





'''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


A :			Tuple							   =      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def    lowercase_   (		lowercase__    ) ->List[str]:
						if "token" in model_name_or_path:
												return "rag_token"
						if "sequence" in model_name_or_path:
												return "rag_sequence"
						if "bart" in model_name_or_path:
												return "bart"
						return None





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						return max(metric_fn(lowercase__					,	lowercase__    ) for gt in ground_truths    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: str	       =		[line.strip() for line in open(lowercase__					,	'r'    ).readlines()]
						_snake_case: Tuple	       =		[]

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

						_snake_case: Optional[Any]	       =		0
						for prediction, ground_truths in zip(lowercase__					,	lowercase__    ):
												total += 1
												em += metric_max_over_ground_truths(lowercase__					,	lowercase__					,	lowercase__    )
												fa += metric_max_over_ground_truths(lowercase__					,	lowercase__					,	lowercase__    )

						_snake_case: Any	       =		1_0_0.0 * em / total
						_snake_case: List[str]	       =		1_0_0.0 * fa / total

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





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[Any]	       =		args.k
						_snake_case: Optional[Any]	       =		[line.strip() for line in open(lowercase__					,	'r'    ).readlines()]
						_snake_case: Optional[Any]	       =		[line.strip() for line in open(lowercase__					,	'r'    ).readlines()]

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

						_snake_case: str	       =		1_0_0.0 * em / total
						logger.info(F'''Precision@{k}: {em: .2f}'''    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						def strip_title(lowercase__    ):
												if title.startswith('"'    ):
																		_snake_case: Optional[Any]	       =		title[1:]
												if title.endswith('"'    ):
																		_snake_case: Tuple	       =		title[:-1]
												return title

						_snake_case: Optional[int]	       =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowercase__					,	return_tensors='pt'					,	padding=lowercase__					,	truncation=lowercase__					,	)['input_ids'].to(args.device    )

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

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





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[Any]:
						with torch.no_grad():
												_snake_case: str	       =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    lowercase__					,	return_tensors='pt'					,	padding=lowercase__					,	truncation=lowercase__    )

												_snake_case: Any	       =		inputs_dict.input_ids.to(args.device    )
												_snake_case: Union[str, Any]	       =		inputs_dict.attention_mask.to(args.device    )
												_snake_case: Optional[int]	       =		rag_model.generate(  # rag_model overwrites generate
												    lowercase__					,	attention_mask=lowercase__					,	num_beams=args.num_beams					,	min_length=args.min_length					,	max_length=args.max_length					,	early_stopping=lowercase__					,	num_return_sequences=1					,	bad_words_ids=[[0, 0]]					,	)
												_snake_case: str	       =		rag_model.retriever.generator_tokenizer.batch_decode(lowercase__					,	skip_special_tokens=lowercase__    )

												if args.print_predictions:
																		for q, a in zip(lowercase__					,	lowercase__    ):
																								logger.info('Q: {} - A: {}'.format(lowercase__					,	lowercase__    )    )

												return answers





def    lowercase_   (		) ->str:
						_snake_case: str	       =		argparse.ArgumentParser()
						parser.add_argument(
						    '--model_type'					,	choices=['rag_sequence', 'rag_token', 'bart']					,	type=lowercase__					,	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=lowercase__					,	choices=['exact', 'compressed', 'legacy']					,	type=lowercase__					,	help='RAG model retriever type'					,	)
						parser.add_argument(
						    '--index_path'					,	default=lowercase__					,	type=lowercase__					,	help='Path to the retrieval index'					,	)
						parser.add_argument('--n_docs'					,	default=5					,	type=lowercase__					,	help='Number of retrieved docs'    )
						parser.add_argument(
						    '--model_name_or_path'					,	default=lowercase__					,	type=lowercase__					,	required=lowercase__					,	help='Path to pretrained checkpoints or model identifier from huggingface.co/models'					,	)
						parser.add_argument(
						    '--eval_mode'					,	choices=['e2e', 'retrieval']					,	default='e2e'					,	type=lowercase__					,	help=(
						        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
						        ' precision@k.'
						    )					,	)
						parser.add_argument('--k'					,	default=1					,	type=lowercase__					,	help='k for the precision@k calculation'    )
						parser.add_argument(
						    '--evaluation_set'					,	default=lowercase__					,	type=lowercase__					,	required=lowercase__					,	help='Path to a file containing evaluation samples'					,	)
						parser.add_argument(
						    '--gold_data_path'					,	default=lowercase__					,	type=lowercase__					,	required=lowercase__					,	help='Path to a tab-separated file with gold samples'					,	)
						parser.add_argument(
						    '--gold_data_mode'					,	default='qa'					,	type=lowercase__					,	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=lowercase__					,	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=lowercase__					,	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=lowercase__					,	help='Number of beams to be used when generating answers'					,	)
						parser.add_argument('--min_length'					,	default=1					,	type=lowercase__					,	help='Min length of the generated answers'    )
						parser.add_argument('--max_length'					,	default=50					,	type=lowercase__					,	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.'					,	)
						_snake_case: str	       =		parser.parse_args()
						_snake_case: Optional[Any]	       =		torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
						return args





def    lowercase_   (		lowercase__    ) ->List[Any]:
						_snake_case: Any	       =		{}
						if args.model_type is None:
												_snake_case: Tuple	       =		infer_model_type(args.model_name_or_path    )
												assert args.model_type is not None
						if args.model_type.startswith('rag'    ):
												_snake_case: List[Any]	       =		RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
												_snake_case: int	       =		args.n_docs
												if args.index_name is not None:
																		_snake_case: Union[str, Any]	       =		args.index_name
												if args.index_path is not None:
																		_snake_case: List[str]	       =		args.index_path
						else:
												_snake_case: Dict	       =		BartForConditionalGeneration

						_snake_case: List[Any]	       =		(
						    [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'					,	lowercase__    )

						_snake_case: Dict	       =		get_scores if args.eval_mode == 'e2e' else get_precision_at_k
						_snake_case: List[Any]	       =		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(lowercase__					,	args.predictions_path					,	args.gold_data_path    )
																		continue

												logger.info('***** Running evaluation for {} *****'.format(lowercase__    )    )
												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'    ):
																		_snake_case: List[Any]	       =		RagRetriever.from_pretrained(lowercase__					,	**lowercase__    )
																		_snake_case: Tuple	       =		model_class.from_pretrained(lowercase__					,	retriever=lowercase__					,	**lowercase__    )
																		model.retriever.init_retrieval()
												else:
																		_snake_case: str	       =		model_class.from_pretrained(lowercase__					,	**lowercase__    )
												model.to(args.device    )

												with open(args.evaluation_set					,	'r'    ) as eval_file, open(args.predictions_path					,	'w'    ) as preds_file:
																		_snake_case: Any	       =		[]
																		for line in tqdm(lowercase__    ):
																								questions.append(line.strip()    )
																								if len(lowercase__    ) == args.eval_batch_size:
																														_snake_case: Optional[int]	       =		evaluate_batch_fn(lowercase__					,	lowercase__					,	lowercase__    )
																														preds_file.write('\n'.join(lowercase__    ) + '\n'    )
																														preds_file.flush()
																														_snake_case: List[str]	       =		[]
																		if len(lowercase__    ) > 0:
																								_snake_case: List[Any]	       =		evaluate_batch_fn(lowercase__					,	lowercase__					,	lowercase__    )
																								preds_file.write('\n'.join(lowercase__    )    )
																								preds_file.flush()

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


if __name__ == "__main__":
		A :			Optional[int]							   =      get_args()
		main(args)












'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





