



'''simple docstring'''

import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
				from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=32     ,	A_=3     ,	A_=4     ,	A_=[10, 20, 30, 40]     ,	A_=[2, 2, 3, 2]     ,	A_=True     ,	A_=True     ,	A_=37     ,	A_="gelu"     ,	A_=10     ,	A_=0.0_2     ,	A_=["stage2", "stage3", "stage4"]     ,	A_=[2, 3, 4]     ,	A_=None     ,	)     ->     int:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     num_stages
							lowerCAmelCase   =     hidden_sizes
							lowerCAmelCase   =     depths
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     intermediate_size
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     num_labels
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     out_features
							lowerCAmelCase   =     out_indices
							lowerCAmelCase   =     scope


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.num_labels				)

							lowerCAmelCase   =     self.get_config()
							return config, pixel_values, labels


						def     __snake_case   ( self				)     ->     List[Any]:
							return ConvNextConfig(
							    num_channels=self.num_channels     ,	hidden_sizes=self.hidden_sizes     ,	depths=self.depths     ,	num_stages=self.num_stages     ,	hidden_act=self.hidden_act     ,	is_decoder=A_     ,	initializer_range=self.initializer_range     ,	out_features=self.out_features     ,	out_indices=self.out_indices     ,	num_labels=self.num_labels     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     ConvNextModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape     ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     ConvNextForImageClassification(A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Any:
							lowerCAmelCase   =     ConvNextBackbone(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps				)     ,	len(config.out_features				)				)
							self.parent.assertListEqual(list(result.feature_maps[0].shape				)     ,	[self.batch_size, self.hidden_sizes[1], 4, 4]				)

							# verify channels
							self.parent.assertEqual(len(model.channels				)     ,	len(config.out_features				)				)
							self.parent.assertListEqual(model.channels     ,	config.hidden_sizes[1:]				)

							# verify backbone works with out_features=None
							lowerCAmelCase   =     None
							lowerCAmelCase   =     ConvNextBackbone(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps				)     ,	1				)
							self.parent.assertListEqual(list(result.feature_maps[0].shape				)     ,	[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

							# verify channels
							self.parent.assertEqual(len(model.channels				)     ,	1				)
							self.parent.assertListEqual(model.channels     ,	[config.hidden_sizes[-1]]				)




						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							lowerCAmelCase   =     config_and_inputs
							lowerCAmelCase   =     {"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class __snake_case(				__UpperCamelCase		,							__UpperCamelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		(
						    (
						        ConvNextModel,
						        ConvNextForImageClassification,
						        ConvNextBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase							:   Any									=		(
						    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase							:   Optional[Any]									=		True
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Dict									=		False
						UpperCAmelCase							:   List[Any]									=		False
						UpperCAmelCase							:   Dict									=		False


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     ConvNextModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	has_text_modality=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     int:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


						def     __snake_case   ( self				)     ->     Any:
							return


						@unittest.skip(reason="""ConvNext does not use inputs_embeds"""				)
						def     __snake_case   ( self				)     ->     Optional[Any]:
							pass


						@unittest.skip(reason="""ConvNext does not support input and output embeddings"""				)
						def     __snake_case   ( self				)     ->     List[str]:
							pass


						@unittest.skip(reason="""ConvNext does not use feedforward chunking"""				)
						def     __snake_case   ( self				)     ->     Optional[Any]:
							pass


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     inspect.signature(model.forward				)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								lowerCAmelCase   =     [*signature.parameters.keys()]

								lowerCAmelCase   =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]     ,	A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*A_				)


						def     __snake_case   ( self				)     ->     Dict:
							def check_hidden_states_output(A_     ,	A_     ,	A_				):
								lowerCAmelCase   =     model_class(A_				)
								model.to(A_				)
								model.eval()

								with torch.no_grad():
									lowerCAmelCase   =     model(**self._prepare_for_class(A_     ,	A_				)				)

								lowerCAmelCase   =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								lowerCAmelCase   =     self.model_tester.num_stages
								self.assertEqual(len(A_				)     ,	expected_num_stages + 1				)

								# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]				)     ,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,	)

							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								check_hidden_states_output(A_     ,	A_     ,	A_				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase   =     True

								check_hidden_states_output(A_     ,	A_     ,	A_				)


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_				)




						@slow
						def     __snake_case   ( self				)     ->     str:
							for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     ConvNextModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
	return image

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@cached_property
						def     __snake_case   ( self				)     ->     Any:
							return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""				) if is_vision_available() else None




						@slow
						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""				).to(A_				)

							lowerCAmelCase   =     self.default_image_processor
							lowerCAmelCase   =     prepare_img()
							lowerCAmelCase   =     image_processor(images=A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)

							# verify the logits
							lowerCAmelCase   =     torch.Size((1, 1000)				)
							self.assertEqual(outputs.logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]				).to(A_				)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,	A_     ,	atol=1e-4				)				)







@require_torch
class __snake_case(				unittest.TestCase		,							__UpperCamelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Any									=		(ConvNextBackbone,) if is_torch_available() else ()
						UpperCAmelCase							:   Any									=		ConvNextConfig

						UpperCAmelCase							:   Union[str, Any]									=		False


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     ConvNextModelTester(self				)




'''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


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

transformers_logging.set_verbosity_info()






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[Any]:







	"""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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Dict:







	"""simple docstring"""
	return max(metric_fn(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) for gt in ground_truths							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     []

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

	lowerCAmelCase   =     lowerCAmelCase   =     lowerCAmelCase   =     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							)

	lowerCAmelCase   =     100.0 * em / total
	lowerCAmelCase   =     100.0 * fa / total

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     args.k
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]
	lowerCAmelCase   =     [line.strip() for line in open(_SCREAMING_SNAKE_CASE							,		"""r"""							).readlines()]

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

	lowerCAmelCase   =     100.0 * em / total
	logger.info(f'Precision@{k}: {em: .2f}'							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Any:







	"""simple docstring"""
	def strip_title(_SCREAMING_SNAKE_CASE					:						Union[str, Any]							):
		if title.startswith("""\""""							):
			lowerCAmelCase   =     title[1:]
		if title.endswith("""\""""							):
			lowerCAmelCase   =     title[:-1]
		return title

	lowerCAmelCase   =     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							)

	lowerCAmelCase   =     rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     question_enc_outputs[0]

	lowerCAmelCase   =     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"""							,		)
	lowerCAmelCase   =     rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
	lowerCAmelCase   =     []
	for docs in all_docs:
		lowerCAmelCase   =     [strip_title(_SCREAMING_SNAKE_CASE							) for title in docs["""title"""]]
		provenance_strings.append("""\t""".join(_SCREAMING_SNAKE_CASE							)							)
	return provenance_strings






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						Tuple:







	"""simple docstring"""
	with torch.no_grad():
		lowerCAmelCase   =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    _SCREAMING_SNAKE_CASE							,		return_tensors="""pt"""							,		padding=_SCREAMING_SNAKE_CASE							,		truncation=_SCREAMING_SNAKE_CASE							)

		lowerCAmelCase   =     inputs_dict.input_ids.to(args.device							)
		lowerCAmelCase   =     inputs_dict.attention_mask.to(args.device							)
		lowerCAmelCase   =     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]]							,		)
		lowerCAmelCase   =     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 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     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."""							,		)
	lowerCAmelCase   =     parser.parse_args()
	lowerCAmelCase   =     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)
	return args






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     {}
	if args.model_type is None:
		lowerCAmelCase   =     infer_model_type(args.model_name_or_path							)
		assert args.model_type is not None
	if args.model_type.startswith("""rag"""							):
		lowerCAmelCase   =     RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
		lowerCAmelCase   =     args.n_docs
		if args.index_name is not None:
			lowerCAmelCase   =     args.index_name
		if args.index_path is not None:
			lowerCAmelCase   =     args.index_path
	else:
		lowerCAmelCase   =     BartForConditionalGeneration

	lowerCAmelCase   =     (
	    [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							)

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