





import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  bool = True			,   _lowerCamelCase      :  float = math.inf			,   _lowerCamelCase      :  float = -math.inf			,   _lowerCamelCase      :  float = math.inf			,   _lowerCamelCase      :  float = -math.inf			,   _lowerCamelCase      :  bool = False			,   _lowerCamelCase      :  float = 100			,   _lowerCamelCase      :  float = 0.0_1			,   _lowerCamelCase      :  float = 1			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					False
						SCREAMING_SNAKE_CASE__     :				Any   =					search_prob
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					start_temperate
						SCREAMING_SNAKE_CASE__     :				str   =					[]
						SCREAMING_SNAKE_CASE__     :				List[str]   =					0
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None

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

						if visualization:
												from matplotlib import pyplot as plt

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


if __name__ == "__main__":







				def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Optional[int]     ):





										'''simple docstring'''


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

				# starting the problem with initial coordinates (12, 47)
				__lowercase       :Tuple       =  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
				__lowercase       :str       =  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       :int       =  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
				__lowercase       :Optional[Any]       =  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       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Optional[int]     ):





										'''simple docstring'''


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

				__lowercase       :Dict       =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				__lowercase       :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()}"
				)

				__lowercase       :str       =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				__lowercase       :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 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


__lowercase       :List[Any]       =  logging.get_logger(__name__)

__lowercase       :Optional[int]       =  {
    "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    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"longformer"

							def __init__(      self				:     List[str]							,	a				:     Union[List[int], int] = 5_12							,	a				:     int = 2							,	a				:     int = 1							,	a				:     int = 0							,	a				:     int = 2							,	a				:     int = 3_05_22							,	a				:     int = 7_68							,	a				:     int = 12							,	a				:     int = 12							,	a				:     int = 30_72							,	a				:     str = "gelu"							,	a				:     float = 0.1							,	a				:     float = 0.1							,	a				:     int = 5_12							,	a				:     int = 2							,	a				:     float = 0.02							,	a				:     float = 1E-12							,	a				:     bool = False							,	**a				:     Dict							,	)	->Tuple:
													super().__init__(pad_token_id=a							,	**a    )

													SCREAMING_SNAKE_CASE__     :				int   =					attention_window
													SCREAMING_SNAKE_CASE__     :				Any   =					sep_token_id
													SCREAMING_SNAKE_CASE__     :				str   =					bos_token_id
													SCREAMING_SNAKE_CASE__     :				List[str]   =					eos_token_id
													SCREAMING_SNAKE_CASE__     :				List[str]   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					hidden_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					num_hidden_layers
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					num_attention_heads
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					hidden_act
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					intermediate_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Dict   =					attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				str   =					type_vocab_size
													SCREAMING_SNAKE_CASE__     :				Any   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					layer_norm_eps
													SCREAMING_SNAKE_CASE__     :				Any   =					onnx_export







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     "PretrainedConfig"							,	a				:     str = "default"							,	a				:     "List[PatchingSpec]" = None    )	->str:
													super().__init__(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					True

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

							@property
							def      A_  (      self				:     Optional[Any]    )	->Mapping[str, Mapping[int, str]]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					super().outputs
													if self.task == "default":
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					{0: "batch"}
													return outputs

							@property
							def      A_  (      self				:     str    )	->float:
													return 1E-4

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






							def      A_  (      self				:     str							,	a				:     "PreTrainedTokenizerBase"							,	a				:     int = -1							,	a				:     int = -1							,	a				:     bool = False							,	a				:     Optional[TensorType] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					super().generate_dummy_inputs(
													    preprocessor=a							,	batch_size=a							,	seq_length=a							,	is_pair=a							,	framework=a    )
													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
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.zeros_like(inputs["input_ids"]    )
													# make every second token global
													SCREAMING_SNAKE_CASE__     :				str   =					1

													return inputs