
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      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Union[str, Any]  =					["""image_processor""", """tokenizer"""]
    __lowercase							: Dict  =					"""LayoutLMv2ImageProcessor"""
    __lowercase							: List[Any]  =					("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
    def __init__(    self						,    snake_case_=None						,    snake_case_=None						,    **snake_case_     ):
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."						,    snake_case_						,    )
        _snake_case     :						List[str]						=   kwargs.pop("feature_extractor"     )

      _snake_case     :						Tuple						=   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__(snake_case_						,    snake_case_     )
    def __call__(    self						,    snake_case_						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = True						,    snake_case_ = False						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = 0						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_ = False						,    snake_case_ = False						,    snake_case_ = False						,    snake_case_ = False						,    snake_case_ = True						,    snake_case_ = None						,    **snake_case_						,    ):
      # 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
      _snake_case     :						List[Any]						=   self.image_processor(images=snake_case_						,    return_tensors=snake_case_     )

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

      _snake_case     :						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=snake_case_						,    add_special_tokens=snake_case_						,    padding=snake_case_						,    truncation=snake_case_						,    max_length=snake_case_						,    stride=snake_case_						,    pad_to_multiple_of=snake_case_						,    return_token_type_ids=snake_case_						,    return_attention_mask=snake_case_						,    return_overflowing_tokens=snake_case_						,    return_special_tokens_mask=snake_case_						,    return_offsets_mapping=snake_case_						,    return_length=snake_case_						,    verbose=snake_case_						,    return_tensors=snake_case_						,    **snake_case_						,    )

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

      return encoded_inputs
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      _snake_case     :						Tuple						=   []
      for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]     )

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

      return images_with_overflow
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )
    @property
    def 					lowerCamelCase__	(    self     ):
      return ["input_ids", "bbox", "attention_mask", "image"]
    @property
    def 					lowerCamelCase__	(    self     ):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,    snake_case_						,    )
      return self.image_processor_class


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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			List[str]					=							{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class      _UpperCAmelCase				(					_snake_case		,					_snake_case):
    __lowercase							: List[Any]  =					"""convnextv2"""
    def __init__(    self						,    snake_case_=3						,    snake_case_=4						,    snake_case_=4						,    snake_case_=None						,    snake_case_=None						,    snake_case_="gelu"						,    snake_case_=0.02						,    snake_case_=1E-12						,    snake_case_=0.0						,    snake_case_=2_24						,    snake_case_=None						,    snake_case_=None						,    **snake_case_						,    ):
      super().__init__(**snake_case_     )

      _snake_case     :						Tuple						=   num_channels
      _snake_case     :						Optional[int]						=   patch_size
      _snake_case     :						Tuple						=   num_stages
      _snake_case     :						int						=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
      _snake_case     :						str						=   [3, 3, 9, 3] if depths is None else depths
      _snake_case     :						int						=   hidden_act
      _snake_case     :						Tuple						=   initializer_range
      _snake_case     :						Union[str, Any]						=   layer_norm_eps
      _snake_case     :						Optional[int]						=   drop_path_rate
      _snake_case     :						Union[str, Any]						=   image_size
      _snake_case     :						List[Any]						=   ["stem"] + [F'stage{idx}' for idx in range(1						,    len(self.depths     ) + 1     )]
      _snake_case			,					_snake_case     :						Dict						=   get_aligned_output_features_output_indices(
          out_features=snake_case_						,    out_indices=snake_case_						,    stage_names=self.stage_names     )

