
"""simple docstring"""


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

# 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 ...utils import CONFIG_NAME, 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,
)


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE      =     OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

SCREAMING_SNAKE_CASE      =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
              if class_name in extractors:
                     UpperCamelCase					 =		model_type_to_module_name(UpperCAmelCase_				)

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

       for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
              if getattr(UpperCAmelCase_		,      "__name__"		,      UpperCAmelCase_				) == 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(UpperCAmelCase_		,      UpperCAmelCase_				):
              return getattr(UpperCAmelCase_		,      UpperCAmelCase_				)

       return None



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_ = None		,      UpperCAmelCase_ = False		,      UpperCAmelCase_ = False		,      UpperCAmelCase_ = None		,      UpperCAmelCase_ = None		,      UpperCAmelCase_ = None		,      UpperCAmelCase_ = False		,      **UpperCAmelCase_		,      )->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		get_file_from_repo(
           UpperCAmelCase_		,      UpperCAmelCase_		,      cache_dir=UpperCAmelCase_		,      force_download=UpperCAmelCase_		,      resume_download=UpperCAmelCase_		,      proxies=UpperCAmelCase_		,      use_auth_token=UpperCAmelCase_		,      revision=UpperCAmelCase_		,      local_files_only=UpperCAmelCase_		,      )
       if resolved_config_file is None:
              logger.info(
                  "Could not locate the feature extractor configuration file, will try to use the model config instead."				)
              return {}

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



class   __a  :






       def __init__(  self :							int			)->  List[str]:







              """simple docstring"""
              raise EnvironmentError(
                  "AutoFeatureExtractor is designed to be instantiated "
                  "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."			)






       @classmethod
       @replace_list_option_in_docstrings(UpperCAmelCase_			)
       def 	_SCREAMING_SNAKE_CASE       (  cls :							List[str]       ,      UpperCAmelCase_ :							Dict       ,      **UpperCAmelCase_ :							Dict			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		kwargs.pop("config"       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		kwargs.pop("trust_remote_code"       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		True

              UpperCamelCase ,       UpperCamelCase					 =		FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)
              UpperCamelCase					 =		config_dict.get("feature_extractor_type"       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		None
              if "AutoFeatureExtractor" in config_dict.get("auto_map"       ,      {}			):
                     UpperCamelCase					 =		config_dict["auto_map"]["AutoFeatureExtractor"]

              # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
              if feature_extractor_class is None and feature_extractor_auto_map is None:
                     if not isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                            UpperCamelCase					 =		AutoConfig.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                     # It could be in `config.feature_extractor_type``
                     UpperCamelCase					 =		getattr(UpperCAmelCase_       ,      "feature_extractor_type"       ,      UpperCAmelCase_			)
                     if hasattr(UpperCAmelCase_       ,      "auto_map"			) and "AutoFeatureExtractor" in config.auto_map:
                            UpperCamelCase					 =		config.auto_map["AutoFeatureExtractor"]

              if feature_extractor_class is not None:
                     UpperCamelCase					 =		feature_extractor_class_from_name(UpperCAmelCase_			)

              UpperCamelCase					 =		feature_extractor_auto_map is not None
              UpperCamelCase					 =		feature_extractor_class is not None or type(UpperCAmelCase_			) in FEATURE_EXTRACTOR_MAPPING
              UpperCamelCase					 =		resolve_trust_remote_code(
                  UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

              if has_remote_code and trust_remote_code:
                     UpperCamelCase					 =		get_class_from_dynamic_module(
                         UpperCAmelCase_       ,      UpperCAmelCase_       ,      **UpperCAmelCase_			)
                     UpperCamelCase					 =		kwargs.pop("code_revision"       ,      UpperCAmelCase_			)
                     if os.path.isdir(UpperCAmelCase_			):
                            feature_extractor_class.register_for_auto_class()
                     return feature_extractor_class.from_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)
              elif feature_extractor_class is not None:
                     return feature_extractor_class.from_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)
              # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
              elif type(UpperCAmelCase_			) in FEATURE_EXTRACTOR_MAPPING:
                     UpperCamelCase					 =		FEATURE_EXTRACTOR_MAPPING[type(UpperCAmelCase_			)]
                     return feature_extractor_class.from_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)

              raise ValueError(
                  f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
                  f"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
                  f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()			)}"			)






       @staticmethod
       def 	_SCREAMING_SNAKE_CASE       (  UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							Dict			)->  str:







              """simple docstring"""
              FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase_       ,      UpperCAmelCase_			)







"""simple docstring"""


def 	lowerCamelCase__			(							UpperCAmelCase_				)->							list:







       """simple docstring"""

       if len(UpperCAmelCase_				) <= 1:
              return [tuple(UpperCAmelCase_				)]

       UpperCamelCase					 =		[]

       def generate(UpperCAmelCase_		,      UpperCAmelCase_				):
              if k == 1:
                     res.append(tuple(arr[:]				)				)
                     return

              generate(k - 1		,      UpperCAmelCase_				)

              for i in range(k - 1				):
                     if k % 2 == 0:  # k is even
                            UpperCamelCase ,       UpperCamelCase					 =		arr[k - 1], arr[i]
                     else:  # k is odd
                            UpperCamelCase ,       UpperCamelCase					 =		arr[k - 1], arr[0]
                     generate(k - 1		,      UpperCAmelCase_				)

       generate(len(UpperCAmelCase_				)		,      UpperCAmelCase_				)
       return res


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





