


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    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: str      =					['''image_processor''', '''tokenizer''']
 _UpperCamelCase				: List[Any]      =					'''LayoutLMv2ImageProcessor'''
 _UpperCamelCase				: str      =					('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
 def __init__(			self					: List[Any]       ,    _a					: int=None       ,    _a					: Optional[int]=None       ,    **_a					: List[str]				)    ->			Optional[int]:
      UpperCAmelCase						:				Optional[int]		  =			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       ,    )
           UpperCAmelCase						:				List[str]		  =			kwargs.pop("feature_extractor"				)

      UpperCAmelCase						:				Optional[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					: Optional[Any]       ,    _a					: List[Any]       ,    _a					: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,    _a					: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,    _a					: Union[List[List[int]], List[List[List[int]]]] = None       ,    _a					: Optional[Union[List[int], List[List[int]]]] = None       ,    _a					: bool = True       ,    _a					: Union[bool, str, PaddingStrategy] = False       ,    _a					: Union[bool, str, TruncationStrategy] = False       ,    _a					: Optional[int] = None       ,    _a					: int = 0       ,    _a					: Optional[int] = None       ,    _a					: Optional[bool] = None       ,    _a					: Optional[bool] = None       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = False       ,    _a					: bool = True       ,    _a					: Optional[Union[str, TensorType]] = None       ,    **_a					: 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."				)

      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
      UpperCAmelCase						:				str		  =			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				):
                UpperCAmelCase						:				Optional[Any]		  =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCAmelCase						:				int		  =			features["words"]

      UpperCAmelCase						:				List[Any]		  =			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
      UpperCAmelCase						:				Optional[Any]		  =			features.pop("pixel_values"				)
      if return_overflowing_tokens is True:
           UpperCAmelCase						:				Dict		  =			self.get_overflowing_images(_a       ,    encoded_inputs["overflow_to_sample_mapping"]				)
      UpperCAmelCase						:				List[Any]		  =			images

      return encoded_inputs
 def   _UpperCamelCase    (			self					: str       ,    _a					: Optional[int]       ,    _a					: Optional[Any]				)    ->			int:
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      UpperCAmelCase						:				Optional[int]		  =			[]
      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   _UpperCamelCase    (			self					: Union[str, Any]       ,    *_a					: str       ,    **_a					: Any				)    ->			Any:
      return self.tokenizer.batch_decode(*_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Any       ,    *_a					: Dict       ,    **_a					: List[str]				)    ->			List[str]:
      return self.tokenizer.decode(*_a       ,    **_a				)
 @property
 def   _UpperCamelCase    (			self					: int				)    ->			Optional[int]:
      return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
 @property
 def   _UpperCamelCase    (			self					: Tuple				)    ->			Optional[int]:
      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   _UpperCamelCase    (			self					: List[str]				)    ->			Any:
      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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

      _snake_case   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



