
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def     __lowerCAmelCase			(			a__						,    a__ = True						,    a__ = math.inf						,    a__ = -math.inf						,    a__ = math.inf						,    a__ = -math.inf						,    a__ = False						,    a__ = 100						,    a__ = 0.01						,    a__ = 1						,    )       ->  Any:
				__a										=    False
				__a										=    search_prob
				__a										=    start_temperate
				__a										=    []
				__a										=    0
				__a										=    None

				while not search_end:
								__a										=    current_state.score()
								if best_state is None or current_score > best_state.score():
												__a										=    current_state
								scores.append(a__  )
								iterations += 1
								__a										=    None
								__a										=    current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												__a										=    random.randint(0						,    len(a__  ) - 1  )  # picking a random neighbor
												__a										=    neighbors.pop(a__  )
												__a										=    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:
																__a										=    change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																__a										=    picked_neighbor
												else:
																__a										=    (math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				__a										=    picked_neighbor
								__a										=    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
												__a										=    True
								else:
												__a										=    next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(a__  )						,    a__  )
								plt.xlabel('''Iterations'''  )
								plt.ylabel('''Function values'''  )
								plt.show()
				return best_state


if __name__ == "__main__":


					def     __lowerCAmelCase			(			a__						,    a__  )       ->  List[Any]:
									return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					A     :	Tuple    = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
					A     :	int    = 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)
					A     :	List[str]    = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
					A     :	Union[str, Any]    = 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			(			a__						,    a__  )       ->  Any:
									return (3 * x**2) - (6 * y)

					A     :	Dict    = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					A     :	Optional[int]    = 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()}"
					)

					A     :	Tuple    = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					A     :	str    = 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 ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


