import random import sys from typing import Any, Generator import pandas as pd from pathlib import Path from ..Enums import TaskType import Project_Model.Libs.BPE as BPE # from Scripts.Libs.CleaningPipeline.special_token import SpecialToken from Project_Model.Libs.Transformer import ( SpannedMasker, truncate_rdf_list, normalize_sequence, ) from Project_Model.Libs.BPE import SpecialToken class Batcher: def __init__( self, dataset_path: Path, max_length: int, tokenizer: BPE.TokeNanoCore, masker: SpannedMasker, seed: int = 0, debug = False ) -> None: # ABSTRACT, TRIPLE # tasks: # rdf2text: X: TRIPLE, Y: ABSTRACT # text2rdf: X: ABSTRACT, X:TRIPLE # masking ( call masker): X: incomplete_triple Y: complete_triple (as exam) # completation: X: TRIPLE SUBSET, Y: related TRIPLE SUBSET # it will truncate # it will instantiate spanmaskter and truncator self._dataset_path = dataset_path self._tokenizer = tokenizer self._masker = masker self.__max_length = max_length self._seed = seed # self._token_completation = TokenCompletationTransformer(sotl,eos) self._completation_task_token_truncator = truncate_rdf_list self.__debug = debug def batch(self, batch_size) -> Generator[ tuple[ list[list[int]], list[list[int]], list[list[int]], list[list[int]], TaskType ], Any, Any, ]: """ Yields: X,Y,padding_X """ RNG = random.Random(self._seed) self._masker.reseed(self._seed) for batch in pd.read_csv(self._dataset_path, chunksize=batch_size): tokenized_batch = pd.DataFrame() # encode tokenized_batch[["Abstract", "RDFs"]] = batch[["Abstract", "RDFs"]].map( lambda t: self._tokenizer.encode(t) ) X, Y, padding_X, padding_Y = self.__rdf2txt_transformation(tokenized_batch) yield X, Y, padding_X, padding_Y, TaskType.RDF2TXT ( X, Y, padding_X, padding_Y, ) = self.__txt2rdf_transformation(tokenized_batch) yield X, Y, padding_X, padding_Y, TaskType.TEXT2RDF ( X, Y, padding_X, padding_Y, ) = self.__masking_trasformation(tokenized_batch) yield X, Y, padding_X, padding_Y, TaskType.MASKING ( X, Y, padding_X, padding_Y, ) = self.__token_completation_task( tokenized_batch, RNG.randint(0, sys.maxsize) ) yield X, Y, padding_X, padding_Y, TaskType.COMPLETATION # output = pd.concat([rdf2txt_batch,txt2rdf_batch,completation_batch],ignore_index=True) # output = output.sample(frac=1).reset_index(drop=True) # self.decode_debug(output) # yield output def __random_subset_rdfs(self, batch: pd.DataFrame, seed=0): # WIP rng = random.Random(seed) def to_list(x): return x.split(SpecialToken.START_TRIPLE.value)[1:] batch["RDFs"] = batch["RDFs"].map(to_list) def decode_debug(self, batch: pd.DataFrame): decoded = pd.DataFrame() decoded[["X", "Y"]] = batch[["X", "Y"]].map(lambda t: self._tokenizer.decode(t)) print(decoded) def __normalization( self, X: list[list[int]], Y: list[list[int]] ) -> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]: pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0] end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0] out_X = [] padding_X = [] out_Y = [] padding_Y = [] for x in X: out_x, padding_x = normalize_sequence( x, self.__max_length, pad_token, end_token, True ) out_X.append(out_x) padding_X.append(padding_x) for y in Y: out_y, padding_y = normalize_sequence( y, self.__max_length, pad_token, end_token, True ) out_Y.append(out_y) padding_Y.append(padding_y) return out_X, out_Y, padding_X, padding_Y def __rdf2txt_transformation(self, batch: pd.DataFrame): X: list[list[int]] task_token = self._tokenizer.encode(SpecialToken.RDF_TO_TEXT.value) out = batch.rename(columns={"RDFs": "X", "Abstract": "Y"})[["X", "Y"]] out["X"] = [task_token + x for x in out["X"]] return self.