





"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConditionalDetrImageProcessor




class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	def __init__(						self    :List[Any]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[Any]=7		, lowerCamelCase_    :str=3		, lowerCamelCase_    :Optional[Any]=30		, lowerCamelCase_    :Dict=4_00		, lowerCamelCase_    :Any=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :List[Any]=[0.5, 0.5, 0.5]		, lowerCamelCase_    :List[str]=[0.5, 0.5, 0.5]		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Tuple=1 / 2_55		, lowerCamelCase_    :Any=True		, )							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
			SCREAMING_SNAKE_CASE       :     Tuple				= parent
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     str				= num_channels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= min_resolution
			SCREAMING_SNAKE_CASE       :     List[Any]				= max_resolution
			SCREAMING_SNAKE_CASE       :     List[Any]				= do_resize
			SCREAMING_SNAKE_CASE       :     List[Any]				= size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= do_normalize
			SCREAMING_SNAKE_CASE       :     Dict				= image_mean
			SCREAMING_SNAKE_CASE       :     List[str]				= image_std
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= do_rescale
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= rescale_factor
			SCREAMING_SNAKE_CASE       :     Optional[int]				= do_pad





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







			'''simple docstring'''




			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_pad": self.do_pad,
			}







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







			'''simple docstring'''




			if not batched:
					SCREAMING_SNAKE_CASE       :     List[Any]				= image_inputs[0]
					if isinstance(lowerCamelCase_		, Image.Image					):
							SCREAMING_SNAKE_CASE       :     Any				= image.size
					else:
							SCREAMING_SNAKE_CASE       :     List[str]				= image.shape[1], image.shape[2]
					if w < h:
							SCREAMING_SNAKE_CASE       :     str				= int(self.size['''shortest_edge'''] * h / w					)
							SCREAMING_SNAKE_CASE       :     Optional[int]				= self.size['''shortest_edge''']
					elif w > h:
							SCREAMING_SNAKE_CASE       :     Tuple				= self.size['''shortest_edge''']
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int(self.size['''shortest_edge'''] * w / h					)
					else:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.size['''shortest_edge''']
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.size['''shortest_edge''']

			else:
					SCREAMING_SNAKE_CASE       :     Dict				= []
					for image in image_inputs:
							SCREAMING_SNAKE_CASE       :     Tuple				= self.get_expected_values([image]					)
							expected_values.append((expected_height, expected_width)					)
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= max(lowerCamelCase_		, key=lambda lowerCamelCase_					: item[0]					)[0]
					SCREAMING_SNAKE_CASE       :     List[str]				= max(lowerCamelCase_		, key=lambda lowerCamelCase_					: item[1]					)[1]

			return expected_height, expected_width





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

	UpperCamelCase				=  ConditionalDetrImageProcessor if is_vision_available() else None





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= ConditionalDetrImageProcessingTester(self					)





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







			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''image_mean'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''image_std'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''do_normalize'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''do_resize'''					)					)
			self.assertTrue(hasattr(lowerCamelCase_		, '''size'''					)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size		, {'''shortest_edge''': 18, '''longest_edge''': 13_33}					)
			self.assertEqual(image_processor.do_pad		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= self.image_processing_class.from_dict(
			    self.image_processor_dict		, size=42		, max_size=84		, pad_and_return_pixel_mask=lowerCamelCase_					)
			self.assertEqual(image_processor.size		, {'''shortest_edge''': 42, '''longest_edge''': 84}					)
			self.assertEqual(image_processor.do_pad		, lowerCamelCase_					)





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







			'''simple docstring'''




			pass





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			SCREAMING_SNAKE_CASE       :     Optional[int]				= prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowerCamelCase_					)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, Image.Image					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     str				= image_processing(image_inputs[0]		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE       :     List[str]				= self.image_processor_tester.get_expected_values(lowerCamelCase_					)

			self.assertEqual(
			    encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

			# Test batched
			SCREAMING_SNAKE_CASE       :     Tuple				= self.image_processor_tester.get_expected_values(lowerCamelCase_		, batched=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= image_processing(lowerCamelCase_		, return_tensors='''pt'''					).pixel_values
			self.assertEqual(
			    encoded_images.shape		, (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )		, )





