



'''simple docstring'''




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							(			__a ):
	__SCREAMING_SNAKE_CASE   :		Tuple					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		int					=					'LayoutLMv3ImageProcessor'
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')


	def __init__(self       ,      lowercase=None       ,      lowercase=None       ,      **lowercase							):
				A_	:						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."""       ,      _A       ,      )
							A_	:						List[Any]	=     kwargs.pop("""feature_extractor"""							)

				A_	:						Union[str, Any]	=     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       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):


				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
				A_	:						int	=     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							):
										A_	:						str	=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
							A_	:						Union[str, Any]	=     features['''words''']

				A_	:						List[str]	=     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
				A_	:						int	=     features.pop("""pixel_values"""							)
				if return_overflowing_tokens is True:
							A_	:						Optional[Any]	=     self.get_overflowing_images(_A       ,      encoded_inputs["""overflow_to_sample_mapping"""]							)
				A_	:						str	=     images

				return encoded_inputs


	def     _a	(self       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     []
				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       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*_A       ,      **_A							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*_A       ,      **_A							)


	@property
	def     _a	(self							):
				return ["input_ids", "bbox", "attention_mask", "pixel_values"]


	@property
	def     _a	(self							):
				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							):
				warnings.warn(
				    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,      _A       ,      )
				return self.image_processor



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''RemBertTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''RemBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''RemBertForCausalLM''',
      '''RemBertForMaskedLM''',
      '''RemBertForMultipleChoice''',
      '''RemBertForQuestionAnswering''',
      '''RemBertForSequenceClassification''',
      '''RemBertForTokenClassification''',
      '''RemBertLayer''',
      '''RemBertModel''',
      '''RemBertPreTrainedModel''',
      '''load_tf_weights_in_rembert''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFRemBertForCausalLM''',
      '''TFRemBertForMaskedLM''',
      '''TFRemBertForMultipleChoice''',
      '''TFRemBertForQuestionAnswering''',
      '''TFRemBertForSequenceClassification''',
      '''TFRemBertForTokenClassification''',
      '''TFRemBertLayer''',
      '''TFRemBertModel''',
      '''TFRemBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
