





"""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,
)


lowerCAmelCase_  			=							logging.get_logger(__name__)

lowerCAmelCase_  			=							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'''),
    ]
)

lowerCAmelCase_  			=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     _UpperCAmelCase     =     model_type_to_module_name(lowercase__							)

                     _UpperCAmelCase     =     importlib.import_module(F""".{module_name}""",'transformers.models'							)
                     try:
                            return getattr(lowercase__,lowercase__							)
                     except AttributeError:
                            continue

       for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
              if getattr(lowercase__,'__name__',lowercase__							) == 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.
       _UpperCAmelCase     =     importlib.import_module('transformers'							)
       if hasattr(lowercase__,lowercase__							):
              return getattr(lowercase__,lowercase__							)

       return None

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = False,SCREAMING_SNAKE_CASE = False,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = None,SCREAMING_SNAKE_CASE = False,**SCREAMING_SNAKE_CASE,)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     get_file_from_repo(
           lowercase__,lowercase__,cache_dir=lowercase__,force_download=lowercase__,resume_download=lowercase__,proxies=lowercase__,use_auth_token=lowercase__,revision=lowercase__,local_files_only=lowercase__,)
       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(lowercase__,encoding='utf-8'							) as reader:
              return json.load(lowercase__							)



class 	lowerCAmelCase					:






       def __init__(   self     ):
              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(lowerCAmelCase_     )
       def 							__A (   cls    ,  a__    ,  **a__     ):
              _UpperCAmelCase     =     kwargs.pop('config'    ,  lowerCAmelCase_     )
              _UpperCAmelCase     =     kwargs.pop('trust_remote_code'    ,  lowerCAmelCase_     )
              _UpperCAmelCase     =     True

              _UpperCAmelCase    ,							_UpperCAmelCase     =     ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_    ,  **lowerCAmelCase_     )
              _UpperCAmelCase     =     config_dict.get('image_processor_type'    ,  lowerCAmelCase_     )
              _UpperCAmelCase     =     None
              if "AutoImageProcessor" in config_dict.get('auto_map'    ,  {}     ):
                     _UpperCAmelCase     =     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:
                     _UpperCAmelCase     =     config_dict.pop('feature_extractor_type'    ,  lowerCAmelCase_     )
                     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.'     )
                            _UpperCAmelCase     =     feature_extractor_class.replace('FeatureExtractor'    ,  'ImageProcessor'     )
                     if "AutoFeatureExtractor" in config_dict.get('auto_map'    ,  {}     ):
                            _UpperCAmelCase     =     config_dict['auto_map']['AutoFeatureExtractor']
                            _UpperCAmelCase     =     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(lowerCAmelCase_    ,  lowerCAmelCase_     ):
                            _UpperCAmelCase     =     AutoConfig.from_pretrained(lowerCAmelCase_    ,  **lowerCAmelCase_     )
                     # It could be in `config.image_processor_type``
                     _UpperCAmelCase     =     getattr(lowerCAmelCase_    ,  'image_processor_type'    ,  lowerCAmelCase_     )
                     if hasattr(lowerCAmelCase_    ,  'auto_map'     ) and "AutoImageProcessor" in config.auto_map:
                            _UpperCAmelCase     =     config.auto_map['AutoImageProcessor']

              if image_processor_class is not None:
                     _UpperCAmelCase     =     image_processor_class_from_name(lowerCAmelCase_     )

              _UpperCAmelCase     =     image_processor_auto_map is not None
              _UpperCAmelCase     =     image_processor_class is not None or type(lowerCAmelCase_     ) in IMAGE_PROCESSOR_MAPPING
              _UpperCAmelCase     =     resolve_trust_remote_code(
                  lowerCAmelCase_    ,  lowerCAmelCase_    ,  lowerCAmelCase_    ,  lowerCAmelCase_     )

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

              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 							__A (   a__    ,  a__     ):
              IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_    ,  lowerCAmelCase_     )













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




       _UpperCAmelCase     =     False
       while is_sorted is False:  # Until all the indices are traversed keep looping
              _UpperCAmelCase     =     True
              for i in range(0,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all even indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False

              for i in range(1,len(SCREAMING_SNAKE_CASE							) - 1,2							):  # iterating over all odd indices
                     if input_list[i] > input_list[i + 1]:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     input_list[i + 1], input_list[i]
                            # swapping if elements not in order
                            _UpperCAmelCase     =     False
       return input_list


if __name__ == "__main__":
    print('''Enter list to be sorted''')
    lowerCAmelCase_			=							[int(x) for x in input().split()]
    # inputing elements of the list in one line
    lowerCAmelCase_			=							odd_even_sort(input_list)
    print('''The sorted list is''')
    print(sorted_list)






