
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							) ->    Dict:







	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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) ->    Optional[int]:
	return max(metric_fn(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) for gt in ground_truths							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) ->    Tuple:
	lowercase					  =   [line.strip() for line in open(__SCREAMING_SNAKE_CASE			,							'r'							).readlines()]
	lowercase					  =   []

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

	lowercase					  =   lowercase					  =   lowercase					  =   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							)

	lowercase					  =   1_00.0 * em / total
	lowercase					  =   1_00.0 * fa / total

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) ->    List[Any]:
	lowercase					  =   args.k
	lowercase					  =   [line.strip() for line in open(__SCREAMING_SNAKE_CASE			,							'r'							).readlines()]
	lowercase					  =   [line.strip() for line in open(__SCREAMING_SNAKE_CASE			,							'r'							).readlines()]

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

	lowercase					  =   1_00.0 * em / total
	logger.info(F'''Precision@{k}: {em: .2f}'''							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) ->    List[Any]:







	def strip_title(__SCREAMING_SNAKE_CASE							):
		if title.startswith('"'							):
			lowercase					  =   title[1:]
		if title.endswith('"'							):
			lowercase					  =   title[:-1]
		return title

	lowercase					  =   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							)

	lowercase					  =   rag_model.rag.question_encoder(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   question_enc_outputs[0]

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) ->    Dict:







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

		lowercase					  =   inputs_dict.input_ids.to(args.device							)
		lowercase					  =   inputs_dict.attention_mask.to(args.device							)
		lowercase					  =   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]]			,							)
		lowercase					  =   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 							UpperCAmelCase_		(							) ->    int:
	lowercase					  =   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.'			,							)
	lowercase					  =   parser.parse_args()
	lowercase					  =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
	return args






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							) ->    Any:
	lowercase					  =   {}
	if args.model_type is None:
		lowercase					  =   infer_model_type(args.model_name_or_path							)
		assert args.model_type is not None
	if args.model_type.startswith('rag'							):
		lowercase					  =   RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
		lowercase					  =   args.n_docs
		if args.index_name is not None:
			lowercase					  =   args.index_name
		if args.index_path is not None:
			lowercase					  =   args.index_path
	else:
		lowercase					  =   BartForConditionalGeneration

	lowercase					  =   (
	    [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							)

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



import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VideoMAEImageProcessor







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=7		,   snake_case=3		,   snake_case=10		,   snake_case=18		,   snake_case=30		,   snake_case=400		,   snake_case=True		,   snake_case=None		,   snake_case=True		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=None		,   ):
		lowercase					  =   size if size is not None else {'shortest_edge': 18}
		lowercase					  =   crop_size if crop_size is not None else {'height': 18, 'width': 18}

		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   num_channels
		lowercase					  =   num_frames
		lowercase					  =   image_size
		lowercase					  =   min_resolution
		lowercase					  =   max_resolution
		lowercase					  =   do_resize
		lowercase					  =   size
		lowercase					  =   do_normalize
		lowercase					  =   image_mean
		lowercase					  =   image_std
		lowercase					  =   crop_size





	def      SCREAMING_SNAKE_CASE__						(	self	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "crop_size": self.crop_size,
		}







@require_torch
@require_vision
class 	A_       (      __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	VideoMAEImageProcessor if is_vision_available() else None





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   VideoMAEImageProcessingTester(self	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.image_processor_tester.prepare_image_processor_dict()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(snake_case		,   'image_mean'	)	)
		self.assertTrue(hasattr(snake_case		,   'image_std'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_normalize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_resize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_center_crop'	)	)
		self.assertTrue(hasattr(snake_case		,   'size'	)	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class.from_dict(self.image_processor_dict	)
		self.assertEqual(image_processor.size		,   {'shortest_edge': 18}	)
		self.assertEqual(image_processor.crop_size		,   {'height': 18, 'width': 18}	)

		lowercase					  =   self.image_processing_class.from_dict(self.image_processor_dict		,   size=42		,   crop_size=84	)
		self.assertEqual(image_processor.size		,   {'shortest_edge': 42}	)
		self.assertEqual(image_processor.crop_size		,   {'height': 84, 'width': 84}	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PIL videos
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   Image.Image	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   numpify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   np.ndarray	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   torchify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   torch.Tensor	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

