






"""simple docstring"""



import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


A__	:    Tuple			 = logging.getLogger(__name__)







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Any             =			"summarization"
			_UpperCAmelCase		:List[Any]             =			["loss"]
			_UpperCAmelCase		:str             =			ROUGE_KEYS
			_UpperCAmelCase		:Optional[int]             =			"rouge2"


			def __init__(				self					:  Tuple       ,	snake_case__					:  Dict       ,	**snake_case__					:  Optional[Any] ):
										if hparams.sortish_sampler and hparams.gpus > 1:
																	lowerCamelCase_	:							Union[str, Any]									=False
										elif hparams.max_tokens_per_batch is not None:
																	if hparams.gpus > 1:
																								raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training" )
																	if hparams.sortish_sampler:
																								raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously" )

										super().__init__(snake_case__       ,	num_labels=snake_case__       ,	mode=self.mode       ,	**snake_case__ )
										use_task_specific_params(self.model       ,	"summarization" )
										save_git_info(self.hparams.output_dir )
										lowerCamelCase_	:							Optional[int]									=Path(self.output_dir ) / "metrics.json"
										lowerCamelCase_	:							List[str]									=Path(self.output_dir ) / "hparams.pkl"
										pickle_save(self.hparams       ,	self.hparams_save_path )
										lowerCamelCase_	:							Optional[int]									=0
										lowerCamelCase_	:							Any									=defaultdict(snake_case__ )
										lowerCamelCase_	:							Dict									=self.config.model_type
										lowerCamelCase_	:							List[str]									=self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

										lowerCamelCase_	:							dict									={
										    "data_dir": self.hparams.data_dir,
										    "max_source_length": self.hparams.max_source_length,
										    "prefix": self.model.config.prefix or "",
										}
										lowerCamelCase_	:							int									={
										    "train": self.hparams.n_train,
										    "val": self.hparams.n_val,
										    "test": self.hparams.n_test,
										}
										lowerCamelCase_	:							str									={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

										lowerCamelCase_	:							Any									={
										    "train": self.hparams.max_target_length,
										    "val": self.hparams.val_max_target_length,
										    "test": self.hparams.test_max_target_length,
										}
										assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
										assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
										if self.hparams.freeze_embeds:
																	freeze_embeds(self.model )
										if self.hparams.freeze_encoder:
																	freeze_params(self.model.get_encoder() )
																	assert_all_frozen(self.model.get_encoder() )

										lowerCamelCase_	:							Optional[int]									=get_git_info()["repo_sha"]
										lowerCamelCase_	:							Any									=hparams.num_workers
										lowerCamelCase_	:							Any									=None  # default to config
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,	snake_case__ ):
																	lowerCamelCase_	:							Tuple									=self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	lowerCamelCase_	:							str									=self.decoder_start_token_id
										lowerCamelCase_	:							str									=(
										    SeqaSeqDataset if hasattr(self.tokenizer       ,	"prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
										)
										lowerCamelCase_	:							List[Any]									=False
										lowerCamelCase_	:							int									=self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
										if self.hparams.eval_max_gen_length is not None:
																	lowerCamelCase_	:							Tuple									=self.hparams.eval_max_gen_length
										else:
																	lowerCamelCase_	:							Any									=self.model.config.max_length
										lowerCamelCase_	:							str									=self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict[str, torch.Tensor] ):
										lowerCamelCase_	:							Any									={
										    k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
										}
										save_json(snake_case__       ,	Path(self.output_dir ) / "text_batch.json" )
										save_json({k: v.tolist() for k, v in batch.items()}       ,	Path(self.output_dir ) / "tok_batch.json" )

