

'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_ : Any						  =      {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      ['GLPNFeatureExtractor']
       lowerCAmelCase_ : List[str]						  =      ['GLPNImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : List[Any]						  =      [
           'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GLPNForDepthEstimation',
           'GLPNLayer',
           'GLPNModel',
           'GLPNPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_glpn import GLPNFeatureExtractor
              from .image_processing_glpn import GLPNImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_glpn import (
                  GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GLPNForDepthEstimation,
                  GLPNLayer,
                  GLPNModel,
                  GLPNPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : int						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''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


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





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


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