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowerCamelCase_		, numpify=lowerCamelCase_					)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, np.ndarray					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     List[str]				= image_processing(image_inputs[0]		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE       :     str				= self.image_processor_tester.get_expected_values(lowerCamelCase_					)

			self.assertEqual(
			    encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

			# Test batched
			SCREAMING_SNAKE_CASE       :     int				= image_processing(lowerCamelCase_		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE       :     Tuple				= self.image_processor_tester.get_expected_values(lowerCamelCase_		, batched=lowerCamelCase_					)

			self.assertEqual(
			    encoded_images.shape		, (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )		, )





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			SCREAMING_SNAKE_CASE       :     Dict				= prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowerCamelCase_		, torchify=lowerCamelCase_					)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_		, torch.Tensor					)

			# Test not batched input
			SCREAMING_SNAKE_CASE       :     str				= image_processing(image_inputs[0]		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.image_processor_tester.get_expected_values(lowerCamelCase_					)

			self.assertEqual(
			    encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

			# Test batched
			SCREAMING_SNAKE_CASE       :     str				= image_processing(lowerCamelCase_		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.image_processor_tester.get_expected_values(lowerCamelCase_		, batched=lowerCamelCase_					)

			self.assertEqual(
			    encoded_images.shape		, (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )		, )





	@slow
	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''					) as f:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= json.loads(f.read()					)

			SCREAMING_SNAKE_CASE       :     int				= {'''image_id''': 3_97_69, '''annotations''': target}

			# encode them
			SCREAMING_SNAKE_CASE       :     Any				= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''					)
			SCREAMING_SNAKE_CASE       :     List[str]				= image_processing(images=lowerCamelCase_		, annotations=lowerCamelCase_		, return_tensors='''pt'''					)

			# verify pixel values
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.Size([1, 3, 8_00, 10_66]					)
			self.assertEqual(encoding['''pixel_values'''].shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
			self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, lowerCamelCase_		, atol=1E-4					)					)

			# verify area
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, lowerCamelCase_					)					)
			# verify boxes
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.Size([6, 4]					)
			self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, lowerCamelCase_		, atol=1E-3					)					)
			# verify image_id
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([3_97_69]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, lowerCamelCase_					)					)
			# verify is_crowd
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([0, 0, 0, 0, 0, 0]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, lowerCamelCase_					)					)
			# verify class_labels
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([75, 75, 63, 65, 17, 17]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, lowerCamelCase_					)					)
			# verify orig_size
			SCREAMING_SNAKE_CASE       :     int				= torch.tensor([4_80, 6_40]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, lowerCamelCase_					)					)
			# verify size
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor([8_00, 10_66]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, lowerCamelCase_					)					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		, '''r'''					) as f:
					SCREAMING_SNAKE_CASE       :     List[Any]				= json.loads(f.read()					)

			SCREAMING_SNAKE_CASE       :     List[str]				= {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

			SCREAMING_SNAKE_CASE       :     List[str]				= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

			# encode them
			SCREAMING_SNAKE_CASE       :     int				= ConditionalDetrImageProcessor(format='''coco_panoptic'''					)
			SCREAMING_SNAKE_CASE       :     str				= image_processing(images=lowerCamelCase_		, annotations=lowerCamelCase_		, masks_path=lowerCamelCase_		, return_tensors='''pt'''					)

			# verify pixel values
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.Size([1, 3, 8_00, 10_66]					)
			self.assertEqual(encoding['''pixel_values'''].shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
			self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, lowerCamelCase_		, atol=1E-4					)					)

			# verify area
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, lowerCamelCase_					)					)
			# verify boxes
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.Size([6, 4]					)
			self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, lowerCamelCase_		, atol=1E-3					)					)
			# verify image_id
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([3_97_69]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, lowerCamelCase_					)					)
			# verify is_crowd
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([0, 0, 0, 0, 0, 0]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, lowerCamelCase_					)					)
			# verify class_labels
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([17, 17, 63, 75, 75, 93]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, lowerCamelCase_					)					)
			# verify masks
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 82_28_73
			self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, lowerCamelCase_					)
			# verify orig_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([4_80, 6_40]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, lowerCamelCase_					)					)
			# verify size
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor([8_00, 10_66]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, lowerCamelCase_					)					)







"""simple docstring"""






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


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)





