





"""simple docstring"""






def 			__A					(	)						->						list[list[int]]:
				return [list(range(10_00 - i      ,				-10_00 - i      ,				-1)) for i in range(10_00)]


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 			__A					(	a_  :list[list[int]])						->						None:
				assert all(row == sorted(__A      ,				reverse=__A) for row in grid)
				assert all(list(__A) == sorted(__A      ,				reverse=__A) for col in zip(*__A))

def 			__A					(	a_  :list[int])						->						int:
				__a       :  str					=     0
				__a       :  Any					=     len(__A) - 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:
								__a       :  Any					=     (left + right) // 2
								__a       :  Optional[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:
												__a       :  Optional[Any]					=     mid + 1
								else:
												__a       :  int					=     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
				return len(__A)

def 			__A					(	a_  :list[list[int]])						->						int:
				__a       :  int					=     0
				__a       :  Optional[int]					=     len(grid[0])

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

def 			__A					(	a_  :list[list[int]])						->						int:
				return len([number for row in grid for number in row if number < 0])

def 			__A					(	a_  :list[list[int]])						->						int:
				__a       :  Tuple					=     0
				for row in grid:
								for i, number in enumerate(__A):
												if number < 0:
																total += len(__A) - i
																break
				return total

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

				print('''Running benchmarks''')
				__a       :  str					=     (
				    '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
				):
								__a       :  Any					=     timeit(F"""{func}(grid=grid)"""      ,				setup=__A      ,				number=5_00)
								print(F"""{func}() took {time:0.4f} seconds""")


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()



import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


