

"""simple docstring"""




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	:       Dict        =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return max(metric_fn(lowercase__     ,				lowercase__ ) for gt in ground_truths )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]
					A     =   []

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

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

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

					logger.info(F"""F1: {fa:.2f}""" )
					logger.info(F"""EM: {em:.2f}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   args.k
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]
					A     =   [line.strip() for line in open(lowercase__     ,				"r" ).readlines()]

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

					A     =   1_00.0 * em / total
					logger.info(F"""Precision@{k}: {em: .2f}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					def strip_title(lowercase__ ):
										if title.startswith("\"" ):
															A     =   title[1:]
										if title.endswith("\"" ):
															A     =   title[:-1]
										return title

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

					A     =   rag_model.rag.question_encoder(lowercase__ )
					A     =   question_enc_outputs[0]

					A     =   rag_model.retriever(
					    lowercase__     ,				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     =   rag_model.retriever.index.get_doc_dicts(result.doc_ids )
					A     =   []
					for docs in all_docs:
										A     =   [strip_title(lowercase__ ) for title in docs["title"]]
										provenance_strings.append("\t".join(lowercase__ ) )
					return provenance_strings
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					with torch.no_grad():
										A     =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    lowercase__     ,				return_tensors="pt"     ,				padding=lowercase__     ,				truncation=lowercase__ )

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

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

										return answers
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser()
					parser.add_argument(
					    "--model_type"     ,				choices=["rag_sequence", "rag_token", "bart"]     ,				type=lowercase__     ,				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=lowercase__     ,				choices=["exact", "compressed", "legacy"]     ,				type=lowercase__     ,				help="RAG model retriever type"     ,				)
					parser.add_argument(
					    "--index_path"     ,				default=lowercase__     ,				type=lowercase__     ,				help="Path to the retrieval index"     ,				)
					parser.add_argument("--n_docs"     ,				default=5     ,				type=lowercase__     ,				help="Number of retrieved docs" )
					parser.add_argument(
					    "--model_name_or_path"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to pretrained checkpoints or model identifier from huggingface.co/models"     ,				)
					parser.add_argument(
					    "--eval_mode"     ,				choices=["e2e", "retrieval"]     ,				default="e2e"     ,				type=lowercase__     ,				help=(
					        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
					        " precision@k."
					    )     ,				)
					parser.add_argument("--k"     ,				default=1     ,				type=lowercase__     ,				help="k for the precision@k calculation" )
					parser.add_argument(
					    "--evaluation_set"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to a file containing evaluation samples"     ,				)
					parser.add_argument(
					    "--gold_data_path"     ,				default=lowercase__     ,				type=lowercase__     ,				required=lowercase__     ,				help="Path to a tab-separated file with gold samples"     ,				)
					parser.add_argument(
					    "--gold_data_mode"     ,				default="qa"     ,				type=lowercase__     ,				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=lowercase__     ,				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=lowercase__     ,				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=lowercase__     ,				help="Number of beams to be used when generating answers"     ,				)
					parser.add_argument("--min_length"     ,				default=1     ,				type=lowercase__     ,				help="Min length of the generated answers" )
					parser.add_argument("--max_length"     ,				default=50     ,				type=lowercase__     ,				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     =   parser.parse_args()
					A     =   torch.device("cuda" if torch.cuda.is_available() else "cpu" )
					return args
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   {}
					if args.model_type is None:
										A     =   infer_model_type(args.model_name_or_path )
										assert args.model_type is not None
					if args.model_type.startswith("rag" ):
										A     =   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
										A     =   args.n_docs
										if args.index_name is not None:
															A     =   args.index_name
										if args.index_path is not None:
															A     =   args.index_path
					else:
										A     =   BartForConditionalGeneration

					A     =   (
					    [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"     ,				lowercase__ )

					A     =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
					A     =   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(lowercase__     ,				args.predictions_path     ,				args.gold_data_path )
															continue

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

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

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


if __name__ == "__main__":
	__A	:       Any        =						get_args()
	main(args)










"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_lowercase					:					str 		=		logging.get_logger(__name__)

