


"""simple docstring"""



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

UpperCAmelCase      		=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def       lowerCamelCase					(a_						:str)     ->							Optional[Any]:
       for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     lowercase		:List[Any]       							=				model_type_to_module_name(a_)

                     lowercase		:Optional[Any]       							=				importlib.import_module(F""".{module_name}"""				,				'''transformers.models''')
                     try:
                            return getattr(a_				,				a_)
                     except AttributeError:
                            continue

       for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
              if getattr(a_				,				'''__name__'''				,				a_) == class_name:
                     return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
       lowercase		:Union[str, Any]       							=				importlib.import_module('''transformers''')
       if hasattr(a_				,				a_):
              return getattr(a_				,				a_)

       return None






def       lowerCamelCase					(a_						:Union[str, os.PathLike]				,				a_						:Optional[Union[str, os.PathLike]] = None				,				a_						:bool = False				,				a_						:bool = False				,				a_						:Optional[Dict[str, str]] = None				,				a_						:Optional[Union[bool, str]] = None				,				a_						:Optional[str] = None				,				a_						:bool = False				,				**a_						:List[str]				,				)     ->							List[str]:
       lowercase		:Optional[int]       							=				get_file_from_repo(
           a_				,				a_				,				cache_dir=a_				,				force_download=a_				,				resume_download=a_				,				proxies=a_				,				use_auth_token=a_				,				revision=a_				,				local_files_only=a_				,				)
       if resolved_config_file is None:
              logger.info(
                  '''Could not locate the image processor configuration file, will try to use the model config instead.''')
              return {}

       with open(a_				,				encoding='''utf-8''') as reader:
              return json.load(a_)




class 					__magic_name__		:






       def __init__(   self  :    Any				):

              '''simple docstring'''






              raise EnvironmentError(
                  '''AutoImageProcessor is designed to be instantiated '''
                  '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)






       @classmethod
       @replace_list_option_in_docstrings(snake_case__				)
       def  __snake_case						(   cls  :    Dict  ,     snake_case__  :    Optional[int]  ,     **snake_case__  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:int       							=				kwargs.pop('''config'''  ,     snake_case__				)
              lowercase		:List[str]       							=				kwargs.pop('''trust_remote_code'''  ,     snake_case__				)
              lowercase		:Tuple       							=				True

              lowercase							,  lowercase		:int       							=				ImageProcessingMixin.get_image_processor_dict(snake_case__  ,     **snake_case__				)
              lowercase		:Union[str, Any]       							=				config_dict.get('''image_processor_type'''  ,     snake_case__				)
              lowercase		:Optional[Any]       							=				None
              if "AutoImageProcessor" in config_dict.get('''auto_map'''  ,     {}				):
                     lowercase		:int       							=				config_dict['''auto_map''']['''AutoImageProcessor''']

              # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
              # and if so, infer the image processor class from there.
              if image_processor_class is None and image_processor_auto_map is None:
                     lowercase		:int       							=				config_dict.pop('''feature_extractor_type'''  ,     snake_case__				)
                     if feature_extractor_class is not None:
                            logger.warning(
                                '''Could not find image processor class in the image processor config or the model config. Loading'''
                                ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
                            lowercase		:Optional[int]       							=				feature_extractor_class.replace('''FeatureExtractor'''  ,     '''ImageProcessor'''				)
                     if "AutoFeatureExtractor" in config_dict.get('''auto_map'''  ,     {}				):
                            lowercase		:Any       							=				config_dict['''auto_map''']['''AutoFeatureExtractor''']
                            lowercase		:int       							=				feature_extractor_auto_map.replace('''FeatureExtractor'''  ,     '''ImageProcessor'''				)
                            logger.warning(
                                '''Could not find image processor auto map in the image processor config or the model config.'''
                                ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
              if image_processor_class is None and image_processor_auto_map is None:
                     if not isinstance(snake_case__  ,     snake_case__				):
                            lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(snake_case__  ,     **snake_case__				)
                     # It could be in `config.image_processor_type``
                     lowercase		:str       							=				getattr(snake_case__  ,     '''image_processor_type'''  ,     snake_case__				)
                     if hasattr(snake_case__  ,     '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
                            lowercase		:Union[str, Any]       							=				config.auto_map['''AutoImageProcessor''']

              if image_processor_class is not None:
                     lowercase		:Tuple       							=				image_processor_class_from_name(snake_case__				)

              lowercase		:List[Any]       							=				image_processor_auto_map is not None
              lowercase		:Any       							=				image_processor_class is not None or type(snake_case__				) in IMAGE_PROCESSOR_MAPPING
              lowercase		:Any       							=				resolve_trust_remote_code(
                  snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)

              if has_remote_code and trust_remote_code:
                     lowercase		:Optional[Any]       							=				get_class_from_dynamic_module(
                         snake_case__  ,     snake_case__  ,     **snake_case__				)
                     lowercase		:List[Any]       							=				kwargs.pop('''code_revision'''  ,     snake_case__				)
                     if os.path.isdir(snake_case__				):
                            image_processor_class.register_for_auto_class()
                     return image_processor_class.from_dict(snake_case__  ,     **snake_case__				)
              elif image_processor_class is not None:
                     return image_processor_class.from_dict(snake_case__  ,     **snake_case__				)
              # Last try: we use the IMAGE_PROCESSOR_MAPPING.
              elif type(snake_case__				) in IMAGE_PROCESSOR_MAPPING:
                     lowercase		:str       							=				IMAGE_PROCESSOR_MAPPING[type(snake_case__				)]
                     return image_processor_class.from_dict(snake_case__  ,     **snake_case__				)

              raise ValueError(
                  f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
                  f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
                  f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"""				)







