
'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase												=	{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase												=	{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


__lowerCAmelCase												=	{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase_ 						=						["input_ids", "attention_mask"]
							lowerCAmelCase_ 						=						DistilBertTokenizer

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase="[UNK]"  ,  UpperCAmelCase="[SEP]"  ,  UpperCAmelCase="[PAD]"  ,  UpperCAmelCase="[CLS]"  ,  UpperCAmelCase="[MASK]"  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(
														    UpperCAmelCase  ,  tokenizer_file=UpperCAmelCase  ,  do_lower_case=UpperCAmelCase  ,  unk_token=UpperCAmelCase  ,  sep_token=UpperCAmelCase  ,  pad_token=UpperCAmelCase  ,  cls_token=UpperCAmelCase  ,  mask_token=UpperCAmelCase  ,  tokenize_chinese_chars=UpperCAmelCase  ,  strip_accents=UpperCAmelCase  ,  **UpperCAmelCase  ,  )

														_snake_case       =  json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
														if (
														    normalizer_state.get("""lowercase"""  ,  UpperCAmelCase  ) != do_lower_case
														    or normalizer_state.get("""strip_accents"""  ,  UpperCAmelCase  ) != strip_accents
														    or normalizer_state.get("""handle_chinese_chars"""  ,  UpperCAmelCase  ) != tokenize_chinese_chars
														):
																					_snake_case       =  getattr(UpperCAmelCase  ,  normalizer_state.pop("""type"""  )  )
																					_snake_case       =  do_lower_case
																					_snake_case       =  strip_accents
																					_snake_case       =  tokenize_chinese_chars
																					_snake_case       =  normalizer_class(**UpperCAmelCase  )

														_snake_case       =  do_lower_case

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=None  )					->       Optional[int]:
														_snake_case       =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[str]:
														_snake_case       =  self._tokenizer.model.save(UpperCAmelCase  ,  name=UpperCAmelCase  )
														return tuple(UpperCAmelCase  )

'''simple docstring'''
from random import randint, random






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = 5							, ):
							_snake_case       =  [[-1] * number_of_cells]  # Create a highway without any car
							_snake_case       =  0
							_snake_case       =  max(_SCREAMING_SNAKE_CASE							, 0				)
							while i < number_of_cells:
														_snake_case       =  (
														    randint(0							, _SCREAMING_SNAKE_CASE				) if random_speed else initial_speed
														)  # Place the cars
														i += (
														    randint(1							, max_speed * 2				) if random_frequency else frequency
														)  # Arbitrary number, may need tuning
							return highway






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  0
							_snake_case       =  highway_now[car_index + 1 :]
							for cell in range(len(_SCREAMING_SNAKE_CASE				)				):  # May need a better name for this
														if cells[cell] != -1:  # If the cell is not empty then
																					return distance  # we have the distance we wanted
														distance += 1
							# Here if the car is near the end of the highway
							return distance + get_distance(_SCREAMING_SNAKE_CASE							, -1				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Beforce calculations, the highway is empty
							_snake_case       =  [-1] * number_of_cells

							for car_index in range(_SCREAMING_SNAKE_CASE				):
														if highway_now[car_index] != -1:
																					# Add 1 to the current speed of the car and cap the speed
																					_snake_case       =  min(highway_now[car_index] + 1							, _SCREAMING_SNAKE_CASE				)
																					# Number of empty cell before the next car
																					_snake_case       =  get_distance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) - 1
																					# We can't have the car causing an accident
																					_snake_case       =  min(next_highway[car_index]							, _SCREAMING_SNAKE_CASE				)
																					if random() < probability:
																												# Randomly, a driver will slow down
																												_snake_case       =  max(next_highway[car_index] - 1							, 0				)
							return next_highway






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(highway[0]				)

							for i in range(_SCREAMING_SNAKE_CASE				):
														_snake_case       =  update(highway[i]							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
														_snake_case       =  [-1] * number_of_cells

														for car_index in range(_SCREAMING_SNAKE_CASE				):
																					_snake_case       =  next_speeds_calculated[car_index]
																					if speed != -1:
																												# Change the position based on the speed (with % to create the loop)
																												_snake_case       =  (car_index + speed) % number_of_cells
																												# Commit the change of position
																												_snake_case       =  speed
														highway.append(_SCREAMING_SNAKE_CASE				)

							return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()