






# Imports
import numpy as np



class 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						List[Any]:
           self.set_matricies(red=__UpperCamelCase       ,    green=__UpperCamelCase       ,    blue=__UpperCamelCase       ,    red_edge=__UpperCamelCase       ,    nir=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						Any:
           if red is not None:
                __lowerCAmelCase             =       red
           if green is not None:
                __lowerCAmelCase             =       green
           if blue is not None:
                __lowerCAmelCase             =       blue
           if red_edge is not None:
                __lowerCAmelCase             =       red_edge
           if nir is not None:
                __lowerCAmelCase             =       nir
           return True


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=""       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						Union[str, Any]:
           self.set_matricies(red=__UpperCamelCase       ,    green=__UpperCamelCase       ,    blue=__UpperCamelCase       ,    red_edge=__UpperCamelCase       ,    nir=__UpperCamelCase						)
           __lowerCAmelCase             =       {
               "ARVI2": self.arvaa,
               "CCCI": self.ccci,
               "CVI": self.cvi,
               "GLI": self.gli,
               "NDVI": self.ndvi,
               "BNDVI": self.bndvi,
               "redEdgeNDVI": self.red_edge_ndvi,
               "GNDVI": self.gndvi,
               "GBNDVI": self.gbndvi,
               "GRNDVI": self.grndvi,
               "RBNDVI": self.rbndvi,
               "PNDVI": self.pndvi,
               "ATSAVI": self.atsavi,
               "BWDRVI": self.bwdrvi,
               "CIgreen": self.ci_green,
               "CIrededge": self.ci_rededge,
               "CI": self.ci,
               "CTVI": self.ctvi,
               "GDVI": self.gdvi,
               "EVI": self.evi,
               "GEMI": self.gemi,
               "GOSAVI": self.gosavi,
               "GSAVI": self.gsavi,
               "Hue": self.hue,
               "IVI": self.ivi,
               "IPVI": self.ipvi,
               "I": self.i,
               "RVI": self.rvi,
               "MRVI": self.mrvi,
               "MSAVI": self.m_savi,
               "NormG": self.norm_g,
               "NormNIR": self.norm_nir,
               "NormR": self.norm_r,
               "NGRDI": self.ngrdi,
               "RI": self.ri,
               "S": self.s,
               "IF": self._if,
               "DVI": self.dvi,
               "TVI": self.tvi,
               "NDRE": self.ndre,
           }

           try:
                return funcs[index]()
           except KeyError:
                print("Index not in the list!"						)
                return False


      def 			__UpperCAmelCase		(						self						)->						Any:
           return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))


      def 			__UpperCAmelCase		(						self						)->						int:
           return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
               (self.nir - self.red) / (self.nir + self.red)
           )


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return self.nir * (self.red / (self.green**2))


      def 			__UpperCAmelCase		(						self						)->						Any:
           return (2 * self.green - self.red - self.blue) / (
               2 * self.green + self.red + self.blue
           )


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           return (self.nir - self.red) / (self.nir + self.red)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return (self.nir - self.blue) / (self.nir + self.blue)


      def 			__UpperCAmelCase		(						self						)->						int:
           return (self.redEdge - self.red) / (self.redEdge + self.red)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           return (self.nir - self.green) / (self.nir + self.green)


      def 			__UpperCAmelCase		(						self						)->						str:
           return (self.nir - (self.green + self.blue)) / (
               self.nir + (self.green + self.blue)
           )


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return (self.nir - (self.green + self.red)) / (
               self.nir + (self.green + self.red)
           )


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           return (self.nir - (self.green + self.red + self.blue)) / (
               self.nir + (self.green + self.red + self.blue)
           )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0.0_8       ,    __UpperCamelCase=1.2_2       ,    __UpperCamelCase=0.0_3						)->						List[str]:
           return a * (
               (self.nir - a * self.red - b)
               / (a * self.nir + self.red - a * b + x * (1 + a**2))
           )


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           return (self.nir / self.green) - 1


      def 			__UpperCAmelCase		(						self						)->						Any:
           return (self.nir / self.redEdge) - 1


      def 			__UpperCAmelCase		(						self						)->						int:
           return (self.red - self.blue) / self.red


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.ndvi()
           return ((ndvi + 0.5) / (abs(ndvi + 0.5						))) * (abs(ndvi + 0.5						) ** (1 / 2))


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return self.nir - self.green


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return 2.5 * (
               (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
           )


