

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  __lowerCAmelCase     (					UpperCamelCase__							):
  _a									=  ["""image_processor""", """tokenizer"""]
  _a									=  """LayoutLMv2ImageProcessor"""
  _a									=  ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")




  def __init__(				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        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	       =kwargs.pop('feature_extractor'					)

        _lowercase	       =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							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = True							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = 0							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = False							,   lowerCAmelCase = False							,   lowerCAmelCase = False							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> BatchEncoding:
        '''simple docstring'''






        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.'					)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'					)

        # first, apply the image processor
        _lowercase	       =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	       =[text]  # add batch dimension (as the image processor always adds a batch dimension)
              _lowercase	       =features['words']

        _lowercase	       =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	       =features.pop('pixel_values'					)
        if return_overflowing_tokens is True:
              _lowercase	       =self.get_overflowing_images(lowercase_							,   encoded_inputs['overflow_to_sample_mapping']					)
        _lowercase	       =images

        return encoded_inputs




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =[]
        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   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        return self.tokenizer.batch_decode(*lowercase_							,   **lowercase_					)




  def   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        return self.tokenizer.decode(*lowercase_							,   **lowercase_					)




  @property
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        return ["input_ids", "bbox", "attention_mask", "image"]




  @property
  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        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   A__     (				self					)  -> Any:
        '''simple docstring'''






        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,   lowercase_							,   )
        return self.image_processor









'''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


__lowerCAmelCase							:	Optional[Any]			=logging.get_logger(__name__)






class    UpperCAmelCase	:
 __lowercase   		=				42
 __lowercase   		=				None
 @staticmethod
 def      UpperCAmelCase_							(				)->							Dict:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							str:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :int							)->							Any:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     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      UpperCAmelCase_							(				cls      :int							)->							Any:
     return F"`pip install {cls.pip_package or cls.name}`"






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""optuna"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							int:
     return is_optuna_available()
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[Any]							)->							Tuple:
     return run_hp_search_optuna(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]							)->							Optional[Any]:
     return default_hp_space_optuna(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""ray"""
 __lowercase   		=				"""'ray[tune]'"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							str:
     return is_ray_available()
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[str]							)->							int:
     return run_hp_search_ray(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							int:
     return default_hp_space_ray(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sigopt"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							Union[str, Any]:
     return is_sigopt_available()
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							Dict:
     return run_hp_search_sigopt(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return default_hp_space_sigopt(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""wandb"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							List[str]:
     return is_wandb_available()
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							List[str]:
     return run_hp_search_wandb(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :str							)->							Dict:
     return default_hp_space_wandb(lowercase_							)

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





def 						UpperCamelCase				(							):
    A__				=			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase       ) > 0:
        A__				=			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()       )       )



