




"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		'longformer'


     def __init__(      self    ,				__UpperCAmelCase = 512    ,				__UpperCAmelCase = 2    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 0    ,				__UpperCAmelCase = 2    ,				__UpperCAmelCase = 30522    ,				__UpperCAmelCase = 768    ,				__UpperCAmelCase = 12    ,				__UpperCAmelCase = 12    ,				__UpperCAmelCase = 3072    ,				__UpperCAmelCase = "gelu"    ,				__UpperCAmelCase = 0.1    ,				__UpperCAmelCase = 0.1    ,				__UpperCAmelCase = 512    ,				__UpperCAmelCase = 2    ,				__UpperCAmelCase = 0.02    ,				__UpperCAmelCase = 1e-1_2    ,				__UpperCAmelCase = False    ,				**__UpperCAmelCase    ,				)       ->							str:
          super().__init__(pad_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          _a    	=				attention_window
          _a    	=				sep_token_id
          _a    	=				bos_token_id
          _a    	=				eos_token_id
          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				hidden_act
          _a    	=				intermediate_size
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				max_position_embeddings
          _a    	=				type_vocab_size
          _a    	=				initializer_range
          _a    	=				layer_norm_eps
          _a    	=				onnx_export







class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = "default"    ,				__UpperCAmelCase = None							)       ->							Any:
          super().__init__(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				True


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a    	=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a    	=				{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
                  ('''global_attention_mask''', dynamic_axis),
              ]							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Mapping[str, Mapping[int, str]]:
          _a    	=				super().outputs
          if self.task == "default":
               _a    	=				{0: '''batch'''}
          return outputs


     @property
     def 							_UpperCAmelCase      (      self							)       ->							float:
          return 1e-4


     @property
     def 							_UpperCAmelCase      (      self							)       ->							int:
          # needs to be >= 14 to support tril operator
          return max(super().default_onnx_opset    ,				14							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				)       ->							Mapping[str, Any]:
          _a    	=				super().generate_dummy_inputs(
              preprocessor=__UpperCAmelCase    ,				batch_size=__UpperCAmelCase    ,				seq_length=__UpperCAmelCase    ,				is_pair=__UpperCAmelCase    ,				framework=__UpperCAmelCase							)
          import torch

          # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
          # makes the export fail randomly
          _a    	=				torch.zeros_like(inputs['''input_ids''']							)
          # make every second token global
          _a    	=				1

          return inputs



"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					bool = True,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					float = math.inf,     _lowerCAmelCase     :					float = -math.inf,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					float = 1_00,     _lowerCAmelCase     :					float = 0.0_1,     _lowerCAmelCase     :					float = 1,     ):






     """simple docstring"""

     _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(_lowerCAmelCase       )
          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(_lowerCAmelCase       ) - 1       )  # picking a random neighbor
               _a    	=				neighbors.pop(_lowerCAmelCase       )
               _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(_lowerCAmelCase       ),     _lowerCAmelCase       )
          plt.xlabel('''Iterations'''       )
          plt.ylabel('''Function values'''       )
          plt.show()
     return best_state


if __name__ == "__main__":
   def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict       ):






        """simple docstring"""

        return (x**2) + (y**2)

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]       ):






        """simple docstring"""

        return (3 * x**2) - (6 * y)

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )