


'''simple docstring'''







from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


a__  :					Any        =       logging.get_logger(__name__)
class 					lowercase_     :
    __UpperCAmelCase   			=	42
    __UpperCAmelCase   			=	None


    @staticmethod
    def    __a							(     ):
          raise NotImplementedError


    def    __a							(     self  ,					a  ,					a  ,					a  ,					**a    ):
          raise NotImplementedError


    def    __a							(     self  ,					a    ):
          raise NotImplementedError


    def    __a							(     self    ):
          if not self.is_available():
                raise RuntimeError(
                    f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''    )


    @classmethod
    def    __a							(     cls    ):
          return f'''`pip install {cls.pip_package or cls.name}`'''

class 					lowercase_     (    __A   ):
    __UpperCAmelCase   			=	"optuna"


    @staticmethod
    def    __a							(     ):
          return is_optuna_available()


    def    __a							(     self  ,					a  ,					a  ,					a  ,					**a    ):
          return run_hp_search_optuna(lowercase_  ,					lowercase_  ,					lowercase_  ,					**lowercase_    )


    def    __a							(     self  ,					a    ):
          return default_hp_space_optuna(lowercase_    )

class 					lowercase_     (    __A   ):
    __UpperCAmelCase   			=	"ray"
    __UpperCAmelCase   			=	"'ray[tune]'"


    @staticmethod
    def    __a							(     ):
          return is_ray_available()


    def    __a							(     self  ,					a  ,					a  ,					a  ,					**a    ):
          return run_hp_search_ray(lowercase_  ,					lowercase_  ,					lowercase_  ,					**lowercase_    )


    def    __a							(     self  ,					a    ):
          return default_hp_space_ray(lowercase_    )

class 					lowercase_     (    __A   ):
    __UpperCAmelCase   			=	"sigopt"


    @staticmethod
    def    __a							(     ):
          return is_sigopt_available()


    def    __a							(     self  ,					a  ,					a  ,					a  ,					**a    ):
          return run_hp_search_sigopt(lowercase_  ,					lowercase_  ,					lowercase_  ,					**lowercase_    )


    def    __a							(     self  ,					a    ):
          return default_hp_space_sigopt(lowercase_    )

class 					lowercase_     (    __A   ):
    __UpperCAmelCase   			=	"wandb"


    @staticmethod
    def    __a							(     ):
          return is_wandb_available()


    def    __a							(     self  ,					a  ,					a  ,					a  ,					**a    ):
          return run_hp_search_wandb(lowercase_  ,					lowercase_  ,					lowercase_  ,					**lowercase_    )


    def    __a							(     self  ,					a    ):
          return default_hp_space_wandb(lowercase_    )

a__  :					Optional[int]        =       {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}







def _UpperCamelCase       (     )							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__										=				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
      if len(_lowerCamelCase    ) > 0:
            UpperCamelCase__										=				available_backends[0].name
            if len(_lowerCamelCase    ) > 1:
                  logger.info(
                      F'''{len(_lowerCamelCase    )} hyperparameter search backends available. Using {name} as the default.'''    )
            return name
      raise RuntimeError(
          "No hyperparameter search backend available.\n"
          + "\n".join(
              F''' - To install {backend.name} run {backend.pip_install()}'''
              for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int=None	,						lowercase_				:     str=None	,						**lowercase_				:     Optional[Any]  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,						lowercase_	,						)
           lowercase__ :   Optional[int]    					=				kwargs.pop("feature_extractor"  )

       lowercase__ :   int    					=				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."  )
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."  )

       super().__init__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."  )

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

       # first, apply the image processor
       lowercase__ :   Union[str, Any]    					=				self.image_processor(images=lowercase_	,						return_tensors=lowercase_  )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   Optional[Any]    					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__ :   Any    					=				features["words"]

       lowercase__ :   Tuple    					=				self.tokenizer(
           text=text if text is not None else features["words"]	,						text_pair=text_pair if text_pair is not None else None	,						boxes=boxes if boxes is not None else features["boxes"]	,						word_labels=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

       # add pixel values
       lowercase__ :   Optional[int]    					=				features.pop("pixel_values"  )
       if return_overflowing_tokens is True:
           lowercase__ :   Dict    					=				self.get_overflowing_images(lowercase_	,						encoded_inputs["overflow_to_sample_mapping"]  )
       lowercase__ :   str    					=				images

       return encoded_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[Any]  )					->						Dict:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__ :   Tuple    					=				[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]  )

       if len(lowercase_  ) != len(lowercase_  ):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_  )} and {len(lowercase_  )}'''  )

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Tuple:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,						lowercase_	,						)
       return self.image_processor



