"""simple docstring"""





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A  =				logging.get_logger(__name__)

__A  =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A  =				{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A  =				{'''allegro/herbert-base-cased''': 514}
__A  =				{}
class  _snake_case     (					a__		):
						snake_case__			  =			VOCAB_FILES_NAMES
						snake_case__			  =			PRETRAINED_VOCAB_FILES_MAP
						snake_case__			  =			PRETRAINED_INIT_CONFIGURATION
						snake_case__			  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__			  =			HerbertTokenizer






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			Any=None							,					UpperCAmelCase      :			str="<s>"							,					UpperCAmelCase      :			Union[str, Any]="<unk>"							,					UpperCAmelCase      :			int="<pad>"							,					UpperCAmelCase      :			Optional[Any]="<mask>"							,					UpperCAmelCase      :			str="</s>"							,					**UpperCAmelCase      :			Union[str, Any]							,					):
													super().__init__(
													    UpperCAmelCase							,					UpperCAmelCase							,					tokenizer_file=UpperCAmelCase							,					cls_token=UpperCAmelCase							,					unk_token=UpperCAmelCase							,					pad_token=UpperCAmelCase							,					mask_token=UpperCAmelCase							,					sep_token=UpperCAmelCase							,					**UpperCAmelCase							,					)






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Any		   =     [self.cls_token_id]
													__lowerCamelCase					:		Optional[Any]		   =     [self.sep_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       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			bool = False    ):
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=UpperCAmelCase							,					token_ids_a=UpperCAmelCase							,					already_has_special_tokens=UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Any		   =     [self.sep_token_id]
													__lowerCamelCase					:		int		   =     [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       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[str] = None    ):
													__lowerCamelCase					:		List[Any]		   =     self._tokenizer.model.save(UpperCAmelCase							,					name=UpperCAmelCase    )
													return tuple(UpperCAmelCase    )
"""simple docstring"""





import gc
import threading
import time

import psutil
import torch
class  _snake_case     :






						def __init__(       self      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     psutil.Process()
													__lowerCamelCase					:		List[Any]		   =     False






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     -1

													while True:
																				__lowerCamelCase					:		Union[str, Any]		   =     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       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     True
													__lowerCamelCase					:		Union[str, Any]		   =     threading.Thread(target=self.peak_monitor    )
													__lowerCamelCase					:		Optional[Any]		   =     True
													self.thread.start()







						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     False
													self.thread.join()
													return self.cpu_memory_peak

__A  =				PeakCPUMemory()


def    lowercase_   (   ) ->      Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     {"time": time.time()}

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

							# CPU mem
							__lowerCamelCase					:		Optional[int]		   =     psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

							# GPU mem
							for i in range(torch.cuda.device_count()      ):
														__lowerCamelCase					:		int		   =     torch.cuda.memory_allocated(_lowerCamelCase      )
							torch.cuda.reset_peak_memory_stats()

							return measures


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     {"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					:		Tuple		   =     (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

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

							return measures







def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	List[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(_lowerCamelCase      )]:.2f}MiB"""      )
														__lowerCamelCase					:		List[Any]		   =     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"""      )