





"""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_mobilebert import MobileBertTokenizer


a		:				List[str]  			=    logging.get_logger(__name__)

a		:				List[Any]  			=    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a		:				Optional[int]  			=    {
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

a		:				int  			=    {"""mobilebert-uncased""": 512}


a		:				Tuple  			=    {}


class  __UpperCamelCase       (		__UpperCAmelCase		):
							lowerCamelCase     :      Tuple         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Any         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase     :      Optional[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      Tuple         =MobileBertTokenizer






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

								a   :	Optional[Any]			=       json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
								if (
								    normalizer_state.get("lowercase"   ,      lowerCAmelCase__   ) != do_lower_case
								    or normalizer_state.get("strip_accents"   ,      lowerCAmelCase__   ) != strip_accents
								    or normalizer_state.get("handle_chinese_chars"   ,      lowerCAmelCase__   ) != tokenize_chinese_chars
								):
									a   :	Dict			=       getattr(lowerCAmelCase__   ,      normalizer_state.pop("type"   )   )
									a   :	Union[str, Any]			=       do_lower_case
									a   :	int			=       strip_accents
									a   :	List[str]			=       tokenize_chinese_chars
									a   :	Tuple			=       normalizer_class(**lowerCAmelCase__   )

								a   :	Dict			=       do_lower_case






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=None   )			->				Dict:
								a   :	List[str]			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Dict:
								a   :	Any			=       [self.sep_token_id]
								a   :	str			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Optional[Any]:
								a   :	List[str]			=       self._tokenizer.model.save(lowerCAmelCase__   ,      name=lowerCAmelCase__   )
								return tuple(lowerCAmelCase__   )









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






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






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






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






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


