



"""simple docstring"""
import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def      __magic_name__				(							lowercase							,			lowercase = True							,			lowercase = math.inf							,			lowercase = -math.inf							,			lowercase = math.inf							,			lowercase = -math.inf							,			lowercase = False							,			lowercase = 100							,			lowercase = 0.01							,			lowercase = 1							,			):
   SCREAMING_SNAKE_CASE_: int     			=False
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=search_prob
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=start_temperate
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: str     			=0
   SCREAMING_SNAKE_CASE_: Optional[int]     			=None

   while not search_end:
      SCREAMING_SNAKE_CASE_: int     			=current_state.score()
      if best_state is None or current_score > best_state.score():
         SCREAMING_SNAKE_CASE_: Optional[int]     			=current_state
      scores.append(lowercase )
      iterations += 1
      SCREAMING_SNAKE_CASE_: int     			=None
      SCREAMING_SNAKE_CASE_: int     			=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_: Dict     			=random.randint(0							,			len(lowercase ) - 1 )  # picking a random neighbor
         SCREAMING_SNAKE_CASE_: int     			=neighbors.pop(lowercase )
         SCREAMING_SNAKE_CASE_: Tuple     			=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_: Tuple     			=change * -1  # in case we are finding minimum
         if change > 0:  # improves the solution
            SCREAMING_SNAKE_CASE_: Optional[int]     			=picked_neighbor
         else:
            SCREAMING_SNAKE_CASE_: str     			=(math.e) ** (
                change / current_temp
            )  # probability generation function
            if random.random() < probability:  # random number within probability
               SCREAMING_SNAKE_CASE_: Optional[int]     			=picked_neighbor
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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_: Union[str, 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      __magic_name__				(							lowercase							,			lowercase ):
      return (x**2) + (y**2)

   # starting the problem with initial coordinates (12, 47)
   _UpperCAmelCase     =			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
   _UpperCAmelCase     =			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)
   _UpperCAmelCase     =			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
   _UpperCAmelCase     =			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      __magic_name__				(							lowercase							,			lowercase ):
      return (3 * x**2) - (6 * y)

   _UpperCAmelCase     =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _UpperCAmelCase     =			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()}"""
   )

   _UpperCAmelCase     =			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _UpperCAmelCase     =			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()}"""
   )








"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser(
       description=(
           """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
           """ Distillation"""
       )
   )
   parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
   parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
   parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
   parser.add_argument("""--vocab_transform""", action="""store_true""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

   for w in ["word_embeddings", "position_embeddings"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _UpperCAmelCase     =			0
   for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



