
'''simple docstring'''

import argparse
import copy






def 	_snake_case						(      A_       :    Union[str, Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							{}

  with open(A_   ) as f:
    for line in f:
      if line.split()[0] not in dict_of_neighbours:
        a_      :	Optional[int]      =							[]
        _list.append([line.split()[1], line.split()[2]]   )
        a_      :	Optional[int]      =							_list
      else:
        dict_of_neighbours[line.split()[0]].append(
            [line.split()[1], line.split()[2]]   )
      if line.split()[1] not in dict_of_neighbours:
        a_      :	Union[str, Any]      =							[]
        _list.append([line.split()[0], line.split()[2]]   )
        a_      :	Union[str, Any]      =							_list
      else:
        dict_of_neighbours[line.split()[1]].append(
            [line.split()[0], line.split()[2]]   )

  return dict_of_neighbours






def 	_snake_case						(      A_       :    str     ,		A_       :    Union[str, Any]   ):
  """simple docstring"""







  with open(A_   ) as f:
    a_      :	str      =							f.read(1   )
  a_      :	Any      =							start_node

  a_      :	Optional[Any]      =							[]

  a_      :	int      =							start_node

  a_      :	List[Any]      =							0
  while visiting not in first_solution:
    a_      :	Any      =							1_0000
    for k in dict_of_neighbours[visiting]:
      if int(k[1]   ) < int(A_   ) and k[0] not in first_solution:
        a_      :	Union[str, Any]      =							k[1]
        a_      :	List[Any]      =							k[0]

    first_solution.append(A_   )
    a_      :	Union[str, Any]      =							distance_of_first_solution + int(A_   )
    a_      :	int      =							best_node

  first_solution.append(A_   )

  a_      :	List[Any]      =							0
  for k in dict_of_neighbours[first_solution[-2]]:
    if k[0] == start_node:
      break
    position += 1

  a_      :	List[str]      =							(
      distance_of_first_solution
      + int(dict_of_neighbours[first_solution[-2]][position][1]   )
      - 1_0000
  )
  return first_solution, distance_of_first_solution






def 	_snake_case						(      A_       :    Dict     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	Dict      =							[]

  for n in solution[1:-1]:
    a_      :	Optional[int]      =							solution.index(A_   )
    for kn in solution[1:-1]:
      a_      :	List[Any]      =							solution.index(A_   )
      if n == kn:
        continue

      a_      :	Any      =							copy.deepcopy(A_   )
      a_      :	Dict      =							kn
      a_      :	Dict      =							n

      a_      :	List[Any]      =							0

      for k in _tmp[:-1]:
        a_      :	Union[str, Any]      =							_tmp[_tmp.index(A_   ) + 1]
        for i in dict_of_neighbours[k]:
          if i[0] == next_node:
            a_      :	List[str]      =							distance + int(i[1]   )
      _tmp.append(A_   )

      if _tmp not in neighborhood_of_solution:
        neighborhood_of_solution.append(_tmp   )

  a_      :	Any      =							len(neighborhood_of_solution[0]   ) - 1

  neighborhood_of_solution.sort(key=lambda A_   : x[index_of_last_item_in_the_list]   )
  return neighborhood_of_solution






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    int     ,		A_       :    Optional[int]     ,		A_       :    List[str]     ,		A_       :    str   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							1
  a_      :	Tuple      =							first_solution
  a_      :	Optional[Any]      =							[]
  a_      :	int      =							distance_of_first_solution
  a_      :	Any      =							solution

  while count <= iters:
    a_      :	str      =							find_neighborhood(A_     ,		A_   )
    a_      :	Any      =							0
    a_      :	List[str]      =							neighborhood[index_of_best_solution]
    a_      :	Dict      =							len(A_   ) - 1

    a_      :	Optional[int]      =							False
    while not found:
      a_      :	Tuple      =							0
      while i < len(A_   ):
        if best_solution[i] != solution[i]:
          a_      :	Union[str, Any]      =							best_solution[i]
          a_      :	str      =							solution[i]
          break
        a_      :	Union[str, Any]      =							i + 1

      if [first_exchange_node, second_exchange_node] not in tabu_list and [
          second_exchange_node,
          first_exchange_node,
      ] not in tabu_list:
        tabu_list.append([first_exchange_node, second_exchange_node]   )
        a_      :	Tuple      =							True
        a_      :	List[str]      =							best_solution[:-1]
        a_      :	int      =							neighborhood[index_of_best_solution][best_cost_index]
        if cost < best_cost:
          a_      :	Union[str, Any]      =							cost
          a_      :	Dict      =							solution
      else:
        a_      :	Tuple      =							index_of_best_solution + 1
        a_      :	Optional[Any]      =							neighborhood[index_of_best_solution]

    if len(A_   ) >= size:
      tabu_list.pop(0   )

    a_      :	Optional[int]      =							count + 1

  return best_solution_ever, best_cost






def 	_snake_case						(      A_       :    int=None   ):
  """simple docstring"""







  a_      :	Optional[int]      =							generate_neighbours(args.File   )

  a_      :	str      =							generate_first_solution(
      args.File     ,		A_   )

  a_      :	str      =							tabu_search(
      A_     ,		A_     ,		A_     ,		args.Iterations     ,		args.Size     ,		)

  print(f'''Best solution: {best_sol}, with total distance: {best_cost}.'''   )


if __name__ == "__main__":
    __snake_case:  Any								=			argparse.ArgumentParser(description="Tabu Search")
    parser.add_argument(
        "-f",
        "--File",
        type=str,
        help="Path to the file containing the data",
        required=True,
    )
    parser.add_argument(
        "-i",
        "--Iterations",
        type=int,
        help="How many iterations the algorithm should perform",
        required=True,
    )
    parser.add_argument(
        "-s", "--Size", type=int, help="Size of the tabu list", required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())

'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

       a_      :	List[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							{
           """num_attention_heads""": 2,
           """attention_head_dim""": 4,
           """num_layers""": 2,
           """embedding_dim""": 8,
           """num_embeddings""": 7,
           """additional_embeddings""": 4,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							PriorTransformer.from_pretrained(
           """hf-internal-testing/prior-dummy"""							,		output_loading_info=lowerCAmelCase_ )
       self.assertIsNotNone(lowerCAmelCase_ )
       self.assertEqual(len(loading_info["""missing_keys"""] )							,		0 )

       model.to(lowerCAmelCase_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

       assert hidden_states is not None, "Make sure output is not None"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

       a_      :	str      =							self.model_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       a_      :	str      =							[*signature.parameters.keys()]

       a_      :	str      =							["""hidden_states""", """timestep"""]
       self.assertListEqual(arg_names[:2]							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
       a_      :	Optional[Any]      =							model.to(lowerCAmelCase_ )

       if hasattr(lowerCAmelCase_							,		"""set_default_attn_processor""" ):
         model.set_default_attn_processor()

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							output[0, :5].flatten().cpu()
       print(lowerCAmelCase_ )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
             [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
             # fmt: on
         ] )
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,		subfolder="""prior""" )
       model.to(lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.get_dummy_seed_input(seed=lowerCAmelCase_ )

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

       assert torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		atol=1E-3 )


