






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


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

transformers_logging.set_verbosity_info()

def        UpperCAmelCase__		(						lowerCamelCase_       :       int				):
			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__		(						lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Dict				):
			return max(metric_fn(lowerCamelCase_   ,  lowerCamelCase_				) for gt in ground_truths				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Union[str, Any]				):
			__a						:  Optional[Any]									=     [line.strip() for line in open(lowerCamelCase_   ,  'r'				).readlines()]
			__a						:  Any									=     []

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

			__a						:  Tuple									=     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_				)

			__a						:  List[Any]									=     100.0 * em / total
			__a						:  int									=     100.0 * fa / total

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       int				):
			__a						:  str									=     args.k
			__a						:  Optional[Any]									=     [line.strip() for line in open(lowerCamelCase_   ,  'r'				).readlines()]
			__a						:  Tuple									=     [line.strip() for line in open(lowerCamelCase_   ,  'r'				).readlines()]

			__a						:  Optional[Any]									=     0
			for hypo, reference in zip(lowerCamelCase_   ,  lowerCamelCase_				):
						__a						:  Dict									=     set(hypo.split('\t'				)[:k]				)
						__a						:  List[str]									=     set(reference.split('\t'				)				)
						total += 1
						em += len(hypo_provenance & ref_provenance				) / k

			__a						:  List[Any]									=     100.0 * em / total
			logger.info(f'''Precision@{k}: {em: .2f}'''				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       List[Any]				):
			def strip_title(lowerCamelCase_       :       Dict				):
						if title.startswith('"'				):
									__a						:  List[str]									=     title[1:]
						if title.endswith('"'				):
									__a						:  str									=     title[:-1]
						return title

			__a						:  List[str]									=     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    lowerCamelCase_   ,  return_tensors='pt'   ,  padding=lowerCamelCase_   ,  truncation=lowerCamelCase_   ,  )['input_ids'].to(args.device				)

			__a						:  Union[str, Any]									=     rag_model.rag.question_encoder(lowerCamelCase_				)
			__a						:  List[Any]									=     question_enc_outputs[0]

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Optional[int]				):
			with torch.no_grad():
						__a						:  int									=     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowerCamelCase_   ,  return_tensors='pt'   ,  padding=lowerCamelCase_   ,  truncation=lowerCamelCase_				)

						__a						:  int									=     inputs_dict.input_ids.to(args.device				)
						__a						:  Dict									=     inputs_dict.attention_mask.to(args.device				)
						__a						:  List[str]									=     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]]   ,  )
						__a						:  Optional[int]									=     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        UpperCAmelCase__		(						):
			__a						:  Union[str, Any]									=     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=5_0   ,  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.'   ,  )
			__a						:  Tuple									=     parser.parse_args()
			__a						:  List[Any]									=     torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
			return args

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]				):
			__a						:  Union[str, Any]									=     {}
			if args.model_type is None:
						__a						:  List[Any]									=     infer_model_type(args.model_name_or_path				)
						assert args.model_type is not None
			if args.model_type.startswith('rag'				):
						__a						:  Union[str, Any]									=     RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
						__a						:  List[Any]									=     args.n_docs
						if args.index_name is not None:
									__a						:  Union[str, Any]									=     args.index_name
						if args.index_path is not None:
									__a						:  Union[str, Any]									=     args.index_path
			else:
						__a						:  List[Any]									=     BartForConditionalGeneration

			__a						:  Optional[int]									=     (
			    [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_				)

			__a						:  Tuple									=     get_scores if args.eval_mode == 'e2e' else get_precision_at_k
			__a						:  int									=     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'				):
									__a						:  Union[str, Any]									=     RagRetriever.from_pretrained(lowerCamelCase_   ,  **lowerCamelCase_				)
									__a						:  List[Any]									=     model_class.from_pretrained(lowerCamelCase_   ,  retriever=lowerCamelCase_   ,  **lowerCamelCase_				)
									model.retriever.init_retrieval()
						else:
									__a						:  List[str]									=     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:
									__a						:  List[Any]									=     []
									for line in tqdm(lowerCamelCase_				):
												questions.append(line.strip()				)
												if len(lowerCamelCase_				) == args.eval_batch_size:
															__a						:  Tuple									=     evaluate_batch_fn(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
															preds_file.write('\n'.join(lowerCamelCase_				) + '\n'				)
															preds_file.flush()
															__a						:  Optional[int]									=     []
									if len(lowerCamelCase_				) > 0:
												__a						:  int									=     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__":
							SCREAMING_SNAKE_CASE__				     =			get_args()
							main(args)









from typing import Dict, List, Optional, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

								return rescale(snake_case_			,							scale=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								return normalize(snake_case_			,							mean=snake_case_			,							std=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

								if not valid_images(snake_case_    ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								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.'''    )

								# All transformations expect numpy arrays.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

								if do_rescale:
											__lowercase			       = [self.rescale(image=snake_case_			,							scale=snake_case_    ) for image in images]

								if do_normalize:
											__lowercase			       = [self.normalize(image=snake_case_			,							mean=snake_case_			,							std=snake_case_    ) for image in images]

								__lowercase			       = [to_channel_dimension_format(snake_case_			,							snake_case_    ) for image in images]

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