       @staticmethod
       def  __snake_case						(   snake_case__  :    Tuple  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              IMAGE_PROCESSOR_MAPPING.register(snake_case__  ,     snake_case__				)





"""simple docstring"""



import argparse
import copy






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Tuple:
       lowercase		:Dict       							=				{}

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

       return dict_of_neighbours






def       lowerCamelCase					(a_						:Any				,				a_						:Dict)     ->							Any:

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

       lowercase		:Any       							=				[]

       lowercase		:Union[str, Any]       							=				start_node

       lowercase		:int       							=				0
       while visiting not in first_solution:
              lowercase		:int       							=				1_0000
              for k in dict_of_neighbours[visiting]:
                     if int(k[1]) < int(a_) and k[0] not in first_solution:
                            lowercase		:str       							=				k[1]
                            lowercase		:str       							=				k[0]

              first_solution.append(a_)
              lowercase		:int       							=				distance_of_first_solution + int(a_)
              lowercase		:Dict       							=				best_node

       first_solution.append(a_)

       lowercase		:int       							=				0
       for k in dict_of_neighbours[first_solution[-2]]:
              if k[0] == start_node:
                     break
              position += 1

       lowercase		: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       lowerCamelCase					(a_						:List[str]				,				a_						:str)     ->							str:
       lowercase		:Any       							=				[]

       for n in solution[1:-1]:
              lowercase		:int       							=				solution.index(a_)
              for kn in solution[1:-1]:
                     lowercase		:Union[str, Any]       							=				solution.index(a_)
                     if n == kn:
                            continue

                     lowercase		:int       							=				copy.deepcopy(a_)
                     lowercase		:str       							=				kn
                     lowercase		:List[Any]       							=				n

                     lowercase		:int       							=				0

                     for k in _tmp[:-1]:
                            lowercase		:Tuple       							=				_tmp[_tmp.index(a_) + 1]
                            for i in dict_of_neighbours[k]:
                                   if i[0] == next_node:
                                          lowercase		:Tuple       							=				distance + int(i[1])
                     _tmp.append(a_)

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

       lowercase		:Dict       							=				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       lowerCamelCase					(a_						:int				,				a_						:Optional[int]				,				a_						:List[Any]				,				a_						:Any				,				a_						:Optional[Any])     ->							List[Any]:
       lowercase		:Union[str, Any]       							=				1
       lowercase		:str       							=				first_solution
       lowercase		:int       							=				[]
       lowercase		:int       							=				distance_of_first_solution
       lowercase		:List[str]       							=				solution

       while count <= iters:
              lowercase		:Optional[Any]       							=				find_neighborhood(a_				,				a_)
              lowercase		:Any       							=				0
              lowercase		:Optional[Any]       							=				neighborhood[index_of_best_solution]
              lowercase		:int       							=				len(a_) - 1

              lowercase		:Dict       							=				False
              while not found:
                     lowercase		:List[str]       							=				0
                     while i < len(a_):
                            if best_solution[i] != solution[i]:
                                   lowercase		:Tuple       							=				best_solution[i]
                                   lowercase		:Tuple       							=				solution[i]
                                   break
                            lowercase		:List[str]       							=				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])
                            lowercase		:Tuple       							=				True
                            lowercase		:Optional[int]       							=				best_solution[:-1]
                            lowercase		:Any       							=				neighborhood[index_of_best_solution][best_cost_index]
                            if cost < best_cost:
                                   lowercase		:Union[str, Any]       							=				cost
                                   lowercase		:Optional[Any]       							=				solution
                     else:
                            lowercase		:Dict       							=				index_of_best_solution + 1
                            lowercase		:int       							=				neighborhood[index_of_best_solution]

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

              lowercase		:int       							=				count + 1

       return best_solution_ever, best_cost






def       lowerCamelCase					(a_						:Tuple=None)     ->							Any:
       lowercase		:Tuple       							=				generate_neighbours(args.File)

       lowercase							,  lowercase		:List[str]       							=				generate_first_solution(
           args.File				,				a_)

       lowercase							,  lowercase		:Union[str, Any]       							=				tabu_search(
           a_				,				a_				,				a_				,				args.Iterations				,				args.Size				,				)

       print(F"""Best solution: {best_sol}, with total distance: {best_cost}.""")


if __name__ == "__main__":
 UpperCAmelCase      		=			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())