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
               self.nir + self.red + 0.5
           )
           return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0.1_6						)->						Optional[int]:
           return (self.nir - self.green) / (self.nir + self.green + y)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0.5						)->						Optional[Any]:
           return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)


      def 			__UpperCAmelCase		(						self						)->						str:
           return np.arctan(
               ((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						Optional[int]:
           return (self.nir - b) / (a * self.red)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)


      def 			__UpperCAmelCase		(						self						)->						Any:
           return (self.red + self.green + self.blue) / 3_0.5


      def 			__UpperCAmelCase		(						self						)->						Any:
           return self.nir / self.red


      def 			__UpperCAmelCase		(						self						)->						int:
           return (self.rvi() - 1) / (self.rvi() + 1)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           return (
               (2 * self.nir + 1)
               - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
           ) / 2


      def 			__UpperCAmelCase		(						self						)->						str:
           return self.green / (self.nir + self.red + self.green)


      def 			__UpperCAmelCase		(						self						)->						Any:
           return self.nir / (self.nir + self.red + self.green)


      def 			__UpperCAmelCase		(						self						)->						int:
           return self.red / (self.nir + self.red + self.green)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return (self.green - self.red) / (self.green + self.red)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return (self.red - self.green) / (self.red + self.green)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       np.max([np.max(self.red						), np.max(self.green						), np.max(self.blue						)]						)
           __lowerCAmelCase             =       np.min([np.min(self.red						), np.min(self.green						), np.min(self.blue						)]						)
           return (max_value - min_value) / max_value


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           return (2 * self.red - self.green - self.blue) / (self.green - self.blue)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return self.nir / self.red


      def 			__UpperCAmelCase		(						self						)->						Any:
           return (self.ndvi() + 0.5) ** (1 / 2)


      def 			__UpperCAmelCase		(						self						)->						Any:
           return (self.nir - self.redEdge) / (self.nir + self.redEdge)














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,
)


lowerCamelCase      :       Optional[Any]    =						logging.get_logger(__name__)

lowerCamelCase      :       Any    =						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'''),
    ]
)

lowerCamelCase      :       Optional[int]    =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def    __lowerCAmelCase   (    __snake_case							):
     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __lowerCAmelCase             =       model_type_to_module_name(__snake_case							)

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

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(__snake_case       ,			"__name__"       ,			__snake_case							) == 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.
     __lowerCAmelCase             =       importlib.import_module("transformers"							)
     if hasattr(__snake_case       ,			__snake_case							):
          return getattr(__snake_case       ,			__snake_case							)

     return None





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = None       ,			__snake_case = False       ,			__snake_case = False       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = None       ,			__snake_case = False       ,			**__snake_case       ,			):
     __lowerCAmelCase             =       get_file_from_repo(
         __snake_case       ,			__snake_case       ,			cache_dir=__snake_case       ,			force_download=__snake_case       ,			resume_download=__snake_case       ,			proxies=__snake_case       ,			use_auth_token=__snake_case       ,			revision=__snake_case       ,			local_files_only=__snake_case       ,			)
     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(__snake_case       ,			encoding="utf-8"							) as reader:
          return json.load(__snake_case							)



class 				_UpperCamelCase      :


      def __init__(						self						)->						Union[str, Any]:
           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 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       kwargs.pop("config"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       kwargs.pop("trust_remote_code"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       True

           __lowerCAmelCase      ,			__lowerCAmelCase             =       FeatureExtractionMixin.get_feature_extractor_dict(__UpperCamelCase       ,    **__UpperCamelCase						)
           __lowerCAmelCase             =       config_dict.get("feature_extractor_type"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"       ,    {}						):
                __lowerCAmelCase             =       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						):
                     __lowerCAmelCase             =       AutoConfig.from_pretrained(__UpperCamelCase       ,    **__UpperCamelCase						)
                # It could be in `config.feature_extractor_type``
                __lowerCAmelCase             =       getattr(__UpperCamelCase       ,    "feature_extractor_type"       ,    __UpperCamelCase						)
                if hasattr(__UpperCamelCase       ,    "auto_map"						) and "AutoFeatureExtractor" in config.auto_map:
                     __lowerCAmelCase             =       config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
                __lowerCAmelCase             =       feature_extractor_class_from_name(__UpperCamelCase						)

           __lowerCAmelCase             =       feature_extractor_auto_map is not None
           __lowerCAmelCase             =       feature_extractor_class is not None or type(__UpperCamelCase						) in FEATURE_EXTRACTOR_MAPPING
           __lowerCAmelCase             =       resolve_trust_remote_code(
               __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

           if has_remote_code and trust_remote_code:
                __lowerCAmelCase             =       get_class_from_dynamic_module(
                    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						)
                __lowerCAmelCase             =       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:
                __lowerCAmelCase             =       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 			__UpperCAmelCase		(						__UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase       ,    __UpperCamelCase						)






