




'''simple docstring'''







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


A					       =logging.get_logger(__name__)

A					       =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'),
    ]
)

A					       =_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 		snake_case_				(_a      :				str	):
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            UpperCAmelCase     			=						model_type_to_module_name(_a	)

            UpperCAmelCase     			=						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.
    UpperCAmelCase     			=						importlib.import_module('''transformers'''	)
    if hasattr(_a       ,  _a	):
        return getattr(_a       ,  _a	)

    return None







class      _a    :
       def __init__(      self		:      Union[str, 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        A (      cls		:      Optional[int]  ,							lowercase		:      Optional[Any]  ,							**lowercase		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						kwargs.pop('''config'''  ,							lowercase							)
           UpperCAmelCase     			=						kwargs.pop('''trust_remote_code'''  ,							lowercase							)
           UpperCAmelCase     			=						True

           UpperCAmelCase     			=						None
           UpperCAmelCase     			=						None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           UpperCAmelCase     			=						{
               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
           UpperCAmelCase     			=						get_file_from_repo(
               lowercase  ,							lowercase  ,							**lowercase							)
           if preprocessor_config_file is not None:
               UpperCAmelCase			,	UpperCAmelCase     			=						ImageProcessingMixin.get_image_processor_dict(lowercase  ,							**lowercase							)
               UpperCAmelCase     			=						config_dict.get('''processor_class'''  ,							lowercase							)
               if "AutoProcessor" in config_dict.get('''auto_map'''  ,							{}							):
                   UpperCAmelCase     			=						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:
               UpperCAmelCase			,	UpperCAmelCase     			=						FeatureExtractionMixin.get_feature_extractor_dict(lowercase  ,							**lowercase							)
               UpperCAmelCase     			=						config_dict.get('''processor_class'''  ,							lowercase							)
               if "AutoProcessor" in config_dict.get('''auto_map'''  ,							{}							):
                   UpperCAmelCase     			=						config_dict['''auto_map''']['''AutoProcessor''']

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

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

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

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

           if processor_class is not None:
               UpperCAmelCase     			=						processor_class_from_name(lowercase							)

           UpperCAmelCase     			=						processor_auto_map is not None
           UpperCAmelCase     			=						processor_class is not None or type(lowercase							) in PROCESSOR_MAPPING
           UpperCAmelCase     			=						resolve_trust_remote_code(
               lowercase  ,							lowercase  ,							lowercase  ,							lowercase							)

           if has_remote_code and trust_remote_code:
               UpperCAmelCase     			=						get_class_from_dynamic_module(
                   lowercase  ,							lowercase  ,							**lowercase							)
               UpperCAmelCase     			=						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        A (      lowercase		:      Dict  ,							lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           PROCESSOR_MAPPING.register(lowercase  ,							lowercase							)










'''simple docstring'''







def 		snake_case_				(_a      :				float       ,  _a      :				float       ,  _a      :				float	):
    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'''	)

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

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

    return [red, green, blue]





def 		snake_case_				(_a      :				int       ,  _a      :				int       ,  _a      :				int	):
    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'''	)

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

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

    UpperCAmelCase     			=						(hue + 3_6_0) % 3_6_0

    return [hue, saturation, value]







def 		snake_case_				(_a      :				list[float]       ,  _a      :				list[float]	):
    UpperCAmelCase     			=						abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCAmelCase     			=						abs(hsv_a[1] - hsv_a[1]	) < 0.002
    UpperCAmelCase     			=						abs(hsv_a[2] - hsv_a[2]	) < 0.002

    return check_hue and check_saturation and check_value