										lowerCamelCase_	:							str									=True
										return readable_batch


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Union[str, Any]       ,	**snake_case__					:  Optional[Any] ):
										return self.model(snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  List[int] ):
										lowerCamelCase_	:							Optional[int]									=self.tokenizer.batch_decode(
										    snake_case__       ,	skip_special_tokens=snake_case__       ,	clean_up_tokenization_spaces=snake_case__ )
										return lmap(str.strip       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  dict ):
										lowerCamelCase_	:							int									=self.tokenizer.pad_token_id
										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=batch["input_ids"], batch["attention_mask"]
										lowerCamelCase_	:							Any									=batch["labels"]
										if isinstance(self.model       ,	snake_case__ ):
																	lowerCamelCase_	:							List[Any]									=self.model._shift_right(snake_case__ )
										else:
																	lowerCamelCase_	:							Optional[Any]									=shift_tokens_right(snake_case__       ,	snake_case__ )
										if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	lowerCamelCase_	:							Any									=decoder_input_ids
																	self.save_readable_batch(snake_case__ )

										lowerCamelCase_	:							List[str]									=self(snake_case__       ,	attention_mask=snake_case__       ,	decoder_input_ids=snake_case__       ,	use_cache=snake_case__ )
										lowerCamelCase_	:							str									=outputs["logits"]
										if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	lowerCamelCase_	:							Tuple									=nn.CrossEntropyLoss(ignore_index=snake_case__ )

																	assert lm_logits.shape[-1] == self.vocab_size
																	lowerCamelCase_	:							List[str]									=ce_loss_fct(lm_logits.view(-1       ,	lm_logits.shape[-1] )       ,	tgt_ids.view(-1 ) )
										else:
																	lowerCamelCase_	:							Tuple									=nn.functional.log_softmax(snake_case__       ,	dim=-1 )
																	lowerCamelCase_							,						lowerCamelCase_	:							Dict									=label_smoothed_nll_loss(
																	    snake_case__       ,	snake_case__       ,	self.hparams.label_smoothing       ,	ignore_index=snake_case__ )
										return (loss,)


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return self.tokenizer.pad_token_id


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  int ):
										lowerCamelCase_	:							List[Any]									=self._step(snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=dict(zip(self.loss_names       ,	snake_case__ ) )
										# tokens per batch
										lowerCamelCase_	:							Union[str, Any]									=batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
										lowerCamelCase_	:							List[Any]									=batch["input_ids"].shape[0]
										lowerCamelCase_	:							List[str]									=batch["input_ids"].eq(self.pad ).sum()
										lowerCamelCase_	:							int									=batch["input_ids"].eq(self.pad ).float().mean()
										# TODO(SS): make a wandb summary metric for this
										return {"loss": loss_tensors[0], "log": logs}


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  List[str]       ,	snake_case__					:  int ):
										return self._generative_step(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[Any]="val" ):
										self.step_count += 1
										lowerCamelCase_	:							Union[str, Any]									={k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
										lowerCamelCase_	:							Any									=losses["loss"]
										lowerCamelCase_	:							int									={
										    k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
										}
										lowerCamelCase_	:							Optional[Any]									=(
										    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
										)
										lowerCamelCase_	:							torch.FloatTensor									=torch.tensor(snake_case__ ).type_as(snake_case__ )
										generative_metrics.update({k: v.item() for k, v in losses.items()} )
										losses.update(snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									={F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
										lowerCamelCase_	:							Optional[Any]									=self.step_count
										self.metrics[prefix].append(snake_case__ )  # callback writes this to self.metrics_save_path
										lowerCamelCase_	:							str									=flatten_list([x["preds"] for x in outputs] )
										return {
										    "log": all_metrics,
										    "preds": preds,
										    F"""{prefix}_loss""": loss,
										    F"""{prefix}_{self.val_metric}""": metric_tensor,
										}


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, Any] ):
										return calculate_rouge(snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  dict ):
										lowerCamelCase_	:							List[Any]									=time.time()

										# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
										lowerCamelCase_	:							Optional[int]									=self.model.generate(
										    batch["input_ids"]       ,	attention_mask=batch["attention_mask"]       ,	use_cache=snake_case__       ,	decoder_start_token_id=self.decoder_start_token_id       ,	num_beams=self.eval_beams       ,	max_length=self.eval_max_length       ,	)
										lowerCamelCase_	:							Optional[Any]									=(time.time() - ta) / batch["input_ids"].shape[0]
										lowerCamelCase_	:							List[str]									=self.ids_to_clean_text(snake_case__ )
										lowerCamelCase_	:							List[str]									=self.ids_to_clean_text(batch["labels"] )
										lowerCamelCase_	:							Optional[int]									=self._step(snake_case__ )
										lowerCamelCase_	:							List[Any]									=dict(zip(self.loss_names       ,	snake_case__ ) )
										lowerCamelCase_	:							Dict									=self.calc_generative_metrics(snake_case__       ,	snake_case__ )
										lowerCamelCase_	:							str									=np.mean(lmap(snake_case__       ,	snake_case__ ) )
										base_metrics.update(gen_time=snake_case__       ,	gen_len=snake_case__       ,	preds=snake_case__       ,	target=snake_case__       ,	**snake_case__ )
										return base_metrics


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  List[Any]       ,	snake_case__					:  str ):
										return self._generative_step(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Optional[Any] ):
										return self.validation_epoch_end(snake_case__       ,	prefix="test" )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  str ):
										lowerCamelCase_	:							str									=self.n_obs[type_path]
										lowerCamelCase_	:							Any									=self.target_lens[type_path]
										lowerCamelCase_	:							int									=self.dataset_class(
										    self.tokenizer       ,	type_path=snake_case__       ,	n_obs=snake_case__       ,	max_target_length=snake_case__       ,	**self.dataset_kwargs       ,	)
										return dataset


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str       ,	snake_case__					:  int       ,	snake_case__					:  bool = False ):
										lowerCamelCase_	:							Tuple									=self.get_dataset(snake_case__ )

										if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																	lowerCamelCase_	:							List[str]									=dataset.make_sortish_sampler(snake_case__       ,	distributed=self.hparams.gpus > 1 )
																	return DataLoader(
																	    snake_case__       ,	batch_size=snake_case__       ,	collate_fn=dataset.collate_fn       ,	shuffle=snake_case__       ,	num_workers=self.num_workers       ,	sampler=snake_case__       ,	)

										elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
																	lowerCamelCase_	:							str									=dataset.make_dynamic_sampler(
																	    self.hparams.max_tokens_per_batch       ,	distributed=self.hparams.gpus > 1 )
																	return DataLoader(
																	    snake_case__       ,	batch_sampler=snake_case__       ,	collate_fn=dataset.collate_fn       ,	num_workers=self.num_workers       ,	)
										else:
																	return DataLoader(
																	    snake_case__       ,	batch_size=snake_case__       ,	collate_fn=dataset.collate_fn       ,	shuffle=snake_case__       ,	num_workers=self.num_workers       ,	sampler=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Dict									=self.get_dataloader("train"       ,	batch_size=self.hparams.train_batch_size       ,	shuffle=snake_case__ )
										return dataloader


			def      UpperCAmelCase__						(				self					:  Tuple ):
										return self.get_dataloader("val"       ,	batch_size=self.hparams.eval_batch_size )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return self.get_dataloader("test"       ,	batch_size=self.hparams.eval_batch_size )


