

"""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 inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin





class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       SpeechaTextaTokenizer
		SCREAMING_SNAKE_CASE														=       False


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()

							A     =   "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" ")
							A     =   ["he re</w> 123", "here a 1456"]
							A     =   dict(zip(__SCREAMING_SNAKE_CASE	,	range(len(__SCREAMING_SNAKE_CASE))))

							A     =   {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

							A     =   tempfile.mkdtemp()
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["merges_file"])

							with open(self.vocab_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")

							with open(self.merges_file	,	"w") as fp:
												fp.write("\n".join(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   list(self.get_tokenizer().get_vocab().keys())

							self.assertEqual(vocab_keys[0]	,	"<s>")
							self.assertEqual(vocab_keys[1]	,	"<pad>")
							self.assertEqual(vocab_keys[-1]	,	"vocab")
							self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							self.assertEqual(self.get_tokenizer().vocab_size	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							# make sure @@ is correctly concatenated
							A     =   [4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
							A     =   tokenizer.decode(__SCREAMING_SNAKE_CASE)

							self.assertTrue(output_string == "herecouple words ofthe")


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												os.remove(os.path.join(__SCREAMING_SNAKE_CASE	,	"merges.txt"))

												# load tokenizer without merges file should not throw an error
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							if not self.test_slow_tokenizer:
												return

							A     =   inspect.signature(self.tokenizer_class.__init__)
							A     =   self.get_tokenizer()

							for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																	self.assertIn(__SCREAMING_SNAKE_CASE	,	tokenizer.init_kwargs)





