

from .number_theory.prime_numbers import next_prime







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Optional[Any]  ,	A_     :					int  ,	A_     :					int | None = None  ,	A_     :					float | None = None  ,	)       ->			None:
      SCREAMING_SNAKE_CASE__         =    size_table
      SCREAMING_SNAKE_CASE__         =    [None] * self.size_table
      SCREAMING_SNAKE_CASE__         =    0.75 if lim_charge is None else lim_charge
      SCREAMING_SNAKE_CASE__         =    1 if charge_factor is None else charge_factor
      SCREAMING_SNAKE_CASE__         =    []
      SCREAMING_SNAKE_CASE__         =    {}



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      return self._keys



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			Optional[Any]:
      return sum(1 for slot in self.values if slot is not None						) / (
          self.size_table * self.charge_factor
      )



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					str						)       ->			List[str]:
      return key % self.size_table



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					Optional[int]						)       ->			Dict:
      print(f"step {step_ord}"						)
      print(list(range(len(self.values						)						)						)						)
      print(self.values						)



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Any						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    1
      SCREAMING_SNAKE_CASE__         =    values
      for value in values:
          self.insert_data(A_						)
          self._step_by_step(A_						)
          i += 1



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					List[Any]  ,	A_     :					str						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    data
      SCREAMING_SNAKE_CASE__         =    data



  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					List[str]  ,	A_     :					Tuple=None						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    self.hash_function(key + 1						)

      while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(A_						) > 0:
              SCREAMING_SNAKE_CASE__         =    self.hash_function(new_key + 1						)
          else:
              SCREAMING_SNAKE_CASE__         =    None
              break

      return new_key



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    [value for value in self.values if value is not None]
      SCREAMING_SNAKE_CASE__         =    next_prime(self.size_table  ,	factor=2						)
      self._keys.clear()
      SCREAMING_SNAKE_CASE__         =    [None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
          self.insert_data(A_						)







  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					int						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.hash_function(A_						)

      if self.values[key] is None:
          self._set_value(A_  ,	A_						)

      elif self.values[key] == data:
          pass

      else:
          SCREAMING_SNAKE_CASE__         =    self._collision_resolution(A_  ,	A_						)
          if collision_resolution is not None:
              self._set_value(A_  ,	A_						)
          else:
              self.rehashing()
              self.insert_data(A_						)





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


