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


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					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'),
    ]
)

lowerCAmelCase__      		=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
              if class_name in processors:
                     lowerCAmelCase__	      =   model_type_to_module_name(lowerCAmelCase__      )

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

       for processor in PROCESSOR_MAPPING._extra_content.values():
              if getattr(lowerCAmelCase__      ,    '__name__'      ,    lowerCAmelCase__      ) == 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.
       lowerCAmelCase__	      =   importlib.import_module('transformers'      )
       if hasattr(lowerCAmelCase__      ,    lowerCAmelCase__      ):
              return getattr(lowerCAmelCase__      ,    lowerCAmelCase__      )

       return None



class 							a_					:






 '''simple docstring'''






 def __init__(     self   :    List[Any]):







        '''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(lowercase__)
 def 						__snake_case		(     cls   :    List[str]     ,       lowercase__   :    Any     ,       **lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   kwargs.pop('config'     ,       lowercase__)
        lowerCAmelCase__	      =   kwargs.pop('trust_remote_code'     ,       lowercase__)
        lowerCAmelCase__	      =   True

        lowerCAmelCase__	      =   None
        lowerCAmelCase__	      =   None

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

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

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

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

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

        if processor_class is not None:
               lowerCAmelCase__	      =   processor_class_from_name(lowercase__)

        lowerCAmelCase__	      =   processor_auto_map is not None
        lowerCAmelCase__	      =   processor_class is not None or type(lowercase__) in PROCESSOR_MAPPING
        lowerCAmelCase__	      =   resolve_trust_remote_code(
            lowercase__     ,       lowercase__     ,       lowercase__     ,       lowercase__)

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

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

               try:
                      return AutoFeatureExtractor.from_pretrained(
                          lowercase__     ,       trust_remote_code=lowercase__     ,       **lowercase__)
               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 						__snake_case		(     lowercase__   :    List[Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        PROCESSOR_MAPPING.register(lowercase__     ,       lowercase__)

def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if hue < 0 or hue > 3_6_0:
              raise Exception('hue should be between 0 and 360'      )

       if saturation < 0 or saturation > 1:
              raise Exception('saturation should be between 0 and 1'      )

       if value < 0 or value > 1:
              raise Exception('value should be between 0 and 1'      )

       lowerCAmelCase__	      =   value * saturation
       lowerCAmelCase__	      =   hue / 6_0
       lowerCAmelCase__	      =   chroma * (1 - abs(hue_section % 2 - 1      ))
       lowerCAmelCase__	      =   value - chroma

       if hue_section >= 0 and hue_section <= 1:
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
       elif hue_section > 1 and hue_section <= 2:
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
       elif hue_section > 2 and hue_section <= 3:
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
       elif hue_section > 3 and hue_section <= 4:
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
       elif hue_section > 4 and hue_section <= 5:
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
       else:
              lowerCAmelCase__	      =   round(2_5_5 * (chroma + match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (match_value)      )
              lowerCAmelCase__	      =   round(2_5_5 * (second_largest_component + match_value)      )

       return [red, green, blue]






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if red < 0 or red > 2_5_5:
              raise Exception('red should be between 0 and 255'      )

       if green < 0 or green > 2_5_5:
              raise Exception('green should be between 0 and 255'      )

       if blue < 0 or blue > 2_5_5:
              raise Exception('blue should be between 0 and 255'      )

       lowerCAmelCase__	      =   red / 2_5_5
       lowerCAmelCase__	      =   green / 2_5_5
       lowerCAmelCase__	      =   blue / 2_5_5
       lowerCAmelCase__	      =   max(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   value - min(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )
       lowerCAmelCase__	      =   0 if value == 0 else chroma / value

       if chroma == 0:
              lowerCAmelCase__	      =   0.0
       elif value == float_red:
              lowerCAmelCase__	      =   6_0 * (0 + (float_green - float_blue) / chroma)
       elif value == float_green:
              lowerCAmelCase__	      =   6_0 * (2 + (float_blue - float_red) / chroma)
       else:
              lowerCAmelCase__	      =   6_0 * (4 + (float_red - float_green) / chroma)

       lowerCAmelCase__	      =   (hue + 3_6_0) % 3_6_0

       return [hue, saturation, value]






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   abs(hsv_a[0] - hsv_a[0]      ) < 0.2
       lowerCAmelCase__	      =   abs(hsv_a[1] - hsv_a[1]      ) < 0.0_0_2
       lowerCAmelCase__	      =   abs(hsv_a[2] - hsv_a[2]      ) < 0.0_0_2

       return check_hue and check_saturation and check_value
