Any]				= feature_extractor(speech_inputs[0]		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     int				= feature_extractor(np_speech_inputs[0]		, return_tensors='''np'''					).input_features
			self.assertTrue(np.allclose(lowerCamelCase_		, lowerCamelCase_		, atol=1E-3					)					)

			# Test batched
			SCREAMING_SNAKE_CASE       :     Optional[int]				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     Any				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			for enc_seq_a, enc_seq_a in zip(lowerCamelCase_		, lowerCamelCase_					):
					self.assertTrue(np.allclose(lowerCamelCase_		, lowerCamelCase_		, atol=1E-3					)					)

			# Test 2-D numpy arrays are batched.
			SCREAMING_SNAKE_CASE       :     Tuple				= [floats_list((1, x)					)[0] for x in (8_00, 8_00, 8_00)]
			SCREAMING_SNAKE_CASE       :     str				= np.asarray(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= feature_extractor(lowerCamelCase_		, return_tensors='''np'''					).input_features
			for enc_seq_a, enc_seq_a in zip(lowerCamelCase_		, lowerCamelCase_					):
					self.assertTrue(np.allclose(lowerCamelCase_		, lowerCamelCase_		, atol=1E-3					)					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]

			SCREAMING_SNAKE_CASE       :     Dict				= ['''longest''', '''max_length''', '''do_not_pad''']
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [None, 16, None]
			for max_length, padding in zip(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= feature_extractor(
					    lowerCamelCase_		, padding=lowerCamelCase_		, max_length=lowerCamelCase_		, return_attention_mask=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= inputs.input_features
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= inputs.attention_mask
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= [np.sum(lowerCamelCase_					) 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    :List[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     Dict				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]

			SCREAMING_SNAKE_CASE       :     str				= ['''longest''', '''max_length''', '''do_not_pad''']
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [None, 16, None]
			for max_length, padding in zip(lowerCamelCase_		, lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     List[Any]				= feature_extractor(
					    lowerCamelCase_		, max_length=lowerCamelCase_		, padding=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= inputs.input_features
					SCREAMING_SNAKE_CASE       :     str				= inputs.attention_mask
					SCREAMING_SNAKE_CASE       :     Dict				= [np.sum(lowerCamelCase_					) 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    :List[str]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     int				= feature_extractor(
			    lowerCamelCase_		, padding='''max_length'''		, max_length=4		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     Dict				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     Optional[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[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     Dict				= feature_extractor(
			    lowerCamelCase_		, padding='''longest'''		, max_length=4		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     int				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     int				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     Optional[int]				= 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)					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= [floats_list((1, x)					)[0] for x in range(8_00		, 14_00		, 2_00					)]
			SCREAMING_SNAKE_CASE       :     int				= feature_extractor(
			    lowerCamelCase_		, padding='''longest'''		, max_length=16		, truncation=lowerCamelCase_		, return_tensors='''np'''		, return_attention_mask=lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     int				= inputs.input_features
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= inputs.attention_mask
			SCREAMING_SNAKE_CASE       :     Union[str, 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, : 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    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			import torch

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

			for inputs in [py_speech_inputs, np_speech_inputs]:
					SCREAMING_SNAKE_CASE       :     Dict				= feature_extractor.pad([{'''input_features''': inputs}]		, return_tensors='''np'''					)
					self.assertTrue(np_processed.input_features.dtype == np.floataa					)
					SCREAMING_SNAKE_CASE       :     Any				= feature_extractor.pad([{'''input_features''': inputs}]		, return_tensors='''pt'''					)
					self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :List[Any]					)							->			Optional[Any]:







			'''simple docstring'''




			from datasets import load_dataset

			SCREAMING_SNAKE_CASE       :     Optional[int]				= load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		, '''clean'''		, split='''validation'''					)
			# automatic decoding with librispeech
			SCREAMING_SNAKE_CASE       :     Any				= ds.sort('''id'''					).select(range(lowerCamelCase_					)					)[:num_samples]['''audio''']

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







	def   __lowerCAmelCase (						self    :List[Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.array([
			    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
			    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
			    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
			]					)
			# fmt: on

