"""simple docstring"""



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase		:		int														=							logging.get_logger(__name__)

__lowercase		:		Any														=							{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					'''mvp'''
    UpperCamelCase_	:					Union[str, Any]							=					['''past_key_values''']
    UpperCamelCase_	:					List[str]							=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
    def __init__(		self							:     List[Any]	,      A_							:     Optional[Any]=50_267	,      A_							:     Union[str, Any]=1_024	,      A_							:     List[str]=12	,      A_							:     Dict=4_096	,      A_							:     int=16	,      A_							:     Optional[Any]=12	,      A_							:     Optional[int]=4_096	,      A_							:     Tuple=16	,      A_							:     Optional[Any]=0.0	,      A_							:     Any=0.0	,      A_							:     Optional[Any]="gelu"	,      A_							:     Tuple=1_024	,      A_							:     str=0.1	,      A_							:     List[str]=0.0	,      A_							:     Union[str, Any]=0.0	,      A_							:     Union[str, Any]=0.02	,      A_							:     str=0.0	,      A_							:     Optional[int]=False	,      A_							:     Any=True	,      A_							:     List[str]=1	,      A_							:     int=0	,      A_							:     Dict=2	,      A_							:     int=True	,      A_							:     Optional[int]=2	,      A_							:     Any=2	,      A_							:     Optional[int]=False	,      A_							:     Any=100	,      A_							:     str=800	,      **A_							:     Optional[Any]	,      )    ->       Any:
         __snake_case				=							vocab_size
         __snake_case				=							max_position_embeddings
         __snake_case				=							d_model
         __snake_case				=							encoder_ffn_dim
         __snake_case				=							encoder_layers
         __snake_case				=							encoder_attention_heads
         __snake_case				=							decoder_ffn_dim
         __snake_case				=							decoder_layers
         __snake_case				=							decoder_attention_heads
         __snake_case				=							dropout
         __snake_case				=							attention_dropout
         __snake_case				=							activation_dropout
         __snake_case				=							activation_function
         __snake_case				=							init_std
         __snake_case				=							encoder_layerdrop
         __snake_case				=							decoder_layerdrop
         __snake_case				=							classifier_dropout
         __snake_case				=							use_cache
         __snake_case				=							encoder_layers
         __snake_case				=							scale_embedding  # scale factor will be sqrt(d_model) if True
         __snake_case				=							use_prompt
         __snake_case				=							prompt_length
         __snake_case				=							prompt_mid_dim

         super().__init__(
             pad_token_id=A_	,      bos_token_id=A_	,      eos_token_id=A_	,      is_encoder_decoder=A_	,      decoder_start_token_id=A_	,      forced_eos_token_id=A_	,      **A_	,      )

         if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,      A_    ):
              __snake_case				=							self.bos_token_id
              warnings.warn(
                  f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                  '''The config can simply be saved and uploaded again to be fixed.'''    )
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							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:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							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
               __snake_case				=							True
          else:
               __snake_case				=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, 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		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, 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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							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		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							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()}"""
       )