
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__magic_name__  							=	logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		bool     						=  field(default=__UpperCamelCase							,       metadata={'help': 'Whether to use SortishSampler or not.'}			)
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `max_length` value of the model configuration.'
              )
          }							,       )
      _A    :		Optional[int]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': (
                  'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `num_beams` value of the model configuration.'
              )
          }							,       )
      _A    :		Optional[Union[str, Path, GenerationConfig]]     						=  field(
          default=__UpperCamelCase							,       metadata={
              'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
          }							,       )
      def A_      (      self			):
          snake_case__    =					super().to_dict()
          for k, v in d.items():
              if isinstance(lowerCamelCase		,						lowerCamelCase			):
                  snake_case__    =					v.to_dict()
          return d





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