			SCREAMING_SNAKE_CASE       :     List[Any]				= self._load_datasamples(1					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
			SCREAMING_SNAKE_CASE       :     List[str]				= feature_extractor(lowerCamelCase_		, return_tensors='''pt'''					).input_features
			self.assertEquals(input_features.shape		, (1, 5_84, 24)					)
			self.assertTrue(np.allclose(input_features[0, 0, :30]		, lowerCamelCase_		, 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__ :       Optional[Any]						     =			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def __A						(   a_ :							Optional[int]     )->							Dict:




		'''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_ :							List[Any]		,		a_ :							Optional[int]		,		a_ :							Optional[int]     )->							Dict:




		'''simple docstring'''






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





def __A						(   a_ :							List[Any]		,		a_ :							Union[str, Any]		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE       :     List[Any]				= pd.read_csv(a_		,		sep='''\t'''		,		header=a_     )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE       :     str				= ast.literal_eval(a_     )
						answers.append(a_     )
		else:
				SCREAMING_SNAKE_CASE       :     Any				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
				SCREAMING_SNAKE_CASE       :     Dict				= [[reference] for reference in references]

		SCREAMING_SNAKE_CASE       :     Dict				= 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_     )

		SCREAMING_SNAKE_CASE       :     Any				= 100.0 * em / total
		SCREAMING_SNAKE_CASE       :     Optional[int]				= 100.0 * fa / total

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





def __A						(   a_ :							Any		,		a_ :							Any		,		a_ :							List[Any]     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= args.k
		SCREAMING_SNAKE_CASE       :     Tuple				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [line.strip() for line in open(a_		,		'''r'''     ).readlines()]

		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for hypo, reference in zip(a_		,		a_     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= set(hypo.split('''\t'''     )[:k]     )
				SCREAMING_SNAKE_CASE       :     List[str]				= set(reference.split('''\t'''     )     )
				total += 1
				em += len(hypo_provenance & ref_provenance     ) / k

		SCREAMING_SNAKE_CASE       :     Dict				= 100.0 * em / total
		logger.info(F"Precision@{k}: {em: .2f}"     )





def __A						(   a_ :							Any		,		a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		def strip_title(a_ :							Optional[Any]     ):
				if title.startswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Tuple				= title[1:]
				if title.endswith('''"'''     ):
						SCREAMING_SNAKE_CASE       :     Any				= title[:-1]
				return title

		SCREAMING_SNAKE_CASE       :     Tuple				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_		,		)['''input_ids'''].to(args.device     )

		SCREAMING_SNAKE_CASE       :     Any				= rag_model.rag.question_encoder(a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE       :     Dict				= 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'''		,		)
		SCREAMING_SNAKE_CASE       :     Any				= rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
		SCREAMING_SNAKE_CASE       :     Dict				= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE       :     List[Any]				= [strip_title(a_     ) for title in docs['''title''']]
				provenance_strings.append('''\t'''.join(a_     )     )
		return provenance_strings





def __A						(   a_ :							List[Any]		,		a_ :							int		,		a_ :							str     )->							Tuple:




		'''simple docstring'''






		with torch.no_grad():
				SCREAMING_SNAKE_CASE       :     Optional[int]				= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    a_		,		return_tensors='''pt'''		,		padding=a_		,		truncation=a_     )

				SCREAMING_SNAKE_CASE       :     Dict				= inputs_dict.input_ids.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Any				= inputs_dict.attention_mask.to(args.device     )
				SCREAMING_SNAKE_CASE       :     Tuple				= 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]]		,		)
				SCREAMING_SNAKE_CASE       :     Dict				= 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						(   )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= 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.'''		,		)
		SCREAMING_SNAKE_CASE       :     List[str]				= parser.parse_args()
		SCREAMING_SNAKE_CASE       :     Dict				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )
		return args





def __A						(   a_ :							Optional[Any]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= 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[str]				= 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       :     Tuple				= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE       :     List[Any]				= args.index_path
		else:
				SCREAMING_SNAKE_CASE       :     str				= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE       :     Optional[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'''		,		a_     )

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