			@staticmethod
			def      UpperCAmelCase__						(				snake_case__					:  str       ,	snake_case__					:  List[str] ):
										BaseTransformer.add_model_specific_args(snake_case__       ,	snake_case__ )
										add_generic_args(snake_case__       ,	snake_case__ )
										parser.add_argument(
										    "--max_source_length"       ,	default=1024       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--max_target_length"       ,	default=56       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--val_max_target_length"       ,	default=142       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument(
										    "--test_max_target_length"       ,	default=142       ,	type=snake_case__       ,	help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )       ,	)
										parser.add_argument("--freeze_encoder"       ,	action="store_true" )
										parser.add_argument("--freeze_embeds"       ,	action="store_true" )
										parser.add_argument("--sortish_sampler"       ,	action="store_true"       ,	default=snake_case__ )
										parser.add_argument("--overwrite_output_dir"       ,	action="store_true"       ,	default=snake_case__ )
										parser.add_argument("--max_tokens_per_batch"       ,	type=snake_case__       ,	default=snake_case__ )
										parser.add_argument("--logger_name"       ,	type=snake_case__       ,	choices=["default", "wandb", "wandb_shared"]       ,	default="default" )
										parser.add_argument("--n_train"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--n_val"       ,	type=snake_case__       ,	default=500       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--n_test"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument(
										    "--task"       ,	type=snake_case__       ,	default="summarization"       ,	required=snake_case__       ,	help="# examples. -1 means use all." )
										parser.add_argument("--label_smoothing"       ,	type=snake_case__       ,	default=0.0       ,	required=snake_case__ )
										parser.add_argument("--src_lang"       ,	type=snake_case__       ,	default=""       ,	required=snake_case__ )
										parser.add_argument("--tgt_lang"       ,	type=snake_case__       ,	default=""       ,	required=snake_case__ )
										parser.add_argument("--eval_beams"       ,	type=snake_case__       ,	default=snake_case__       ,	required=snake_case__ )
										parser.add_argument(
										    "--val_metric"       ,	type=snake_case__       ,	default=snake_case__       ,	required=snake_case__       ,	choices=["bleu", "rouge2", "loss", None] )
										parser.add_argument("--eval_max_gen_length"       ,	type=snake_case__       ,	default=snake_case__       ,	help="never generate more than n tokens" )
										parser.add_argument("--save_top_k"       ,	type=snake_case__       ,	default=1       ,	required=snake_case__       ,	help="How many checkpoints to save" )
										parser.add_argument(
										    "--early_stopping_patience"       ,	type=snake_case__       ,	default=-1       ,	required=snake_case__       ,	help=(
										        "-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"
										        " val_check_interval will effect it."
										    )       ,	)
										return parser







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[Any]             =			"translation"
			_UpperCAmelCase		:str             =			["loss"]
			_UpperCAmelCase		:Any             =			["bleu"]
			_UpperCAmelCase		:int             =			"bleu"


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  int       ,	**snake_case__					:  List[str] ):
										super().__init__(snake_case__       ,	**snake_case__ )
										lowerCamelCase_	:							Any									=hparams.src_lang
										lowerCamelCase_	:							Dict									=hparams.tgt_lang


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  int ):
										return calculate_bleu(snake_case__       ,	snake_case__ )


def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   int=None  )    ->     SummarizationModule:
							Path(args.output_dir  ).mkdir(exist_ok=lowerCamelCase__  )
							check_output_dir(lowerCamelCase__    ,						expected_items=3  )

							if model is None:
														if "summarization" in args.task:
																					lowerCamelCase_	:							SummarizationModule									=SummarizationModule(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							SummarizationModule									=TranslationModule(lowerCamelCase__  )
							lowerCamelCase_	:							Tuple									=Path(args.data_dir  ).name
							if (
							    args.logger_name == "default"
							    or args.fast_dev_run
							    or str(args.output_dir  ).startswith("/tmp"  )
							    or str(args.output_dir  ).startswith("/var"  )
							):
														lowerCamelCase_	:							Optional[Any]									=True  # don't pollute wandb logs unnecessarily
							elif args.logger_name == "wandb":
														from pytorch_lightning.loggers import WandbLogger

														lowerCamelCase_	:							List[str]									=os.environ.get("WANDB_PROJECT"    ,						lowerCamelCase__  )
														lowerCamelCase_	:							str									=WandbLogger(name=model.output_dir.name    ,						project=lowerCamelCase__  )

							elif args.logger_name == "wandb_shared":
														from pytorch_lightning.loggers import WandbLogger

