




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

transformers_logging.set_verbosity_info()





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  [line.strip() for line in open(UpperCamelCase__						,			"""r"""   ).readlines()]
				__lowerCAmelCase					=  []

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

				__lowerCAmelCase					=  __lowerCAmelCase					=  __lowerCAmelCase					=  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__   )

				__lowerCAmelCase					=  100.0 * em / total
				__lowerCAmelCase					=  100.0 * fa / total

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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


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

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

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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     str:







				'''simple docstring'''


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

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

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

				__lowerCAmelCase					=  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"""						,			)
				__lowerCAmelCase					=  rag_model.retriever.index.get_doc_dicts(result.doc_ids   )
				__lowerCAmelCase					=  []
				for docs in all_docs:
								__lowerCAmelCase					=  [strip_title(UpperCamelCase__   ) for title in docs["""title"""]]
								provenance_strings.append("""\t""".join(UpperCamelCase__   )   )
				return provenance_strings





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     str:







				'''simple docstring'''


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

								__lowerCAmelCase					=  inputs_dict.input_ids.to(args.device   )
								__lowerCAmelCase					=  inputs_dict.attention_mask.to(args.device   )
								__lowerCAmelCase					=  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]]						,			)
								__lowerCAmelCase					=  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      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


				__lowerCAmelCase					=  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."""						,			)
				__lowerCAmelCase					=  parser.parse_args()
				__lowerCAmelCase					=  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )
				return args





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


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

				__lowerCAmelCase					=  (
				    [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__   )

				__lowerCAmelCase					=  get_scores if args.eval_mode == """e2e""" else get_precision_at_k
				__lowerCAmelCase					=  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"""   ):
												__lowerCAmelCase					=  RagRetriever.from_pretrained(UpperCamelCase__						,			**UpperCamelCase__   )
												__lowerCAmelCase					=  model_class.from_pretrained(UpperCamelCase__						,			retriever=UpperCamelCase__						,			**UpperCamelCase__   )
												model.retriever.init_retrieval()
								else:
												__lowerCAmelCase					=  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:
												__lowerCAmelCase					=  []
												for line in tqdm(UpperCamelCase__   ):
																questions.append(line.strip()   )
																if len(UpperCamelCase__   ) == args.eval_batch_size:
																				__lowerCAmelCase					=  evaluate_batch_fn(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
																				preds_file.write("""\n""".join(UpperCamelCase__   ) + """\n"""   )
																				preds_file.flush()
																				__lowerCAmelCase					=  []
												if len(UpperCamelCase__   ) > 0:
																__lowerCAmelCase					=  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__":
	__A						:		Union[str, Any]          =						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, 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						:		Dict          =						logging.get_logger(__name__)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["pixel_values"]




					def __init__(   self:		str,     _lowercase:		bool = True,     _lowercase:		Dict[str, int] = None,     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		bool = False,     _lowercase:		Dict[str, int] = None,     _lowercase:		Union[int, float] = 1 / 255,     _lowercase:		bool = False,     _lowercase:		bool = True,     _lowercase:		bool = True,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = True,     **_lowercase:		Union[str, Any],     ):


									'''simple docstring'''


									super().__init__(**_lowercase)
									__lowerCAmelCase					=  size if size is not None else {"""height""": 346, """width""": 346}
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  resample
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size
									__lowerCAmelCase					=  do_rescale
									__lowerCAmelCase					=  rescale_factor
									__lowerCAmelCase					=  rescale_offset
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowerCAmelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD
									__lowerCAmelCase					=  include_top




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return resize(
									    _lowercase,     size=(size["""height"""], size["""width"""]),     resample=_lowercase,     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		List[str],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return center_crop(_lowercase,     size=(size["""height"""], size["""width"""]),     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		np.ndarray,     _lowercase:		Union[int, float],     _lowercase:		bool = True,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									if offset:
													__lowerCAmelCase					=  (image - 127.5) * scale
													if data_format is not None:
																	__lowerCAmelCase					=  to_channel_dimension_format(_lowercase,     _lowercase)
													__lowerCAmelCase					=  rescaled_image.astype(np.floataa)
									else:
													__lowerCAmelCase					=  rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase)
									return rescaled_image




					def     _lowercase						(   self:		Dict,     _lowercase:		np.ndarray,     _lowercase:		Union[float, List[float]],     _lowercase:		Union[float, List[float]],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Any,     ):


									'''simple docstring'''


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







					def     _lowercase						(   self:		Dict,     _lowercase:		ImageInput,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		str=None,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		bool = None,     _lowercase:		float = None,     _lowercase:		bool = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[str, TensorType]] = None,     _lowercase:		ChannelDimension = ChannelDimension.FIRST,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  do_resize if do_resize is not None else self.do_resize
									__lowerCAmelCase					=  resample if resample is not None else self.resample
									__lowerCAmelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowerCAmelCase					=  do_rescale if do_rescale is not None else self.do_rescale
									__lowerCAmelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowerCAmelCase					=  rescale_offset if rescale_offset is not None else self.rescale_offset
									__lowerCAmelCase					=  do_normalize if do_normalize is not None else self.do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else self.image_mean
									__lowerCAmelCase					=  image_std if image_std is not None else self.image_std
									__lowerCAmelCase					=  include_top if include_top is not None else self.include_top

									__lowerCAmelCase					=  size if size is not None else self.size
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else self.crop_size
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  make_list_of_images(_lowercase)

									if not valid_images(_lowercase):
													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_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.""")

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

									if do_resize:
													__lowerCAmelCase					=  [self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase) for image in images]

									if do_center_crop:
													__lowerCAmelCase					=  [self.center_crop(image=_lowercase,     size=_lowercase) for image in images]

									if do_rescale:
													__lowerCAmelCase					=  [self.rescale(image=_lowercase,     scale=_lowercase,     offset=_lowercase) for image in images]

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

									if include_top:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=[0, 0, 0],     std=_lowercase) for image in images]

									__lowerCAmelCase					=  [to_channel_dimension_format(_lowercase,     _lowercase) for image in images]

									__lowerCAmelCase					=  {"""pixel_values""": images}
									return BatchFeature(data=_lowercase,     tensor_type=_lowercase)





