

def        SCREAMING_SNAKE_CASE__	(  )  ->					list[list[int]]:
	return [list(range(1_0_0_0 - i     ,			-1_0_0_0 - i     ,			-1      )      ) for i in range(1_0_0_0      )]


a		=generate_large_matrix()
a		=(
    [[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__	(  lowerCamelCase__      )  ->					None:
	assert all(row == sorted(SCREAMING_SNAKE_CASE_     ,			reverse=SCREAMING_SNAKE_CASE_      ) for row in grid      )
	assert all(list(SCREAMING_SNAKE_CASE_      ) == sorted(SCREAMING_SNAKE_CASE_     ,			reverse=SCREAMING_SNAKE_CASE_      ) for col in zip(*SCREAMING_SNAKE_CASE_      )      )






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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Optional[Any] 	=			0
	__lowerCamelCase    :    int 	=			len(grid[0]      )

	for i in range(len(SCREAMING_SNAKE_CASE_      )      ):
		__lowerCamelCase    :    List[Any] 	=			find_negative_index(grid[i][:bound]      )
		total += bound
	return (len(SCREAMING_SNAKE_CASE_      ) * len(grid[0]      )) - total






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






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






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

	print('Running benchmarks'      )
	__lowerCamelCase    :    Optional[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    :    Tuple 	=			timeit(F"{func}(grid=grid)"     ,			setup=SCREAMING_SNAKE_CASE_     ,			number=5_0_0      )
		print(F"{func}() took {time:0.4f} seconds"      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,dtype=tf.floataa       ,)
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