__normalization(out["X"].to_list(), out["Y"].to_list()) def __txt2rdf_transformation(self, batch: pd.DataFrame): task_token = self._tokenizer.encode(SpecialToken.TEXT_TO_RDF.value) out = batch.rename(columns={"Abstract": "X", "RDFs": "Y"})[["X", "Y"]] out["X"] = [task_token + x for x in out["X"]] return self.__normalization(out["X"].to_list(), out["Y"].to_list()) def __masking_trasformation(self, batch: pd.DataFrame): X = [] Y = [] for rdf in batch["RDFs"]: x, y = self._masker.mask_sequence(rdf[:self.__max_length]) X.append(x) Y.append(y) return self.__normalization(X, Y) def __token_completation_task(self, batch: pd.DataFrame, minibatch_seed: int): continue_triple_token = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[ 0 ] eot = self._tokenizer.encode(SpecialToken.END_TRIPLE.value)[0] X = [] Y = [] for rdf in batch["RDFs"]: # here first truncate to max_lenght rdf = rdf[: self.__max_length] # truncator that uses "eot" so no problem x, y = self._completation_task_token_truncator( rdf, 0.5, continue_triple_token, eot, minibatch_seed ) X.append(x) Y.append(y) return self.__token_cmpletation_task_special_normalization(X, Y) def __token_cmpletation_task_special_normalization(self, X: list[list[int]], Y: list[list[int]] ) -> tuple[list[list[int]], list[list[int]], list[list[int]], list[list[int]]]: def continue_rdf_padding(sequence: list[int], pad_token: int): for i, x in enumerate(sequence): if x == pad_token: i = i+1 # continueRDF will be excluded by the mask # fill the tail with True and stop return [False] * i + [True] * (len(sequence) - i) return [False] * len(sequence) # no pad token found pad_token = self._tokenizer.encode(SpecialToken.PAD.value)[0] end_token = self._tokenizer.encode(SpecialToken.END_OF_SEQUENCE.value)[0] continue_rdf = self._tokenizer.encode(SpecialToken.CONTINUE_RDF.value)[0] out_X = [] padding_X = [] out_Y = [] padding_Y = [] for x in X: out_x, _ = normalize_sequence( x, self.__max_length, pad_token, end_token, True ) out_X.append(out_x) # padding_X.append(padding_x) special_padding = continue_rdf_padding(out_x,continue_rdf) padding_X.append(special_padding) for y in Y: out_y, padding_y = normalize_sequence( y, self.__max_length, pad_token, end_token, True ) out_Y.append(out_y) # special padding # special_padding = continue_rdf_padding(out_y,continue_rdf) # padding_Y.append(special_padding) padding_Y.append(padding_y) return out_X, out_Y, padding_X, padding_Y if __name__ == "__main__": DATASET_PATH = Path("Assets/Dataset/Tmp/rdf_text.csv") VOCABULARY_path = "Assets/Dataset/Tmp/trimmed.json" from pathlib import Path VOCABULARY = BPE.load_nanos_vocabulary(Path(VOCABULARY_path)) SPECIAL_LIST = BPE.default_special_tokens() TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_LIST) SPECIAL_TOKENS: set[int] = set(TOKENANO.encode("".join(SPECIAL_LIST))) MASKER = SpannedMasker(TOKENANO.vocabulary_size, SPECIAL_TOKENS) prova = "Cactus Flower is a 1969 American screwball comedy film directed by Gene Saks, and starring Walter Matthau, Ingrid Bergman and Goldie Hawn, who won an Academy Award for her performance.The screenplay was adapted by I. A. L. Diamond from the 1965 Broadway play of the same title written by Abe Burrows, which, in turn, is based on the French play Fleur de cactus by Pierre Barillet and Jean-Pierre Gredy. Cactus Flower was the ninth highest-grossing film of 1969." print(TOKENANO.encode(prova)) batcher = Batcher(DATASET_PATH,256, TOKENANO, MASKER) for batch in batcher.batch(8): print(batch)