														lowerCamelCase_	:							Union[str, Any]									=WandbLogger(name=model.output_dir.name    ,						project=F"""hf_{dataset}"""  )

							if args.early_stopping_patience >= 0:
														lowerCamelCase_	:							int									=get_early_stopping_callback(model.val_metric    ,						args.early_stopping_patience  )
							else:
														lowerCamelCase_	:							Optional[int]									=False

							lowerCamelCase_	:							int									=args.val_metric == "loss"
							lowerCamelCase_	:							pl.Trainer									=generic_train(
							    lowerCamelCase__    ,						lowerCamelCase__    ,						logging_callback=SeqaSeqLoggingCallback()    ,						checkpoint_callback=get_checkpoint_callback(
							        args.output_dir    ,						model.val_metric    ,						args.save_top_k    ,						lowerCamelCase__  )    ,						early_stopping_callback=lowerCamelCase__    ,						logger=lowerCamelCase__    ,						)
							pickle_save(model.hparams    ,						model.output_dir / "hparams.pkl"  )
							if not args.do_predict:
														return model

							lowerCamelCase_	:							List[str]									=""
							lowerCamelCase_	:							str									=sorted(glob.glob(os.path.join(args.output_dir    ,						"*.ckpt"  )    ,						recursive=lowerCamelCase__  )  )
							if checkpoints:
														lowerCamelCase_	:							List[Any]									=checkpoints[-1]
														lowerCamelCase_	:							Union[str, Any]									=checkpoints[-1]
							trainer.logger.log_hyperparams(model.hparams  )

							# test() without a model tests using the best checkpoint automatically
							trainer.test()
							return model


if __name__ == "__main__":
						A__	:    str			 = argparse.ArgumentParser()
						A__	:    Tuple			 = pl.Trainer.add_argparse_args(parser)
						A__	:    str			 = SummarizationModule.add_model_specific_args(parser, os.getcwd())

						A__	:    Any			 = parser.parse_args()

						main(args)











"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A__	:    List[str]			 = datasets.utils.logging.get_logger(__name__)







@dataclass
class        lowercase__      (							datasets.BuilderConfig       ):
			_UpperCAmelCase		:int             =			10000
			_UpperCAmelCase		:Optional[List[str]]             =			None
			_UpperCAmelCase		:Optional[datasets.Features]             =			None







class        lowercase__      (							datasets.ArrowBasedBuilder       ):
			_UpperCAmelCase		:Optional[int]             =			ParquetConfig


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return datasets.DatasetInfo(features=self.config.features )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any] ):
										if not self.config.data_files:
																	raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
										lowerCamelCase_	:							str									=dl_manager.download_and_extract(self.config.data_files )
										if isinstance(snake_case__       ,	(str, list, tuple) ):
																	lowerCamelCase_	:							Dict									=data_files
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							List[Any]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							Any									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={"files": files} )]
										lowerCamelCase_	:							Optional[int]									=[]
										for split_name, files in data_files.items():
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Optional[int]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							int									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(snake_case__ ):
																															with open(snake_case__       ,	"rb" ) as f:
																																						lowerCamelCase_	:							List[Any]									=datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
																															break
																	splits.append(datasets.SplitGenerator(name=snake_case__       ,	gen_kwargs={"files": files} ) )
										return splits


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pa.Table ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowerCamelCase_	:							List[str]									=table_cast(snake_case__       ,	self.info.features.arrow_schema )
										return pa_table


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema ) != sorted(self.config.columns ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
										for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
																	with open(snake_case__       ,	"rb" ) as f:
																								lowerCamelCase_	:							List[str]									=pq.ParquetFile(snake_case__ )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,	columns=self.config.columns ) ):
																																						lowerCamelCase_	:							Union[str, Any]									=pa.Table.from_batches([record_batch] )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__ )
																								except ValueError as e:
																															logger.error(F"""Failed to read file '{file}' with error {type(snake_case__ )}: {e}""" )
																															raise



