
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler





class       _snake_case					(     lowercase_				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a						,   ):
				super().__init__()
				__magic_name__		:		Any	   =     value_function
				__magic_name__		:		List[Any]	   =     unet
				__magic_name__		:		Tuple	   =     scheduler
				__magic_name__		:		str	   =     env
				__magic_name__		:		List[str]	   =     env.get_dataset()
				__magic_name__		:		List[Any]	   =     {}
				for key in self.data.keys():
					try:
						__magic_name__		:		str	   =     self.data[key].mean()
					except:  # noqa: E722
						pass
				__magic_name__		:		int	   =     {}
				for key in self.data.keys():
					try:
						__magic_name__		:		List[str]	   =     self.data[key].std()
					except:  # noqa: E722
						pass
				__magic_name__		:		Any	   =     env.observation_space.shape[0]
				__magic_name__		:		Any	   =     env.action_space.shape[0]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				return (x_in - self.means[key]) / self.stds[key]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				return x_in * self.stds[key] + self.means[key]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if type(_a		) is dict:
					return {k: self.to_torch(_a		) for k, v in x_in.items()}
				elif torch.is_tensor(_a		):
					return x_in.to(self.unet.device		)
				return torch.tensor(_a						,   device=self.unet.device		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				for key, val in cond.items():
					__magic_name__		:		List[Any]	   =     val.clone()
				return x_in







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     x.shape[0]
				__magic_name__		:		Optional[Any]	   =     None
				for i in tqdm.tqdm(self.scheduler.timesteps		):
					# create batch of timesteps to pass into model
					__magic_name__		:		Optional[int]	   =     torch.full((batch_size,)						,   _a						,   device=self.unet.device						,   dtype=torch.long		)
					for _ in range(_a		):
						with torch.enable_grad():
							x.requires_grad_()

							# permute to match dimension for pre-trained models
							__magic_name__		:		Any	   =     self.value_function(x.permute(0						,   2						,   1		)						,   _a		).sample
							__magic_name__		:		Optional[Any]	   =     torch.autograd.grad([y.sum()]						,   [x]		)[0]

							__magic_name__		:		List[str]	   =     self.scheduler._get_variance(_a		)
							__magic_name__		:		Any	   =     torch.exp(0.5 * posterior_variance		)
							__magic_name__		:		Tuple	   =     model_std * grad

						__magic_name__		:		Tuple	   =     0
						__magic_name__		:		Dict	   =     x.detach()
						__magic_name__		:		Optional[int]	   =     x + scale * grad
						__magic_name__		:		str	   =     self.reset_xa(_a						,   _a						,   self.action_dim		)

					__magic_name__		:		str	   =     self.unet(x.permute(0						,   2						,   1		)						,   _a		).sample.permute(0						,   2						,   1		)

					# TODO: verify deprecation of this kwarg
					__magic_name__		:		Tuple	   =     self.scheduler.step(_a						,   _a						,   _a						,   predict_epsilon=_a		)["prev_sample"]

					# apply conditions to the trajectory (set the initial state)
					__magic_name__		:		Union[str, Any]	   =     self.reset_xa(_a						,   _a						,   self.action_dim		)
					__magic_name__		:		Optional[int]	   =     self.to_torch(_a		)
				return x, y







			def __call__(				self						,   _a						,   _a=64						,   _a=32						,   _a=2						,   _a=0.1		):
				# normalize the observations and create  batch dimension
				__magic_name__		:		Optional[Any]	   =     self.normalize(_a						,   "observations"		)
				__magic_name__		:		Optional[int]	   =     obs[None].repeat(_a						,   axis=0		)

				__magic_name__		:		List[str]	   =     {0: self.to_torch(_a		)}
				__magic_name__		:		Tuple	   =     (batch_size, planning_horizon, self.state_dim + self.action_dim)

				# generate initial noise and apply our conditions (to make the trajectories start at current state)
				__magic_name__		:		Optional[int]	   =     randn_tensor(_a						,   device=self.unet.device		)
				__magic_name__		:		str	   =     self.reset_xa(_a						,   _a						,   self.action_dim		)
				__magic_name__		:		Tuple	   =     self.to_torch(_a		)

				# run the diffusion process
				__magic_name__   ,    __magic_name__		:		int	   =     self.run_diffusion(_a						,   _a						,   _a						,   _a		)

				# sort output trajectories by value
				__magic_name__		:		Any	   =     y.argsort(0						,   descending=_a		).squeeze()
				__magic_name__		:		Union[str, Any]	   =     x[sorted_idx]
				__magic_name__		:		str	   =     sorted_values[:, :, : self.action_dim]
				__magic_name__		:		Union[str, Any]	   =     actions.detach().cpu().numpy()
				__magic_name__		:		Tuple	   =     self.de_normalize(_a						,   key="actions"		)

				# select the action with the highest value
				if y is not None:
					__magic_name__		:		int	   =     0
				else:
					# if we didn't run value guiding, select a random action
					__magic_name__		:		Union[str, Any]	   =     np.random.randint(0						,   _a		)

				__magic_name__		:		Dict	   =     denorm_actions[selected_index, 0]
				return denorm_actions



def 					__lowerCamelCase	( ):







							'''simple docstring'''






							return [list(range(1000 - i   ,							-1000 - i   ,							-1      )      ) for i in range(1000      )]


_UpperCAmelCase		:   Union[str, Any]										=       generate_large_matrix()
_UpperCAmelCase		:   Tuple										=       (
    [[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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    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:
														snake_case_							=    (left + right) // 2
														snake_case_							=    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:
																					snake_case_							=    mid + 1
														else:
																					snake_case_							=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(grid[0]      )

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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return len([number for row in grid for number in row if number < 0]      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							for row in grid:
														for i, number in enumerate(UpperCamelCase__      ):
																					if number < 0:
																												total += len(UpperCamelCase__      ) - i
																												break
							return total





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							from timeit import timeit

							print('Running benchmarks'      )
							snake_case_							=    (
							    '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
							):
														snake_case_							=    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()




