





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 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]						)			-> Union[str, 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 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       List[str]						)			-> Union[str, Any]:



					'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       Union[str, Any]						)			-> str:



					'''simple docstring'''




					_snake_case									=				[line.strip() for line in open(UpperCamelCase__       ,    'r'						).readlines()]
					_snake_case									=				[]

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

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

					_snake_case									=				100.0 * em / total
					_snake_case									=				100.0 * fa / total

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       str						)			-> List[Any]:



					'''simple docstring'''




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

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

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       Optional[Any]						)			-> List[Any]:



					'''simple docstring'''




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

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

					_snake_case									=				rag_model.rag.question_encoder(UpperCamelCase__						)
					_snake_case									=				question_enc_outputs[0]

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Dict:



					'''simple docstring'''




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

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

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

										return answers





def 						lowerCamelCase__     (     )			-> Optional[int]:



					'''simple docstring'''




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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict						)			-> Optional[Any]:



					'''simple docstring'''




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

					_snake_case									=				(
					    [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'       ,    UpperCamelCase__						)

					_snake_case									=				get_scores if args.eval_mode == 'e2e' else get_precision_at_k
					_snake_case									=				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(UpperCamelCase__       ,    args.predictions_path       ,    args.gold_data_path						)
															continue

										logger.info('***** Running evaluation for {} *****'.format(UpperCamelCase__						)						)
										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									=				RagRetriever.from_pretrained(UpperCamelCase__       ,    **UpperCamelCase__						)
															_snake_case									=				model_class.from_pretrained(UpperCamelCase__       ,    retriever=UpperCamelCase__       ,    **UpperCamelCase__						)
															model.retriever.init_retrieval()
										else:
															_snake_case									=				model_class.from_pretrained(UpperCamelCase__       ,    **UpperCamelCase__						)
										model.to(args.device						)

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

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


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










from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
					import PIL

UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple						)			-> List[List[ImageInput]]:



					'''simple docstring'''




					if isinstance(UpperCamelCase__       ,    (list, tuple)						) and isinstance(videos[0]       ,    (list, tuple)						) and is_valid_image(videos[0][0]						):
										return videos

					elif isinstance(UpperCamelCase__       ,    (list, tuple)						) and is_valid_image(videos[0]						):
										return [videos]

					elif is_valid_image(UpperCamelCase__						):
										return [[videos]]

					raise ValueError(F'''Could not make batched video from {videos}'''						)




class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''pixel_values''']




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = 1 / 255				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							None:
										super().__init__(**lowerCAmelCase_  )
										_snake_case									=				size if size is not None else {'shortest_edge': 256}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

										_snake_case									=				do_resize
										_snake_case									=				size
										_snake_case									=				do_center_crop
										_snake_case									=				crop_size
										_snake_case									=				resample
										_snake_case									=				do_rescale
										_snake_case									=				rescale_factor
										_snake_case									=				offset
										_snake_case									=				do_normalize
										_snake_case									=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_snake_case									=				image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										if "shortest_edge" in size:
															_snake_case									=				get_resize_output_image_size(lowerCAmelCase_				,						size['shortest_edge']				,						default_to_square=lowerCAmelCase_  )
										elif "height" in size and "width" in size:
															_snake_case									=				(size['height'], size['width'])
										else:
															raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
										return resize(lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_  )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
										return center_crop(lowerCAmelCase_				,						size=(size['height'], size['width'])				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							int:
										_snake_case									=				image.astype(np.floataa  )
										if offset:
															_snake_case									=				image - (scale / 2)
										return rescale(lowerCAmelCase_				,						scale=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										return normalize(lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						)		->							np.ndarray:
										if do_resize and size is None or resample is None:
															raise ValueError('Size and resample must be specified if do_resize is True.'  )

										if do_center_crop and crop_size is None:
															raise ValueError('Crop size must be specified if do_center_crop is True.'  )

										if do_rescale and rescale_factor is None:
															raise ValueError('Rescale factor must be specified if do_rescale is True.'  )

										if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

										if offset and not do_rescale:
															raise ValueError('For offset, do_rescale must also be set to True.'  )

										# All transformations expect numpy arrays.
										_snake_case									=				to_numpy_array(lowerCAmelCase_  )

										if do_resize:
															_snake_case									=				self.resize(image=lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_  )

										if do_center_crop:
															_snake_case									=				self.center_crop(lowerCAmelCase_				,						size=lowerCAmelCase_  )

										if do_rescale:
															_snake_case									=				self.rescale(image=lowerCAmelCase_				,						scale=lowerCAmelCase_				,						offset=lowerCAmelCase_  )

										if do_normalize:
															_snake_case									=				self.normalize(image=lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_  )

										_snake_case									=				to_channel_dimension_format(lowerCAmelCase_				,						lowerCAmelCase_  )
										return image






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							PIL.Image.Image:
										_snake_case									=				do_resize if do_resize is not None else self.do_resize
										_snake_case									=				resample if resample is not None else self.resample
										_snake_case									=				do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case									=				do_rescale if do_rescale is not None else self.do_rescale
										_snake_case									=				rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case									=				offset if offset is not None else self.offset
										_snake_case									=				do_normalize if do_normalize is not None else self.do_normalize
										_snake_case									=				image_mean if image_mean is not None else self.image_mean
										_snake_case									=				image_std if image_std is not None else self.image_std

										_snake_case									=				size if size is not None else self.size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else self.crop_size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

										if not valid_images(lowerCAmelCase_  ):
															raise ValueError(
															    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
															    'torch.Tensor, tf.Tensor or jax.ndarray.'  )

										_snake_case									=				make_batched(lowerCAmelCase_  )

										_snake_case									=				[
										    [
										        self._preprocess_image(
										            image=lowerCAmelCase_				,						do_resize=lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						do_center_crop=lowerCAmelCase_				,						crop_size=lowerCAmelCase_				,						do_rescale=lowerCAmelCase_				,						rescale_factor=lowerCAmelCase_				,						offset=lowerCAmelCase_				,						do_normalize=lowerCAmelCase_				,						image_mean=lowerCAmelCase_				,						image_std=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						)
										        for img in video
										    ]
										    for video in videos
										]

										_snake_case									=				{'pixel_values': videos}
										return BatchFeature(data=lowerCAmelCase_				,						tensor_type=lowerCAmelCase_  )



