
def 							__SCREAMING_SNAKE_CASE   (		)     ->						Dict:
				return [list(range(1000 - i    ,							-1000 - i    ,							-1			)			) for i in range(1000			)]


SCREAMING_SNAKE_CASE__							=				generate_large_matrix()
SCREAMING_SNAKE_CASE__							=				(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]			)     ->						str:
				assert all(row == sorted(__lowerCAmelCase    ,							reverse=__lowerCAmelCase			) for row in grid			)
				assert all(list(__lowerCAmelCase			) == sorted(__lowerCAmelCase    ,							reverse=__lowerCAmelCase			) for col in zip(*__lowerCAmelCase			)			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Union[str, Any]			)     ->						Dict:
				__lowercase								=					0
				__lowercase								=					len(__lowerCAmelCase			) - 1

				# Edge cases such as no values or all numbers are negative.
				if not array or array[0] < 0:
								return 0

				while right + 1 > left:
								__lowercase								=					(left + right) // 2
								__lowercase								=					array[mid]

								# Num must be negative and the index must be greater than or equal to 0.
								if num < 0 and array[mid - 1] >= 0:
												return mid

								if num >= 0:
												__lowercase								=					mid + 1
								else:
												__lowercase								=					mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
				return len(__lowerCAmelCase			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[int]			)     ->						Union[str, Any]:
				__lowercase								=					0
				__lowercase								=					len(grid[0]			)

				for i in range(len(__lowerCAmelCase			)			):
								__lowercase								=					find_negative_index(grid[i][:bound]			)
								total += bound
				return (len(__lowerCAmelCase			) * len(grid[0]			)) - total




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Union[str, Any]			)     ->						Dict:
				return len([number for row in grid for number in row if number < 0]			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]			)     ->						Optional[Any]:
				__lowercase								=					0
				for row in grid:
								for i, number in enumerate(__lowerCAmelCase			):
												if number < 0:
																total += len(__lowerCAmelCase			) - i
																break
				return total




def 							__SCREAMING_SNAKE_CASE   (		)     ->						Optional[int]:
				from timeit import timeit

				print('Running benchmarks'			)
				__lowercase								=					(
				    "from __main__ import count_negatives_binary_search, "
				    "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
				)
				for func in (
				    "count_negatives_binary_search",  # took 0.7727 seconds
				    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
				    "count_negatives_brute_force",  # took 12.8160 seconds
				):
								__lowercase								=					timeit(F"""{func}(grid=grid)"""    ,							setup=__lowerCAmelCase    ,							number=500			)
								print(F"""{func}() took {time:0.4f} seconds"""			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()









'''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_bert import BertTokenizer


lowerCamelCase__					=   logging.get_logger(__name__)

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

lowerCamelCase__					=   {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__					=   {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

lowerCamelCase__					=   {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Union[str, Any]				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Dict				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Tuple				      =   PRETRAINED_INIT_CONFIGURATION
			lowerCAmelCase       :		Optional[int]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Any				      =   BertTokenizer




			def __init__(						self     :   int		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Optional[int]=None		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Tuple="[UNK]"		,     lowerCamelCase__     :   str="[SEP]"		,     lowerCamelCase__     :   Optional[Any]="[PAD]"		,     lowerCamelCase__     :   List[str]="[CLS]"		,     lowerCamelCase__     :   Union[str, Any]="[MASK]"		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Dict=None		,     **lowerCamelCase__     :   Union[str, Any]		,     )      ->Tuple:



					'''simple docstring'''







					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__		,     )

					_UpperCAmelCase						:				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
					):
							_UpperCAmelCase						:				str	    =					getattr(lowerCamelCase__		,     normalizer_state.pop("type"       )       )
							_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
							_UpperCAmelCase						:				Any	    =					strip_accents
							_UpperCAmelCase						:				List[Any]	    =					tokenize_chinese_chars
							_UpperCAmelCase						:				int	    =					normalizer_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					do_lower_case




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   str=None       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					[self.sep_token_id]
					_UpperCAmelCase						:				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     :   List[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._tokenizer.model.save(lowerCamelCase__		,     name=lowerCamelCase__       )
					return tuple(lowerCamelCase__       )



