import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_rembert import RemBertTokenizer
else:
		__a  :     List[str]       =  None

__a  :     int       =  logging.get_logger(__name__)
__a  :     Tuple       =  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a  :     Dict       =  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

__a  :     Any       =  {
    """google/rembert""": 2_5_6,
}

__a  :     Tuple       =  """▁"""




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						VOCAB_FILES_NAMES
			__a  : Dict			 =						PRETRAINED_VOCAB_FILES_MAP
			__a  : Tuple			 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a  : List[str]			 =						RemBertTokenizer





			def __init__(   self      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__="[CLS]"      ,   lowerCAmelCase__="[SEP]"      ,   lowerCAmelCase__="<unk>"      ,   lowerCAmelCase__="[SEP]"      ,   lowerCAmelCase__="<pad>"      ,   lowerCAmelCase__="[CLS]"      ,   lowerCAmelCase__="[MASK]"      ,   **lowerCAmelCase__      ,   )     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							AddedToken(lowerCAmelCase__      ,   lstrip=lowerCAmelCase__      ,   rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						) else mask_token

							super().__init__(
							    lowerCAmelCase__      ,   tokenizer_file=lowerCAmelCase__      ,   do_lower_case=lowerCAmelCase__      ,   remove_space=lowerCAmelCase__      ,   keep_accents=lowerCAmelCase__      ,   bos_token=lowerCAmelCase__      ,   eos_token=lowerCAmelCase__      ,   unk_token=lowerCAmelCase__      ,   sep_token=lowerCAmelCase__      ,   pad_token=lowerCAmelCase__      ,   cls_token=lowerCAmelCase__      ,   mask_token=lowerCAmelCase__      ,   **lowerCAmelCase__      ,   )

							__lowercase 			=							do_lower_case
							__lowercase 			=							remove_space
							__lowercase 			=							keep_accents
							__lowercase 			=							vocab_file
							__lowercase 			=							False if not self.vocab_file else True





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					List[int]:

							'''simple docstring'''


							__lowercase 			=							[self.sep_token_id]
							__lowercase 			=							[self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = False						)     ->					List[int]:

							'''simple docstring'''


							if already_has_special_tokens:
											if token_ids_a is not None:
															raise ValueError(
															    '''You should not supply a second sequence if the provided sequence of '''
															    '''ids is already formatted with special tokens for the model.'''						)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
											return [1] + ([0] * len(lowerCAmelCase__						)) + [1] + ([0] * len(lowerCAmelCase__						)) + [1]
							return [1] + ([0] * len(lowerCAmelCase__						)) + [1]





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					List[int]:

							'''simple docstring'''


							__lowercase 			=							[self.sep_token_id]
							__lowercase 			=							[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					Tuple[str]:

							'''simple docstring'''


							if not os.path.isdir(lowerCAmelCase__						):
											logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCAmelCase__						)						)
											return
							__lowercase 			=							os.path.join(
							    lowerCAmelCase__      ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCAmelCase__						):
											copyfile(self.vocab_file      ,   lowerCAmelCase__						)

							return (out_vocab_file,)
import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

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

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

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

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

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

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""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"							)
								__lowercase 			=							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"							)