


'''simple docstring'''







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


A				:					str    						=			logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   field(default=a_    , metadata={"help": "Whether to use SortishSampler or not."}	)
    a													=   field(
        default=a_    , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    a													=   field(
        default=a_    , 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													=   field(
        default=a_    , 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													=   field(
        default=a_    , metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }    , )


    def        A   (  self      :							Tuple):
           _A					:     Tuple     = super().to_dict()
           for k, v in d.items():
                  if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         _A					:     int     = v.to_dict()
           return d










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