_lowercase					:					List[str] 		=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase					:					Tuple 		=		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase					:					str 		=		{
    "allenai/longformer-base-4096": 4_0_9_6,
    "allenai/longformer-large-4096": 4_0_9_6,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				VOCAB_FILES_NAMES
					_a       =				PRETRAINED_VOCAB_FILES_MAP
					_a       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a       =				['input_ids', 'attention_mask']
					_a       =				LongformerTokenizer



					def __init__(			self				:    Dict,    lowerCamelCase				:    Any=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    Optional[Any]="replace",    lowerCamelCase				:    List[str]="<s>",    lowerCamelCase				:    Tuple="</s>",    lowerCamelCase				:    Union[str, Any]="</s>",    lowerCamelCase				:    str="<s>",    lowerCamelCase				:    Union[str, Any]="<unk>",    lowerCamelCase				:    Optional[Any]="<pad>",    lowerCamelCase				:    Optional[Any]="<mask>",    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[Any]=True,    **lowerCamelCase				:    Union[str, Any],    )->   List[str]:
								super().__init__(
								    lowerCamelCase,    lowerCamelCase,    tokenizer_file=lowerCamelCase,    errors=lowerCamelCase,    bos_token=lowerCamelCase,    eos_token=lowerCamelCase,    sep_token=lowerCamelCase,    cls_token=lowerCamelCase,    unk_token=lowerCamelCase,    pad_token=lowerCamelCase,    mask_token=lowerCamelCase,    add_prefix_space=lowerCamelCase,    trim_offsets=lowerCamelCase,    **lowerCamelCase,    )

								lowerCamelCase__						:						List[str]						      =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
								if pre_tok_state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
											lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    pre_tok_state.pop('''type'''	)	)
											lowerCamelCase__						:						int						      =add_prefix_space
											lowerCamelCase__						:						int						      =pre_tok_class(**lowerCamelCase	)

								lowerCamelCase__						:						Optional[int]						      =add_prefix_space

								lowerCamelCase__						:						str						      ='''post_processor'''
								lowerCamelCase__						:						int						      =getattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)
								if tokenizer_component_instance:
											lowerCamelCase__						:						Union[str, Any]						      =json.loads(tokenizer_component_instance.__getstate__()	)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''sep''']	)
											if "cls" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''cls''']	)

											lowerCamelCase__						:						Dict						      =False

											if state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
														lowerCamelCase__						:						int						      =add_prefix_space
														lowerCamelCase__						:						int						      =True

											if state.get('''trim_offsets''',    lowerCamelCase	) != trim_offsets:
														lowerCamelCase__						:						Optional[int]						      =trim_offsets
														lowerCamelCase__						:						Union[str, Any]						      =True

											if changes_to_apply:
														lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    state.pop('''type'''	)	)
														lowerCamelCase__						:						Dict						      =component_class(**lowerCamelCase	)
														setattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    str	)->   str:
								if self._mask_token is None:
											if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''	)
											return None
								return str(self._mask_token	)



					@mask_token.setter
					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[Any]	)->   List[str]:
								lowerCamelCase__						:						List[str]						      =AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else value
								lowerCamelCase__						:						Any						      =value



					def 						snake_case			(			self				:    Optional[Any],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Dict	)->   BatchEncoding:
								lowerCamelCase__						:						Union[str, Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)
								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    List[str],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    List[Any]	)->   BatchEncoding:
								lowerCamelCase__						:						List[Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)

								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    str,    lowerCamelCase				:    str,    lowerCamelCase				:    Optional[str] = None	)->   Tuple[str]:
								lowerCamelCase__						:						str						      =self._tokenizer.model.save(lowerCamelCase,    name=lowerCamelCase	)
								return tuple(lowerCamelCase	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    int,    lowerCamelCase				:    int=None	)->   str:
								lowerCamelCase__						:						int						      =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    List[int],    lowerCamelCase				:    Optional[List[int]] = None	)->   List[int]:
								lowerCamelCase__						:						Tuple						      =[self.sep_token_id]
								lowerCamelCase__						:						str						      =[self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




