






'''simple docstring'''







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


_A     :      Tuple   					=generate_large_matrix()
_A     :      str   					=(
    [[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_	(UpperCamelCase				)				->	None:
							assert all(row == sorted(UpperCamelCase    ,      reverse=UpperCamelCase				) for row in grid				)
							assert all(list(UpperCamelCase				) == sorted(UpperCamelCase    ,      reverse=UpperCamelCase				) for col in zip(*UpperCamelCase				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     int	  =	len(UpperCamelCase				) - 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:
														lowerCamelCase__ :     int	  =	(left + right) // 2
														lowerCamelCase__ :     str	  =	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:
																					lowerCamelCase__ :     Dict	  =	mid + 1
														else:
																					lowerCamelCase__ :     Dict	  =	mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Tuple	  =	0
							lowerCamelCase__ :     List[str]	  =	len(grid[0]				)

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							return len([number for row in grid for number in row if number < 0]				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Union[str, Any]	  =	0
							for row in grid:
														for i, number in enumerate(UpperCamelCase				):
																					if number < 0:
																												total += len(UpperCamelCase				) - i
																												break
							return total





def 			SCREAMING_SNAKE_CASE_	()				->	None:
							from timeit import timeit

							print("""Running benchmarks"""				)
							lowerCamelCase__ :     Union[str, Any]	  =	(
							    """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
							):
														lowerCamelCase__ :     int	  =	timeit(f'''{func}(grid=grid)'''    ,      setup=UpperCamelCase    ,      number=500				)
														print(f'''{func}() took {time:0.4f} seconds'''				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()









'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     Any	  =	tempfile.mkdtemp()

														lowerCamelCase__ :     Any	  =	[
														    """[UNK]""",
														    """[CLS]""",
														    """[SEP]""",
														    """[PAD]""",
														    """[MASK]""",
														    """的""",
														    """价""",
														    """格""",
														    """是""",
														    """15""",
														    """便""",
														    """alex""",
														    """##andra""",
														    """，""",
														    """。""",
														    """-""",
														    """t""",
														    """shirt""",
														]
														lowerCamelCase__ :     Optional[int]	  =	os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]					)
														with open(self.vocab_file      ,							"""w"""      ,							encoding="""utf-8"""					) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

														lowerCamelCase__ :     Union[str, Any]	  =	{
														    """do_resize""": True,
														    """size""": {"""height""": 224, """width""": 224},
														    """do_center_crop""": True,
														    """crop_size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
														    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
														    """do_convert_rgb""": True,
														}
														lowerCamelCase__ :     Optional[int]	  =	os.path.join(self.tmpdirname      ,							UpperCamelCase__					)
														with open(self.image_processor_file      ,							"""w"""      ,							encoding="""utf-8"""					) as fp:
																					json.dump(UpperCamelCase__      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							**UpperCamelCase__:    Optional[int]					):
														return BertTokenizer.from_pretrained(self.tmpdirname      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any      ,							**UpperCamelCase__:    List[str]					):
														return BertTokenizerFast.from_pretrained(self.tmpdirname      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str      ,							**UpperCamelCase__:    int					):
														return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														shutil.rmtree(self.tmpdirname					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     int	  =	[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)]

														lowerCamelCase__ :     List[str]	  =	[Image.fromarray(np.moveaxis(UpperCamelCase__      ,							0      ,							-1					)					) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Tuple	  =	self.get_tokenizer()
														lowerCamelCase__ :     Any	  =	self.get_rust_tokenizer()
														lowerCamelCase__ :     str	  =	self.get_image_processor()

														lowerCamelCase__ :     str	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)
														processor_slow.save_pretrained(self.tmpdirname					)
														lowerCamelCase__ :     List[Any]	  =	ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,							use_fast=UpperCamelCase__					)

														lowerCamelCase__ :     Any	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)
														processor_fast.save_pretrained(self.tmpdirname					)
														lowerCamelCase__ :     List[Any]	  =	ChineseCLIPProcessor.from_pretrained(self.tmpdirname					)

