
import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   List[str] = True      ,					UpperCAmelCase__					:   Tuple = math.inf      ,					UpperCAmelCase__					:   Union[str, Any] = -math.inf      ,					UpperCAmelCase__					:   List[str] = math.inf      ,					UpperCAmelCase__					:   Optional[Any] = -math.inf      ,					UpperCAmelCase__					:   List[Any] = False      ,					UpperCAmelCase__					:   Tuple = 1_0_0      ,					UpperCAmelCase__					:   Union[str, Any] = 0.0_1      ,					UpperCAmelCase__					:   List[str] = 1      ,					)	->     Any:
							lowerCamelCase_					=       False
							lowerCamelCase_					=       search_prob
							lowerCamelCase_					=       start_temperate
							lowerCamelCase_					=       []
							lowerCamelCase_					=       0
							lowerCamelCase_					=       None

							while not search_end:
														lowerCamelCase_					=       current_state.score()
														if best_state is None or current_score > best_state.score():
																					lowerCamelCase_					=       current_state
														scores.append(snake_case__							)
														iterations += 1
														lowerCamelCase_					=       None
														lowerCamelCase_					=       current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					lowerCamelCase_					=       random.randint(0      ,					len(snake_case__							) - 1							)  # picking a random neighbor
																					lowerCamelCase_					=       neighbors.pop(snake_case__							)
																					lowerCamelCase_					=       picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												lowerCamelCase_					=       change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												lowerCamelCase_					=       picked_neighbor
																					else:
																												lowerCamelCase_					=       (math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			lowerCamelCase_					=       picked_neighbor
														lowerCamelCase_					=       current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					lowerCamelCase_					=       True
														else:
																					lowerCamelCase_					=       next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(snake_case__							)      ,					snake_case__							)
														plt.xlabel("""Iterations"""							)
														plt.ylabel("""Function values"""							)
														plt.show()
							return best_state


if __name__ == "__main__":



		def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   List[str]							)	->     List[Any]:
									return (x**2) + (y**2)

		# starting the problem with initial coordinates (12, 47)
		lowercase   =	SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		lowercase   =	simulated_annealing(
		    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
		)
		print(
		    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
		)

		# starting the problem with initial coordinates (12, 47)
		lowercase   =	SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		lowercase   =	simulated_annealing(
		    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
		)
		print(
		    '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
		)



		def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   str							)	->     Tuple:
									return (3 * x**2) - (6 * y)

		lowercase   =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		lowercase   =	simulated_annealing(prob, find_max=False, visualization=True)
		print(
		    '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    F"""{local_min.score()}"""
		)

		lowercase   =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		lowercase   =	simulated_annealing(prob, find_max=True, visualization=True)


		print(
		    '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    F"""{local_min.score()}"""
		)










from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	42



class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE ):
		@register_to_config
		def __init__(							self    ,a_ = 16    ,a_ = 88    ,a_ = None    ,a_ = None    ,a_ = 1    ,a_ = 0.0    ,a_ = 32    ,a_ = None    ,a_ = False    ,a_ = None    ,a_ = "geglu"    ,a_ = True    ,a_ = True    ,):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  attention_head_dim
						lowerCAmelCase__		  =  num_attention_heads * attention_head_dim

						lowerCAmelCase__		  =  in_channels

						lowerCAmelCase__		  =  torch.nn.GroupNorm(num_groups=a_    ,num_channels=a_    ,eps=1e-6    ,affine=a_						)
						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)

						# 3. Define transformers blocks
						lowerCAmelCase__		  =  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            a_    ,a_    ,a_    ,dropout=a_    ,cross_attention_dim=a_    ,activation_fn=a_    ,attention_bias=a_    ,double_self_attention=a_    ,norm_elementwise_affine=a_    ,)
						        for d in range(a_						)
						    ]						)

						lowerCAmelCase__		  =  nn.Linear(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=None    ,a_=None    ,a_=None    ,a_=1    ,a_=None    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  hidden_states.shape
						lowerCAmelCase__		  =  batch_frames // num_frames

						lowerCAmelCase__		  =  hidden_states

						lowerCAmelCase__		  =  hidden_states[None, :].reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,2    ,1    ,3    ,4						)

						lowerCAmelCase__		  =  self.norm(a_						)
						lowerCAmelCase__		  =  hidden_states.permute(0    ,3    ,4    ,2    ,1						).reshape(batch_size * height * width    ,a_    ,a_						)

						lowerCAmelCase__		  =  self.proj_in(a_						)

						# 2. Blocks
						for block in self.transformer_blocks:
										lowerCAmelCase__		  =  block(
										    a_    ,encoder_hidden_states=a_    ,timestep=a_    ,cross_attention_kwargs=a_    ,class_labels=a_    ,)

						# 3. Output
						lowerCAmelCase__		  =  self.proj_out(a_						)
						lowerCAmelCase__		  =  (
						    hidden_states[None, None, :]
						    .reshape(a_    ,a_    ,a_    ,a_    ,a_						)
						    .permute(0    ,3    ,4    ,1    ,2						)
						    .contiguous()
						)
						lowerCAmelCase__		  =  hidden_states.reshape(a_    ,a_    ,a_    ,a_						)

						lowerCAmelCase__		  =  hidden_states + residual

						if not return_dict:
										return (output,)

						return TransformerTemporalModelOutput(sample=a_						)
