





"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						str      =							params

								a :						Any      =							np.array(lowerCAmelCase__ )
								a :						List[str]      =							np.array([len(lowerCAmelCase__ ) for t in data] )

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()






							def __getitem__(  self ,   lowerCAmelCase__ )      ->					List[str]:
								return (self.token_ids[index], self.lengths[index])






							def __len__(  self )      ->					Any:
								return len(self.lengths )






							def        __a  (  self )      ->					List[str]:
								assert len(self.token_ids ) == len(self.lengths )
								assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							self.params.max_model_input_size
								a :						int      =							self.lengths > max_len
								logger.info(f"""Splitting {sum(lowerCAmelCase__ )} too long sequences.""" )

								def divide_chunks(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									return [l[i : i + n] for i in range(0 ,   len(lowerCAmelCase__ ) ,   lowerCAmelCase__ )]

								a :						List[Any]      =							[]
								a :						Optional[Any]      =							[]
								if self.params.mlm:
									a,			a :						Dict      =							self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
								else:
									a,			a :						Any      =							self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

								for seq_, len_ in zip(self.token_ids ,   self.lengths ):
									assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
									if len_ <= max_len:
										new_tok_ids.append(seq_ )
										new_lengths.append(len_ )
									else:
										a :						Optional[Any]      =							[]
										for sub_s in divide_chunks(seq_ ,   max_len - 2 ):
											if sub_s[0] != cls_id:
												a :						Optional[int]      =							np.insert(lowerCAmelCase__ ,   0 ,   lowerCAmelCase__ )
											if sub_s[-1] != sep_id:
												a :						Union[str, Any]      =							np.insert(lowerCAmelCase__ ,   len(lowerCAmelCase__ ) ,   lowerCAmelCase__ )
											assert len(lowerCAmelCase__ ) <= max_len
											assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
											sub_seqs.append(lowerCAmelCase__ )

										new_tok_ids.extend(lowerCAmelCase__ )
										new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )

								a :						int      =							np.array(lowerCAmelCase__ )
								a :						List[Any]      =							np.array(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Any      =							len(self )
								a :						str      =							self.lengths > 11
								a :						List[Any]      =							self.token_ids[indices]
								a :						Optional[int]      =							self.lengths[indices]
								a :						int      =							len(self )
								logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )






							def        __a  (  self )      ->					Dict:
								if "unk_token" not in self.params.special_tok_ids:
									return
								else:
									a :						Tuple      =							self.params.special_tok_ids["unk_token"]
								a :						Optional[int]      =							len(self )
								a :						Optional[Any]      =							np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
								a :						str      =							(unk_occs / self.lengths) < 0.5
								a :						List[str]      =							self.token_ids[indices]
								a :						Optional[Any]      =							self.lengths[indices]
								a :						Tuple      =							len(self )
								logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )






							def        __a  (  self )      ->					int:
								if not self.params.is_master:
									return
								logger.info(f"""{len(self )} sequences""" )
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[Any]:
								a :						int      =							[t[0] for t in batch]
								a :						int      =							[t[1] for t in batch]
								assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )

								# Max for paddings
								a :						Any      =							max(lowerCAmelCase__ )

								# Pad token ids
								if self.params.mlm:
									a :						str      =							self.params.special_tok_ids["pad_token"]
								else:
									a :						List[Any]      =							self.params.special_tok_ids["unk_token"]
								a :						Union[str, Any]      =							[list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
								assert len(tk_ ) == len(lowerCAmelCase__ )
								assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )

								a :						Dict      =							torch.tensor(tk_ )  # (bs, max_seq_len_)
								a :						int      =							torch.tensor(lowerCAmelCase__ )  # (bs)
								return tk_t, lg_t









"""simple docstring"""




import gc
import threading
import time

import psutil
import torch


class 			__UpperCamelCase							:






							def __init__(  self )      ->					Any:
								a :						str      =							psutil.Process()
								a :						str      =							False






							def        __a  (  self )      ->					str:
								a :						List[Any]      =							-1

								while True:
									a :						str      =							max(self.process.memory_info().rss ,   self.cpu_memory_peak )

									# can't sleep or will not catch the peak right (this comment is here on purpose)
									if not self.peak_monitoring:
										break






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							True
								a :						Optional[Any]      =							threading.Thread(target=self.peak_monitor )
								a :						List[Any]      =							True
								self.thread.start()






							def        __a  (  self )      ->					List[str]:
								a :						Optional[int]      =							False
								self.thread.join()
								return self.cpu_memory_peak
a		:					Tuple	      =     PeakCPUMemory()






def  _SCREAMING_SNAKE_CASE						(      )					->List[Any]:






	'''simple docstring'''






	a :						List[str]      =							{"time": time.time()}

	gc.collect()
	torch.cuda.empty_cache()

	# CPU mem
	a :						Tuple      =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Any      =							torch.cuda.memory_allocated(_lowercase   )
	torch.cuda.reset_peak_memory_stats()

	return measures






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]   )					->Union[str, Any]:






	'''simple docstring'''






	a :						int      =							{"time": time.time() - start_measures["time"]}

	gc.collect()
	torch.cuda.empty_cache()

	# CPU mem
	a :						List[Any]      =							(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	a :						Union[str, Any]      =							(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Optional[Any]      =							(torch.cuda.memory_allocated(_lowercase   ) - start_measures[str(_lowercase   )]) / 2**20
		a :						Optional[Any]      =							(torch.cuda.max_memory_allocated(_lowercase   ) - start_measures[str(_lowercase   )]) / 2**20

	return measures







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       Union[str, Any]   )					->Optional[int]:






	'''simple docstring'''






	print(F"""{description}:"""   )
	print(F"""- Time: {measures['time']:.2f}s"""   )
	for i in range(torch.cuda.device_count()   ):
		print(F"""- GPU {i} allocated: {measures[str(_lowercase   )]:.2f}MiB"""   )
		a :						str      =							measures[F"""{i}-peak"""]
		print(F"""- GPU {i} peak: {peak:.2f}MiB"""   )
	print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""   )
	print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""   )


