


"""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


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

transformers_logging.set_verbosity_info()




def 	UpperCAmelCase     (		A	:						List[str]       ):

		'''simple docstring'''


		if "token" in model_name_or_path:
				return "rag_token"
		if "sequence" in model_name_or_path:
				return "rag_sequence"
		if "bart" in model_name_or_path:
				return "bart"
		return None




def 	UpperCAmelCase     (		A	:						List[str]     ,				A	:						List[str]     ,				A	:						Any       ):

		'''simple docstring'''


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




def 	UpperCAmelCase     (		A	:						Tuple     ,				A	:						Optional[Any]     ,				A	:						Dict       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[line.strip() for line in open(A     ,				'r'       ).readlines()]
		_UpperCAmelCase							 =		[]

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

		_UpperCAmelCase							 =		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       )

		_UpperCAmelCase							 =		100.0 * em / total
		_UpperCAmelCase							 =		100.0 * fa / total

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




def 	UpperCAmelCase     (		A	:						int     ,				A	:						Tuple     ,				A	:						Tuple       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		args.k
		_UpperCAmelCase							 =		[line.strip() for line in open(A     ,				'r'       ).readlines()]
		_UpperCAmelCase							 =		[line.strip() for line in open(A     ,				'r'       ).readlines()]

		_UpperCAmelCase							 =		0
		for hypo, reference in zip(A     ,				A       ):
				_UpperCAmelCase							 =		set(hypo.split('\t'       )[:k]       )
				_UpperCAmelCase							 =		set(reference.split('\t'       )       )
				total += 1
				em += len(hypo_provenance & ref_provenance       ) / k

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




def 	UpperCAmelCase     (		A	:						int     ,				A	:						Tuple     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


		def strip_title(A	:						List[str]       ):
				if title.startswith('\"'       ):
						_UpperCAmelCase							 =		title[1:]
				if title.endswith('\"'       ):
						_UpperCAmelCase							 =		title[:-1]
				return title

		_UpperCAmelCase							 =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    A     ,				return_tensors='pt'     ,				padding=A     ,				truncation=A     ,				)["""input_ids"""].to(args.device       )

		_UpperCAmelCase							 =		rag_model.rag.question_encoder(A       )
		_UpperCAmelCase							 =		question_enc_outputs[0]

		_UpperCAmelCase							 =		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'     ,				)
		_UpperCAmelCase							 =		rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
		_UpperCAmelCase							 =		[]
		for docs in all_docs:
				_UpperCAmelCase							 =		[strip_title(A       ) for title in docs["""title"""]]
				provenance_strings.append('\t'.join(A       )       )
		return provenance_strings




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						List[str]     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		with torch.no_grad():
				_UpperCAmelCase							 =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    A     ,				return_tensors='pt'     ,				padding=A     ,				truncation=A       )

				_UpperCAmelCase							 =		inputs_dict.input_ids.to(args.device       )
				_UpperCAmelCase							 =		inputs_dict.attention_mask.to(args.device       )
				_UpperCAmelCase							 =		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]]     ,				)
				_UpperCAmelCase							 =		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 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		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.'     ,				)
		_UpperCAmelCase							 =		parser.parse_args()
		_UpperCAmelCase							 =		torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
		return args




def 	UpperCAmelCase     (		A	:						List[str]       ):

		'''simple docstring'''


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

		_UpperCAmelCase							 =		(
		    [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       )

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

						score_fn(A     ,				args.predictions_path     ,				args.gold_data_path       )


if __name__ == "__main__":
					lowercase  							=					get_args()
					main(args)







"""simple docstring"""





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.config_class(**self.inputs_dict						)
									self.parent.assertTrue(hasattr(snake_case	,       'embed_dim'						)						)
									self.parent.assertTrue(hasattr(snake_case	,       'num_heads'						)						)







class 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=13	,       snake_case=64	,       snake_case=3	,       snake_case=[16, 48, 96]	,       snake_case=[1, 3, 6]	,       snake_case=[1, 2, 10]	,       snake_case=[7, 3, 3]	,       snake_case=[4, 2, 2]	,       snake_case=[2, 1, 1]	,       snake_case=[2, 2, 2]	,       snake_case=[False, False, True]	,       snake_case=[0.0, 0.0, 0.0]	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=True	,       snake_case=True	,       snake_case=2	,       )			->							Tuple:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_sizes
									_UpperCAmelCase							 =		patch_stride
									_UpperCAmelCase							 =		patch_padding
									_UpperCAmelCase							 =		is_training
									_UpperCAmelCase							 =		use_labels
									_UpperCAmelCase							 =		num_labels
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		stride_kv
									_UpperCAmelCase							 =		depth
									_UpperCAmelCase							 =		cls_token
									_UpperCAmelCase							 =		attention_drop_rate
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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






							def        lowerCamelCase_   (  self						)			->							List[str]:
									return CvtConfig(
									    image_size=self.image_size	,       num_labels=self.num_labels	,       num_channels=self.num_channels	,       embed_dim=self.embed_dim	,       num_heads=self.num_heads	,       patch_sizes=self.patch_sizes	,       patch_padding=self.patch_padding	,       patch_stride=self.patch_stride	,       stride_kv=self.stride_kv	,       depth=self.depth	,       cls_token=self.cls_token	,       attention_drop_rate=self.attention_drop_rate	,       initializer_range=self.initializer_range	,       )






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		CvtModel(config=snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case						)
									_UpperCAmelCase							 =		(self.image_size, self.image_size)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		image_size[0], image_size[1]
									for i in range(len(self.depth						)						):
											_UpperCAmelCase							 =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
											_UpperCAmelCase							 =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
									self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.embed_dim[-1], height, width)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		CvtForImageClassification(snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case	,       labels=snake_case						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		config_and_inputs
									_UpperCAmelCase							 =		{'pixel_values': pixel_values}
									return config, inputs_dict







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







							_UpperCAmelCase      				=					(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      				=					(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		CvtModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=snake_case	,       has_text_modality=snake_case	,       hidden_size=37						)






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






							@unittest.skip(reason='Cvt does not output attentions'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='Cvt does not use inputs_embeds'						)
							def        lowerCamelCase_   (  self						)			->							int:
									pass






							@unittest.skip(reason='Cvt does not support input and output embeddings'						)
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									pass






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase							 =		['pixel_values']
											self.assertListEqual(arg_names[:1]	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									def check_hidden_states_output(snake_case	,       snake_case	,       snake_case						):
											_UpperCAmelCase							 =		model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

											with torch.no_grad():
													_UpperCAmelCase							 =		model(**self._prepare_for_class(snake_case	,       snake_case						)						)

											_UpperCAmelCase							 =		outputs.hidden_states

											_UpperCAmelCase							 =		len(self.model_tester.depth						)
											self.assertEqual(len(snake_case						)	,       snake_case						)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]						)	,       [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]	,       )

									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		True
											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)

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

											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case						)






							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def        lowerCamelCase_   (  self						)			->							Dict:
									pass






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase							 =		CvtModel.from_pretrained(snake_case						)
											self.assertIsNotNone(snake_case						)







def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
		return image






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







							@cached_property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)

									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(images=snake_case	,       return_tensors='pt'						).to(snake_case						)

									# forward pass
									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

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

									_UpperCAmelCase							 =		torch.tensor([0.9285, 0.9015, -0.3150]						).to(snake_case						)

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


