
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


_snake_case			:       List[str]      =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def    __snake_case       (  __magic_name__	):







    '''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       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







    '''simple docstring'''


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

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







    '''simple docstring'''


    lowercase						    =			[line.strip() for line in open(lowerCAmelCase__		,						"r"	).readlines()]
    lowercase						    =			[]

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

    lowercase						    =			0
    for prediction, ground_truths in zip(lowerCAmelCase__		,						lowerCAmelCase__	):
        total += 1
        em += metric_max_over_ground_truths(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__	)
        fa += metric_max_over_ground_truths(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__	)

    lowercase						    =			1_0_0.0 * em / total
    lowercase						    =			1_0_0.0 * fa / total

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

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







    '''simple docstring'''


    lowercase						    =			args.k
    lowercase						    =			[line.strip() for line in open(lowerCAmelCase__		,						"r"	).readlines()]
    lowercase						    =			[line.strip() for line in open(lowerCAmelCase__		,						"r"	).readlines()]

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

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

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







    '''simple docstring'''


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

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

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

    lowercase						    =			rag_model.retriever(
        lowerCAmelCase__		,						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(lowerCAmelCase__	) for title in docs['title']]
        provenance_strings.append("\t".join(lowerCAmelCase__	)	)
    return provenance_strings

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







    '''simple docstring'''


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

        lowercase						    =			inputs_dict.input_ids.to(args.device	)
        lowercase						    =			inputs_dict.attention_mask.to(args.device	)
        lowercase						    =			rag_model.generate(  # rag_model overwrites generate
            lowerCAmelCase__		,						attention_mask=lowerCAmelCase__		,						num_beams=args.num_beams		,						min_length=args.min_length		,						max_length=args.max_length		,						early_stopping=lowerCAmelCase__		,						num_return_sequences=1		,						bad_words_ids=[[0, 0]]		,						)
        lowercase						    =			rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase__		,						skip_special_tokens=lowerCAmelCase__	)

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

        return answers

def    __snake_case       (  ):







    '''simple docstring'''


    lowercase						    =			argparse.ArgumentParser()
    parser.add_argument(
        "--model_type"		,						choices=["rag_sequence", "rag_token", "bart"]		,						type=lowerCAmelCase__		,						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=lowerCAmelCase__		,						choices=["exact", "compressed", "legacy"]		,						type=lowerCAmelCase__		,						help="RAG model retriever type"		,						)
    parser.add_argument(
        "--index_path"		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						help="Path to the retrieval index"		,						)
    parser.add_argument("--n_docs"		,						default=5		,						type=lowerCAmelCase__		,						help="Number of retrieved docs"	)
    parser.add_argument(
        "--model_name_or_path"		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,						)
    parser.add_argument(
        "--eval_mode"		,						choices=["e2e", "retrieval"]		,						default="e2e"		,						type=lowerCAmelCase__		,						help=(
            "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
            " precision@k."
        )		,						)
    parser.add_argument("--k"		,						default=1		,						type=lowerCAmelCase__		,						help="k for the precision@k calculation"	)
    parser.add_argument(
        "--evaluation_set"		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="Path to a file containing evaluation samples"		,						)
    parser.add_argument(
        "--gold_data_path"		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="Path to a tab-separated file with gold samples"		,						)
    parser.add_argument(
        "--gold_data_mode"		,						default="qa"		,						type=lowerCAmelCase__		,						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=lowerCAmelCase__		,						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=lowerCAmelCase__		,						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=lowerCAmelCase__		,						help="Number of beams to be used when generating answers"		,						)
    parser.add_argument("--min_length"		,						default=1		,						type=lowerCAmelCase__		,						help="Min length of the generated answers"	)
    parser.add_argument("--max_length"		,						default=50		,						type=lowerCAmelCase__		,						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    __snake_case       (  __magic_name__	):







    '''simple docstring'''


    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"		,						lowerCAmelCase__	)

    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(lowerCAmelCase__		,						args.predictions_path		,						args.gold_data_path	)
            continue

        logger.info("***** Running evaluation for {} *****".format(lowerCAmelCase__	)	)
        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(lowerCAmelCase__		,						**lowerCAmelCase__	)
            lowercase						    =			model_class.from_pretrained(lowerCAmelCase__		,						retriever=lowerCAmelCase__		,						**lowerCAmelCase__	)
            model.retriever.init_retrieval()
        else:
            lowercase						    =			model_class.from_pretrained(lowerCAmelCase__		,						**lowerCAmelCase__	)
        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(lowerCAmelCase__	):
                questions.append(line.strip()	)
                if len(lowerCAmelCase__	) == args.eval_batch_size:
                    lowercase						    =			evaluate_batch_fn(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__	)
                    preds_file.write("\n".join(lowerCAmelCase__	) + "\n"	)
                    preds_file.flush()
                    lowercase						    =			[]
            if len(lowerCAmelCase__	) > 0:
                lowercase						    =			evaluate_batch_fn(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__	)
                preds_file.write("\n".join(lowerCAmelCase__	)	)
                preds_file.flush()

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


if __name__ == "__main__":
      _snake_case			:       Any      =						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      UpperCamelCase_			(			unittest.TestCase		):







						'''simple docstring'''



						def __init__( self       :Any		,						lowerCAmelCase__       :Any		,						lowerCAmelCase__       :Optional[int]=7		,						lowerCAmelCase__       :Tuple=3		,						lowerCAmelCase__       :List[str]=10		,						lowerCAmelCase__       :Dict=18		,						lowerCAmelCase__       :int=30		,						lowerCAmelCase__       :List[str]=400		,						lowerCAmelCase__       :Union[str, Any]=True		,						lowerCAmelCase__       :List[Any]=None		,						lowerCAmelCase__       :List[Any]=True		,						lowerCAmelCase__       :int=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Optional[Any]=[0.5, 0.5, 0.5]		,						lowerCAmelCase__       :Dict=None		,						)     ->Union[str, Any]:
										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       :List[Any]    )     ->int:
										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      UpperCamelCase_			(			__a		,						unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				VideoMAEImageProcessor if is_vision_available() else None





						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->List[Any]:
										lowercase						    =			VideoMAEImageProcessingTester(self    )





						@property
						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->Any:
										return self.image_processor_tester.prepare_image_processor_dict()





						def 					SCREAMING_SNAKE_CASE( self       :Dict    )     ->str:
										lowercase						    =			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"image_mean"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"image_std"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_normalize"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_resize"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"do_center_crop"    )    )
										self.assertTrue(hasattr(lowerCAmelCase__		,						"size"    )    )





						def 					SCREAMING_SNAKE_CASE( self       :str    )     ->Tuple:
										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       :int    )     ->Any:
										pass





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]    )     ->List[Any]:
										# 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=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														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(lowerCAmelCase__		,						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       :Dict    )     ->Optional[int]:
										# 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=lowerCAmelCase__		,						numpify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														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(lowerCAmelCase__		,						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       :Any    )     ->Dict:
										# 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=lowerCAmelCase__		,						torchify=lowerCAmelCase__    )
										for video in video_inputs:
														self.assertIsInstance(lowerCAmelCase__		,						lowerCAmelCase__    )
														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(lowerCAmelCase__		,						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"],
										    )		,						)
