



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_):
     """simple docstring"""



     _A      =      ['image_processor', 'tokenizer']
     _A      =      'LayoutLMv3ImageProcessor'
     _A      =      ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







     def __init__(self					,      __a=None					,      __a=None					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		None
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."					,      __a					,      )
                 lowerCamelCase             =		kwargs.pop("feature_extractor"     )

           lowerCamelCase             =		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__(__a					,      __a     )







     def __call__(self					,      __a					,      __a = None					,      __a = None					,      __a = None					,      __a = None					,      __a = True					,      __a = False					,      __a = None					,      __a = None					,      __a = 0					,      __a = None					,      __a = None					,      __a = None					,      __a = False					,      __a = False					,      __a = False					,      __a = False					,      __a = True					,      __a = None					,      **__a					,      ):






           '''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."     )

           # first, apply the image processor
           lowerCamelCase             =		self.image_processor(images=__a					,      return_tensors=__a     )

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

           lowerCamelCase             =		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=__a					,      add_special_tokens=__a					,      padding=__a					,      truncation=__a					,      max_length=__a					,      stride=__a					,      pad_to_multiple_of=__a					,      return_token_type_ids=__a					,      return_attention_mask=__a					,      return_overflowing_tokens=__a					,      return_special_tokens_mask=__a					,      return_offsets_mapping=__a					,      return_length=__a					,      verbose=__a					,      return_tensors=__a					,      **__a					,      )

           # add pixel values
           lowerCamelCase             =		features.pop("pixel_values"     )
           if return_overflowing_tokens is True:
                 lowerCamelCase             =		self.get_overflowing_images(__a					,      encoded_inputs["overflow_to_sample_mapping"]     )
           lowerCamelCase             =		images

           return encoded_inputs







     def     _a   (self					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		[]
           for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]     )

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

           return images_with_overflow







     def     _a   (self					,      *__a					,      **__a     ):






           '''simple docstring'''



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







     def     _a   (self					,      *__a					,      **__a     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,      __a					,      )
           return self.image_processor_class







     @property
     def     _a   (self     ):






           '''simple docstring'''



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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)