





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

from utils import logger

class   lowerCAmelCase				(   __UpperCamelCase     ):
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      Dict:
     lowerCamelCase__       : Dict   					=						params

     lowerCamelCase__       : List[str]   					=						np.array(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						np.array([len(UpperCAmelCase      ) 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    : int     ,	UpperCAmelCase    : str      )     ->      int:
     return (self.token_ids[index], self.lengths[index])
 def __len__( self    : Optional[int]      )     ->      List[Any]:
     return len(self.lengths      )
 def 	A_      ( self    : Union[str, Any]      )     ->      Optional[Any]:
     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    : Union[str, Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : str   					=						self.params.max_model_input_size
     lowerCamelCase__       : Dict   					=						self.lengths > max_len
     logger.info(F"""Splitting {sum(UpperCAmelCase      )} too long sequences."""      )

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

     lowerCamelCase__       : Optional[int]   					=						[]
     lowerCamelCase__       : str   					=						[]
     if self.params.mlm:
         lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
     else:
         lowerCamelCase__					,				lowerCamelCase__       : Union[str, 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:
             lowerCamelCase__       : Tuple   					=						[]
             for sub_s in divide_chunks(seq_     ,	max_len - 2      ):
                 if sub_s[0] != cls_id:
                     lowerCamelCase__       : Any   					=						np.insert(UpperCAmelCase     ,	0     ,	UpperCAmelCase      )
                 if sub_s[-1] != sep_id:
                     lowerCamelCase__       : Dict   					=						np.insert(UpperCAmelCase     ,	len(UpperCAmelCase      )     ,	UpperCAmelCase      )
                 assert len(UpperCAmelCase      ) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(UpperCAmelCase      )

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

     lowerCamelCase__       : int   					=						np.array(UpperCAmelCase      )
     lowerCamelCase__       : Optional[Any]   					=						np.array(UpperCAmelCase      )
 def 	A_      ( self    : List[Any]      )     ->      Dict:
     lowerCamelCase__       : Union[str, Any]   					=						len(self      )
     lowerCamelCase__       : Dict   					=						self.lengths > 11
     lowerCamelCase__       : List[Any]   					=						self.token_ids[indices]
     lowerCamelCase__       : Union[str, Any]   					=						self.lengths[indices]
     lowerCamelCase__       : List[str]   					=						len(self      )
     logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""      )
 def 	A_      ( self    : str      )     ->      Dict:
     if "unk_token" not in self.params.special_tok_ids:
         return
     else:
         lowerCamelCase__       : List[Any]   					=						self.params.special_tok_ids['unk_token']
     lowerCamelCase__       : Any   					=						len(self      )
     lowerCamelCase__       : Dict   					=						np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
     lowerCamelCase__       : int   					=						(unk_occs / self.lengths) < 0.5
     lowerCamelCase__       : Tuple   					=						self.token_ids[indices]
     lowerCamelCase__       : List[Any]   					=						self.lengths[indices]
     lowerCamelCase__       : Optional[Any]   					=						len(self      )
     logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""      )
 def 	A_      ( self    : List[str]      )     ->      List[Any]:
     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    : List[Any]     ,	UpperCAmelCase    : List[str]      )     ->      Optional[Any]:
     lowerCamelCase__       : List[str]   					=						[t[0] for t in batch]
     lowerCamelCase__       : Dict   					=						[t[1] for t in batch]
     assert len(UpperCAmelCase      ) == len(UpperCAmelCase      )

     # Max for paddings
     lowerCamelCase__       : Any   					=						max(UpperCAmelCase      )

     # Pad token ids
     if self.params.mlm:
         lowerCamelCase__       : Dict   					=						self.params.special_tok_ids['pad_token']
     else:
         lowerCamelCase__       : int   					=						self.params.special_tok_ids['unk_token']
     lowerCamelCase__       : str   					=						[list(t.astype(UpperCAmelCase      )      ) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase      )) for t in token_ids]
     assert len(tk_      ) == len(UpperCAmelCase      )
     assert all(len(UpperCAmelCase      ) == max_seq_len_ for t in tk_      )

     lowerCamelCase__       : List[str]   					=						torch.tensor(tk_      )  # (bs, max_seq_len_)
     lowerCamelCase__       : Any   					=						torch.tensor(UpperCAmelCase      )  # (bs)
     return tk_t, lg_t












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						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    : Tuple      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

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

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

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

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

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

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    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(_UpperCAmelCase					)]:.2f}MiB"""					)
        lowerCamelCase__       : List[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"""					)





