
'''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   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

            snake_case_	:      Dict            =  importlib.import_module(f'.{module_name}',						"transformers.models"    )
            try:
                return getattr(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )
            except AttributeError:
                continue

    for processor in PROCESSOR_MAPPING._extra_content.values():
        if getattr(__SCREAMING_SNAKE_CASE,						"__name__",						__SCREAMING_SNAKE_CASE    ) == 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.
    snake_case_	:      str            =  importlib.import_module("transformers"    )
    if hasattr(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
        return getattr(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )

    return None






class 				UpperCAmelCase_			:





 def __init__(      self):
     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	,    lowercase_	,    **lowercase_):
     snake_case_	:      List[str]            =  kwargs.pop("config"	,    lowercase_)
     snake_case_	:      Optional[Any]            =  kwargs.pop("trust_remote_code"	,    lowercase_)
     snake_case_	:      Dict            =  True

     snake_case_	:      List[str]            =  None
     snake_case_	:      str            =  None

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

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

             snake_case_	:      str            =  config_dict.get("processor_class"	,    lowercase_)
             if "AutoProcessor" in config_dict.get("auto_map"	,    {}):
                 snake_case_	:      Optional[int]            =  config_dict["auto_map"]["AutoProcessor"]

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

         # And check if the config contains the processor class.
         snake_case_	:      Optional[Any]            =  getattr(lowercase_	,    "processor_class"	,    lowercase_)
         if hasattr(lowercase_	,    "auto_map") and "AutoProcessor" in config.auto_map:
             snake_case_	:      Union[str, Any]            =  config.auto_map["AutoProcessor"]

     if processor_class is not None:
         snake_case_	:      str            =  processor_class_from_name(lowercase_)

     snake_case_	:      Optional[Any]            =  processor_auto_map is not None
     snake_case_	:      List[str]            =  processor_class is not None or type(lowercase_) in PROCESSOR_MAPPING
     snake_case_	:      Optional[Any]            =  resolve_trust_remote_code(
         lowercase_	,    lowercase_	,    lowercase_	,    lowercase_)

     if has_remote_code and trust_remote_code:
         snake_case_	:      str            =  get_class_from_dynamic_module(
             lowercase_	,    lowercase_	,    **lowercase_)
         snake_case_	:      Tuple            =  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_	,    lowercase_):
     PROCESSOR_MAPPING.register(lowercase_	,    lowercase_)






'''simple docstring'''
from math import exp

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    return 8 * x - 2 * exp(-x    )

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  lower_bound
    snake_case_	:      int            =  upper_bound
    for _ in range(0,						__SCREAMING_SNAKE_CASE    ):
        snake_case_     ,	snake_case_	:      List[str]            =  xa, xa - (f(__SCREAMING_SNAKE_CASE    ) * (xa - xa)) / (f(__SCREAMING_SNAKE_CASE    ) - f(__SCREAMING_SNAKE_CASE    ))
    return xa


if __name__ == "__main__":
  print(f"""Example: {secant_method(1, 3, 2)}""")




