
'''simple docstring'''





import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


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

a__		: Optional[Any]  							=							OrderedDict(
    [
        ("align", "AlignProcessor"),
        ("altclip", "AltCLIPProcessor"),
        ("bark", "BarkProcessor"),
        ("blip", "BlipProcessor"),
        ("blip-2", "Blip2Processor"),
        ("bridgetower", "BridgeTowerProcessor"),
        ("chinese_clip", "ChineseCLIPProcessor"),
        ("clap", "ClapProcessor"),
        ("clip", "CLIPProcessor"),
        ("clipseg", "CLIPSegProcessor"),
        ("flava", "FlavaProcessor"),
        ("git", "GitProcessor"),
        ("groupvit", "CLIPProcessor"),
        ("hubert", "Wav2Vec2Processor"),
        ("instructblip", "InstructBlipProcessor"),
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("markuplm", "MarkupLMProcessor"),
        ("mctct", "MCTCTProcessor"),
        ("mgp-str", "MgpstrProcessor"),
        ("oneformer", "OneFormerProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("pix2struct", "Pix2StructProcessor"),
        ("sam", "SamProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
        ("speech_to_text_2", "Speech2Text2Processor"),
        ("speecht5", "SpeechT5Processor"),
        ("trocr", "TrOCRProcessor"),
        ("tvlt", "TvltProcessor"),
        ("unispeech", "Wav2Vec2Processor"),
        ("unispeech-sat", "Wav2Vec2Processor"),
        ("vilt", "ViltProcessor"),
        ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
        ("wav2vec2", "Wav2Vec2Processor"),
        ("wav2vec2-conformer", "Wav2Vec2Processor"),
        ("wavlm", "Wav2Vec2Processor"),
        ("whisper", "WhisperProcessor"),
        ("xclip", "XCLIPProcessor"),
    ]
)

a__		: Tuple  							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 							snake_case				(		UpperCAmelCase						)->      Dict:







      """simple docstring"""
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  __A													=      model_type_to_module_name(UpperCAmelCase						)

                  __A													=      importlib.import_module(f'.{module_name}'   ,	'transformers.models'						)
                  try:
                        return getattr(UpperCAmelCase   ,	UpperCAmelCase						)
                  except AttributeError:
                        continue

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(UpperCAmelCase   ,	'__name__'   ,	UpperCAmelCase						) == class_name:
                  return processor

    # 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.
      __A													=      importlib.import_module('transformers'						)
      if hasattr(UpperCAmelCase   ,	UpperCAmelCase						):
            return getattr(UpperCAmelCase   ,	UpperCAmelCase						)

      return None


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]      )					->      Tuple:







       '''simple docstring'''



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






 @classmethod
 @replace_list_option_in_docstrings(_A      )
 def 						lowercase_    (      cls	:Union[str, Any]						,					_A	:Optional[int]						,					**_A	:int      )					->      Dict:







       '''simple docstring'''



       __A													=      kwargs.pop('config'						,					_A      )
       __A													=      kwargs.pop('trust_remote_code'						,					_A      )
       __A													=      True

       __A													=      None
       __A													=      None

       # First, let's see if we have a preprocessor config.
       # Filter the kwargs for `get_file_from_repo`.
       __A													=      {
           key: kwargs[key] for key in inspect.signature(_A      ).parameters.keys() if key in kwargs
       }
       # Let's start by checking whether the processor class is saved in an image processor
       __A													=      get_file_from_repo(
           _A						,					_A						,					**_A      )
       if preprocessor_config_file is not None:
             __A						,   __A													=      ImageProcessingMixin.get_image_processor_dict(_A						,					**_A      )
             __A													=      config_dict.get('processor_class'						,					_A      )
             if "AutoProcessor" in config_dict.get('auto_map'						,					{}      ):
                   __A													=      config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
       if preprocessor_config_file is not None and processor_class is None:
             __A						,   __A													=      FeatureExtractionMixin.get_feature_extractor_dict(_A						,					**_A      )
             __A													=      config_dict.get('processor_class'						,					_A      )
             if "AutoProcessor" in config_dict.get('auto_map'						,					{}      ):
                   __A													=      config_dict['auto_map']['AutoProcessor']

       if processor_class is None:
             # Next, let's check whether the processor class is saved in a tokenizer
             __A													=      get_file_from_repo(
                 _A						,					_A						,					**_A      )
             if tokenizer_config_file is not None:
                   with open(_A						,					encoding='utf-8'      ) as reader:
                         __A													=      json.load(_A      )

                   __A													=      config_dict.get('processor_class'						,					_A      )
                   if "AutoProcessor" in config_dict.get('auto_map'						,					{}      ):
                         __A													=      config_dict['auto_map']['AutoProcessor']

       if processor_class is None:
             # Otherwise, load config, if it can be loaded.
             if not isinstance(_A						,					_A      ):
                   __A													=      AutoConfig.from_pretrained(
                       _A						,					trust_remote_code=_A						,					**_A      )

             # And check if the config contains the processor class.
             __A													=      getattr(_A						,					'processor_class'						,					_A      )
             if hasattr(_A						,					'auto_map'      ) and "AutoProcessor" in config.auto_map:
                   __A													=      config.auto_map['AutoProcessor']

       if processor_class is not None:
             __A													=      processor_class_from_name(_A      )