														self.assertEqual(processor_slow.tokenizer.get_vocab()      ,							tokenizer_slow.get_vocab()					)
														self.assertEqual(processor_fast.tokenizer.get_vocab()      ,							tokenizer_fast.get_vocab()					)
														self.assertEqual(tokenizer_slow.get_vocab()      ,							tokenizer_fast.get_vocab()					)
														self.assertIsInstance(processor_slow.tokenizer      ,							UpperCamelCase__					)
														self.assertIsInstance(processor_fast.tokenizer      ,							UpperCamelCase__					)

														self.assertEqual(processor_slow.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
														self.assertEqual(processor_fast.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
														self.assertIsInstance(processor_slow.image_processor      ,							UpperCamelCase__					)
														self.assertIsInstance(processor_fast.image_processor      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Optional[int]	  =	ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()					)
														processor.save_pretrained(self.tmpdirname					)

														lowerCamelCase__ :     str	  =	self.get_tokenizer(cls_token="""(CLS)"""      ,							sep_token="""(SEP)"""					)
														lowerCamelCase__ :     Optional[int]	  =	self.get_image_processor(do_normalize=UpperCamelCase__					)

														lowerCamelCase__ :     Optional[int]	  =	ChineseCLIPProcessor.from_pretrained(
														    self.tmpdirname      ,							cls_token="""(CLS)"""      ,							sep_token="""(SEP)"""      ,							do_normalize=UpperCamelCase__					)

														self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()					)
														self.assertIsInstance(processor.tokenizer      ,							UpperCamelCase__					)

														self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
														self.assertIsInstance(processor.image_processor      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Optional[Any]	  =	self.get_image_processor()
														lowerCamelCase__ :     Optional[int]	  =	self.get_tokenizer()

														lowerCamelCase__ :     int	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)

														lowerCamelCase__ :     Tuple	  =	self.prepare_image_inputs()

														lowerCamelCase__ :     str	  =	image_processor(UpperCamelCase__      ,							return_tensors="""np"""					)
														lowerCamelCase__ :     Optional[int]	  =	processor(images=UpperCamelCase__      ,							return_tensors="""np"""					)

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1e-2					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     int	  =	self.get_image_processor()
														lowerCamelCase__ :     Optional[int]	  =	self.get_tokenizer()

														lowerCamelCase__ :     Union[str, Any]	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)

														lowerCamelCase__ :     Union[str, Any]	  =	"""Alexandra，T-shirt的价格是15便士。"""

														lowerCamelCase__ :     Optional[Any]	  =	processor(text=UpperCamelCase__					)

														lowerCamelCase__ :     Optional[Any]	  =	tokenizer(UpperCamelCase__					)

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Optional[int]	  =	self.get_image_processor()
														lowerCamelCase__ :     List[Any]	  =	self.get_tokenizer()

														lowerCamelCase__ :     Optional[Any]	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	"""Alexandra，T-shirt的价格是15便士。"""
														lowerCamelCase__ :     Any	  =	self.prepare_image_inputs()

														lowerCamelCase__ :     Optional[int]	  =	processor(text=UpperCamelCase__      ,							images=UpperCamelCase__					)

														self.assertListEqual(list(inputs.keys()					)      ,							["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]					)

														# test if it raises when no input is passed
														with pytest.raises(UpperCamelCase__					):
																					processor()




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Dict	  =	self.get_image_processor()
														lowerCamelCase__ :     Any	  =	self.get_tokenizer()

														lowerCamelCase__ :     Union[str, Any]	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)

														lowerCamelCase__ :     str	  =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__ :     Any	  =	processor.batch_decode(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	tokenizer.batch_decode(UpperCamelCase__					)

														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Dict	  =	self.get_image_processor()
														lowerCamelCase__ :     int	  =	self.get_tokenizer()

														lowerCamelCase__ :     Optional[Any]	  =	ChineseCLIPProcessor(tokenizer=UpperCamelCase__      ,							image_processor=UpperCamelCase__					)

														lowerCamelCase__ :     Optional[Any]	  =	"""Alexandra，T-shirt的价格是15便士。"""
														lowerCamelCase__ :     Tuple	  =	self.prepare_image_inputs()

														lowerCamelCase__ :     str	  =	processor(text=UpperCamelCase__      ,							images=UpperCamelCase__					)

														self.assertListEqual(list(inputs.keys()					)      ,							processor.model_input_names					)

