



from __future__ import annotations
def   UpperCAmelCase							(		A__				,  A__     )							->					Tuple:
	_snake_case					:							Tuple					     =   0
	_snake_case					:							Tuple					     =   len(A__     ) - 1
	while left <= right:
		_snake_case					:							Optional[Any]					     =   (left + right) // 2
		if left == right:
			if sorted_list[middle] < item:
				_snake_case					:							str					     =   middle + 1
			break
		elif sorted_list[middle] < item:
			_snake_case					:							int					     =   middle + 1
		else:
			_snake_case					:							int					     =   middle - 1
	sorted_list.insert(A__				,  A__     )
	return sorted_list
def   UpperCAmelCase							(		A__				,  A__     )							->					Optional[int]:
	_snake_case					:							Tuple					     =   []
	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   UpperCAmelCase							(		A__     )							->					List[Any]:
	_snake_case					:							Union[str, Any]					     =   len(A__     )
	if length <= 1:
		return list_ad
	_snake_case					:							Union[str, Any]					     =   length // 2
	return merge(sortlist_ad(list_ad[:middle]     )				,  sortlist_ad(list_ad[middle:]     )     )
def   UpperCAmelCase							(		A__     )							->					list[int]:

	if len(A__     ) <= 1:
		return collection
	_snake_case					:							List[str]					     =   []
	_snake_case					:							Optional[int]					     =   False
	for i in range(0				,  len(A__     )				,  2     ):
		if i == len(A__     ) - 1:
			_snake_case					:							Dict					     =   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]]     )
	_snake_case					:							Tuple					     =   sortlist_ad(A__     )
	_snake_case					:							List[str]					     =   [i[0] for i in sorted_list_ad]
	result.append(sorted_list_ad[-1][1]     )
	if has_last_odd_item:
		_snake_case					:							Any					     =   collection[-1]
		_snake_case					:							Any					     =   binary_search_insertion(A__				,  A__     )
	_snake_case					:							Any					     =   False
	for i in range(len(A__     ) - 1     ):
		if result[i] == collection[-1] and has_last_odd_item:
			_snake_case					:							int					     =   True
		_snake_case					:							str					     =   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:
			_snake_case					:							Optional[int]					     =   result[: i + 2] + binary_search_insertion(result[i + 2 :]				,  A__     )
		else:
			_snake_case					:							Optional[int]					     =   result[: i + 1] + binary_search_insertion(result[i + 1 :]				,  A__     )

	return result


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










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


UpperCAmelCase_			      =  logging.get_logger(__name__)

UpperCAmelCase_			      =  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'''),
    ]
)

UpperCAmelCase_			      =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def   UpperCAmelCase							(		A__     )							->					Dict:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_snake_case					:							str					     =   model_type_to_module_name(A__     )

			_snake_case					:							Dict					     =   importlib.import_module(f'''.{module_name}'''				,  """transformers.models"""     )
			try:
				return getattr(A__				,  A__     )
			except AttributeError:
				continue

	for processor in PROCESSOR_MAPPING._extra_content.values():
		if getattr(A__				,  """__name__"""				,  A__     ) == 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.
	_snake_case					:							Optional[int]					     =   importlib.import_module("""transformers"""     )
	if hasattr(A__				,  A__     ):
		return getattr(A__				,  A__     )

	return None





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self      ):






					"""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(SCREAMING_SNAKE_CASE__      )
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   kwargs.pop("""config"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   kwargs.pop("""trust_remote_code"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   True

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None

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

					if processor_class is None:
						# Next, let's check whether the processor class is saved in a tokenizer
						_snake_case					:							Optional[int]					     =   get_file_from_repo(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						if tokenizer_config_file is not None:
							with open(SCREAMING_SNAKE_CASE__						,	encoding="""utf-8"""      ) as reader:
								_snake_case					:							Optional[Any]					     =   json.load(SCREAMING_SNAKE_CASE__      )

							_snake_case					:							Optional[int]					     =   config_dict.get("""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
							if "AutoProcessor" in config_dict.get("""auto_map"""						,	{}      ):
								_snake_case					:							Tuple					     =   config_dict["""auto_map"""]["""AutoProcessor"""]

					if processor_class is None:
						# Otherwise, load config, if it can be loaded.
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
							_snake_case					:							List[Any]					     =   AutoConfig.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )

						# And check if the config contains the processor class.
						_snake_case					:							Optional[Any]					     =   getattr(SCREAMING_SNAKE_CASE__						,	"""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if hasattr(SCREAMING_SNAKE_CASE__						,	"""auto_map"""      ) and "AutoProcessor" in config.auto_map:
							_snake_case					:							List[str]					     =   config.auto_map["""AutoProcessor"""]

					if processor_class is not None:
						_snake_case					:							Any					     =   processor_class_from_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Optional[Any]					     =   processor_auto_map is not None
					_snake_case					:							Optional[int]					     =   processor_class is not None or type(SCREAMING_SNAKE_CASE__      ) in PROCESSOR_MAPPING
					_snake_case					:							List[Any]					     =   resolve_trust_remote_code(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

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

					# 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(
						    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
					except Exception:
						try:
							return AutoImageProcessor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						except Exception:
							pass

						try:
							return AutoFeatureExtractor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						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 		__lowerCamelCase(				SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