       __A													=      processor_auto_map is not None
       __A													=      processor_class is not None or type(_A      ) in PROCESSOR_MAPPING
       __A													=      resolve_trust_remote_code(
           _A						,					_A						,					_A						,					_A      )

       if has_remote_code and trust_remote_code:
             __A													=      get_class_from_dynamic_module(
                 _A						,					_A						,					**_A      )
             __A													=      kwargs.pop('code_revision'						,					_A      )
             if os.path.isdir(_A      ):
                   processor_class.register_for_auto_class()
             return processor_class.from_pretrained(
                 _A						,					trust_remote_code=_A						,					**_A      )
       elif processor_class is not None:
             return processor_class.from_pretrained(
                 _A						,					trust_remote_code=_A						,					**_A      )
       # Last try: we use the PROCESSOR_MAPPING.
       elif type(_A      ) in PROCESSOR_MAPPING:
             return PROCESSOR_MAPPING[type(_A      )].from_pretrained(_A						,					**_A      )

       # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
       # tokenizer.
       try:
             return AutoTokenizer.from_pretrained(
                 _A						,					trust_remote_code=_A						,					**_A      )
       except Exception:
             try:
                   return AutoImageProcessor.from_pretrained(
                       _A						,					trust_remote_code=_A						,					**_A      )
             except Exception:
                   pass

             try:
                   return AutoFeatureExtractor.from_pretrained(
                       _A						,					trust_remote_code=_A						,					**_A      )
             except Exception:
                   pass

       raise ValueError(
           F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
           'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
           'the files of at least one of those processing classes.'      )






 @staticmethod
 def 						lowercase_    (      _A	:Optional[int]						,					_A	:int      )					->      List[str]:







       '''simple docstring'''



       PROCESSOR_MAPPING.register(_A						,					_A      )














'''simple docstring'''







from __future__ import annotations





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Dict:
							lowerCamelCase__   :      List[str]			    =			0
							lowerCamelCase__   :      Tuple			    =			len(UpperCamelCase ) - 1
							while left <= right:
														lowerCamelCase__   :      List[str]			    =			(left + right) // 2
														if left == right:
																					if sorted_list[middle] < item:
																												lowerCamelCase__   :      Any			    =			middle + 1
																					break
														elif sorted_list[middle] < item:
																					lowerCamelCase__   :      Optional[Any]			    =			middle + 1
														else:
																					lowerCamelCase__   :      Dict			    =			middle - 1
							sorted_list.insert(UpperCamelCase							,     UpperCamelCase )
							return sorted_list





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Tuple:
							lowerCamelCase__   :      Any			    =			[]
							while left and right:
														if left[0][0] < right[0][0]:
																					result.append(left.pop(0 ) )
														else:
																					result.append(right.pop(0 ) )
							return result + left + right





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[Any]			    =			len(UpperCamelCase )
							if length <= 1:
														return list_ad
							lowerCamelCase__   :      Tuple			    =			length // 2
							return merge(sortlist_ad(list_ad[:middle] )							,     sortlist_ad(list_ad[middle:] ) )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			list[int]:

							if len(UpperCamelCase ) <= 1:
														return collection
							lowerCamelCase__   :      int			    =			[]
							lowerCamelCase__   :      int			    =			False
							for i in range(0							,     len(UpperCamelCase )							,     2 ):
														if i == len(UpperCamelCase ) - 1:
																					lowerCamelCase__   :      List[Any]			    =			True
														else:
																					if collection[i] < collection[i + 1]:
																												two_paired_list.append([collection[i], collection[i + 1]] )
																					else:
																												two_paired_list.append([collection[i + 1], collection[i]] )
							lowerCamelCase__   :      List[Any]			    =			sortlist_ad(UpperCamelCase )
							lowerCamelCase__   :      List[Any]			    =			[i[0] for i in sorted_list_ad]
							result.append(sorted_list_ad[-1][1] )
							if has_last_odd_item:
														lowerCamelCase__   :      int			    =			collection[-1]
														lowerCamelCase__   :      str			    =			binary_search_insertion(UpperCamelCase							,     UpperCamelCase )
							lowerCamelCase__   :      Any			    =			False
							for i in range(len(UpperCamelCase ) - 1 ):
														if result[i] == collection[-1] and has_last_odd_item:
																					lowerCamelCase__   :      Any			    =			True
														lowerCamelCase__   :      Dict			    =			sorted_list_ad[i][1]
														# If last_odd_item is inserted before the item's index,
														# you should forward index one more.
														if is_last_odd_item_inserted_before_this_index:
																					lowerCamelCase__   :      List[str]			    =			result[: i + 2] + binary_search_insertion(result[i + 2 :]							,     UpperCamelCase )
														else:
																					lowerCamelCase__   :      Dict			    =			result[: i + 1] + binary_search_insertion(result[i + 1 :]							,     UpperCamelCase )

							return result


if __name__ == "__main__":
			_A      :     Tuple       	=input('''Enter numbers separated by a comma:\n''').strip()
			_A      :     List[Any]       	=[int(item) for item in user_input.split(''',''')]
			print(merge_insertion_sort(unsorted))

