




import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def lowerCamelCase__       (      _lowercase			,						_lowercase = True			,						_lowercase = math.inf			,						_lowercase = -math.inf			,						_lowercase = math.inf			,						_lowercase = -math.inf			,						_lowercase = False			,						_lowercase = 100			,						_lowercase = 0.01			,						_lowercase = 1			,						):

					'''simple docstring'''


					UpperCAmelCase_      :							Optional[int]       =      False
					UpperCAmelCase_      :							Union[str, Any]       =      search_prob
					UpperCAmelCase_      :							Optional[Any]       =      start_temperate
					UpperCAmelCase_      :							Optional[int]       =      []
					UpperCAmelCase_      :							str       =      0
					UpperCAmelCase_      :							Dict       =      None

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

										'''simple docstring'''


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

					# starting the problem with initial coordinates (12, 47)
					__a          =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					__a          =			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)
					__a          =			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					__a          =			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 lowerCamelCase__       (      _lowercase			,						_lowercase				):

										'''simple docstring'''


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

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





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.esm.modeling_esmfold import EsmForProteinFolding

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=19       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							Union[str, Any]       =      is_training
         UpperCAmelCase_      :							Any       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							List[Any]       =      vocab_size
         UpperCAmelCase_      :							List[Any]       =      hidden_size
         UpperCAmelCase_      :							str       =      num_hidden_layers
         UpperCAmelCase_      :							Dict       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[int]       =      max_position_embeddings
         UpperCAmelCase_      :							Optional[int]       =      type_vocab_size
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							int       =      initializer_range
         UpperCAmelCase_      :							Any       =      num_labels
         UpperCAmelCase_      :							Optional[Any]       =      num_choices
         UpperCAmelCase_      :							List[str]       =      scope





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							str       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							Optional[int]       =      random_attention_mask([self.batch_size, self.seq_length]  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         UpperCAmelCase_      :							List[str]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							Optional[Any]       =      self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      EsmConfig(
             vocab_size=33       ,hidden_size=self.hidden_size       ,pad_token_id=1       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,is_folding_model=_SCREAMING_SNAKE_CASE       ,esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,)
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      EsmForProteinFolding(config=_SCREAMING_SNAKE_CASE  ).float()
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.positions.shape       ,(8, self.batch_size, self.seq_length, 14, 3)  )
         self.parent.assertEqual(result.angles.shape       ,(8, self.batch_size, self.seq_length, 7, 2)  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Optional[Any]       =      config_and_inputs
         UpperCAmelCase_      :							int       =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				False

    lowerCAmelCase        =				(EsmForProteinFolding,) if is_torch_available() else ()
    lowerCAmelCase        =				()
    lowerCAmelCase        =				{} if is_torch_available() else {}
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      EsmFoldModelTester(self  )
         UpperCAmelCase_      :							int       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Tuple:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip('''Does not support attention outputs'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		Union[str, Any]:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold does not support passing input embeds!'''  )
    def 							a__				(				self  )			->		Any:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''ESMFold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		str:
         pass





    @unittest.skip('''ESMfold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold only has one output format.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''  )
    def 							a__				(				self  )			->		int:
         pass





    @unittest.skip('''ESMFold does not support input chunking.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		List[str]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support data parallel.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass


@require_torch
class   __a(					_a       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''  ).float()
         model.eval()
         UpperCAmelCase_      :							str       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE  )['''positions''']
         UpperCAmelCase_      :							List[str]       =      torch.tensor([2.58_28, 0.79_93, -10.93_34]       ,dtype=torch.floataa  )
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )