from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
      from ...processing_utils import ProcessorMixin
      from ...utils import TensorType


A     :  List[Any]					     =       logging.get_logger(__name__)

A     :  Optional[int]					     =       {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''layoutlmv3'''







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       str=50_265  ,     __magic_name__ :       Optional[int]=768  ,     __magic_name__ :       List[Any]=12  ,     __magic_name__ :       Tuple=12  ,     __magic_name__ :       List[str]=3_072  ,     __magic_name__ :       Dict="gelu"  ,     __magic_name__ :       Union[str, Any]=0.1  ,     __magic_name__ :       Dict=0.1  ,     __magic_name__ :       List[Any]=512  ,     __magic_name__ :       List[Any]=2  ,     __magic_name__ :       str=0.02  ,     __magic_name__ :       int=1e-5  ,     __magic_name__ :       str=1  ,     __magic_name__ :       List[Any]=0  ,     __magic_name__ :       Tuple=2  ,     __magic_name__ :       List[str]=1_024  ,     __magic_name__ :       int=128  ,     __magic_name__ :       Tuple=128  ,     __magic_name__ :       Union[str, Any]=True  ,     __magic_name__ :       List[Any]=32  ,     __magic_name__ :       Union[str, Any]=128  ,     __magic_name__ :       Optional[int]=64  ,     __magic_name__ :       Optional[Any]=256  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       Optional[Any]=True  ,     __magic_name__ :       Union[str, Any]=224  ,     __magic_name__ :       Union[str, Any]=3  ,     __magic_name__ :       int=16  ,     __magic_name__ :       List[Any]=None  ,     **__magic_name__ :       List[Any]  ,     )  ->	Union[str, Any]:
        super().__init__(
            vocab_size=__magic_name__  ,     hidden_size=__magic_name__  ,     num_hidden_layers=__magic_name__  ,     num_attention_heads=__magic_name__  ,     intermediate_size=__magic_name__  ,     hidden_act=__magic_name__  ,     hidden_dropout_prob=__magic_name__  ,     attention_probs_dropout_prob=__magic_name__  ,     max_position_embeddings=__magic_name__  ,     type_vocab_size=__magic_name__  ,     initializer_range=__magic_name__  ,     layer_norm_eps=__magic_name__  ,     pad_token_id=__magic_name__  ,     bos_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     **__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  max_ad_position_embeddings
        SCREAMING_SNAKE_CASE_          =  coordinate_size
        SCREAMING_SNAKE_CASE_          =  shape_size
        SCREAMING_SNAKE_CASE_          =  has_relative_attention_bias
        SCREAMING_SNAKE_CASE_          =  rel_pos_bins
        SCREAMING_SNAKE_CASE_          =  max_rel_pos
        SCREAMING_SNAKE_CASE_          =  has_spatial_attention_bias
        SCREAMING_SNAKE_CASE_          =  rel_ad_pos_bins
        SCREAMING_SNAKE_CASE_          =  max_rel_ad_pos
        SCREAMING_SNAKE_CASE_          =  text_embed
        SCREAMING_SNAKE_CASE_          =  visual_embed
        SCREAMING_SNAKE_CASE_          =  input_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  patch_size
        SCREAMING_SNAKE_CASE_          =  classifier_dropout



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						version.parse('''1.12'''					)







  @property
  def    __A     (							self :       Dict   )  ->	Mapping[str, Mapping[int, str]]:
        # The order of inputs is different for question answering and sequence classification
        if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "sequence"}),
                      ("attention_mask", {0: "batch", 1: "sequence"}),
                      ("bbox", {0: "batch", 1: "sequence"}),
                      ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ]   )
        else:
              return OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "sequence"}),
                      ("bbox", {0: "batch", 1: "sequence"}),
                      ("attention_mask", {0: "batch", 1: "sequence"}),
                      ("pixel_values", {0: "batch", 1: "num_channels"}),
                  ]   )







  @property
  def    __A     (							self :       Optional[int]   )  ->	float:
        return 1e-5







  @property
  def    __A     (							self :       Optional[int]   )  ->	int:
        return 12







  def    __A     (							self :       str  ,     __magic_name__ :       "ProcessorMixin"  ,     __magic_name__ :       int = -1  ,     __magic_name__ :       int = -1  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       Optional["TensorType"] = None  ,     __magic_name__ :       int = 3  ,     __magic_name__ :       int = 40  ,     __magic_name__ :       int = 40  ,     )  ->	Mapping[str, Any]:
        setattr(processor.image_processor  ,     "apply_ocr"  ,     __magic_name__   )

        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        SCREAMING_SNAKE_CASE_          =  compute_effective_axis_dimension(
            __magic_name__  ,     fixed_dimension=OnnxConfig.default_fixed_batch  ,     num_token_to_add=0   )
        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        SCREAMING_SNAKE_CASE_          =  processor.tokenizer.num_special_tokens_to_add(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  compute_effective_axis_dimension(
            __magic_name__  ,     fixed_dimension=OnnxConfig.default_fixed_sequence  ,     num_token_to_add=__magic_name__   )
        # Generate dummy inputs according to compute batch and sequence
        SCREAMING_SNAKE_CASE_          =  [[" ".join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

        # Generate dummy bounding boxes
        SCREAMING_SNAKE_CASE_          =  [[[48, 84, 73, 128]]] * batch_size

        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
        SCREAMING_SNAKE_CASE_          =  self._generate_dummy_images(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  dict(
            processor(
                __magic_name__  ,     text=__magic_name__  ,     boxes=__magic_name__  ,     return_tensors=__magic_name__  ,     )   )

        return inputs



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import jax
      import jaxlib

A     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
        super().__init__(features=__magic_name__   )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__magic_name__  ,     __magic_name__   ):
              raise ValueError(
                  F'''Expected {device} to be a `str` not {type(__magic_name__   )}, as `jaxlib.xla_extension.Device` '''
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."   )
        SCREAMING_SNAKE_CASE_          =  device if isinstance(__magic_name__  ,     __magic_name__   ) else str(jax.devices()[0]   )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()   ):
              logger.warning(
                  F'''Device with string identifier {self.device} not listed among the available '''
                  F'''devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '''
                  F'''device: {str(jax.devices()[0]   )}.'''   )
              SCREAMING_SNAKE_CASE_          =  str(jax.devices()[0]   )
        SCREAMING_SNAKE_CASE_          =  jnp_array_kwargs







  @staticmethod
  def    __A     (							)  ->	Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(__magic_name__   ): device for device in jax.devices()}







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     __magic_name__   ) and column:
              if all(
                  isinstance(__magic_name__  ,     jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                    return jnp.stack(__magic_name__  ,     axis=0   )
        return column







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     (str, bytes, type(__magic_name__   ))   ):
              return value
        elif isinstance(__magic_name__  ,     (np.character, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.character   ):
              return value.tolist()

        SCREAMING_SNAKE_CASE_          =  {}

        if isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.integer   ):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
              else:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
        elif isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.floating   ):
              SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__magic_name__  ,     PIL.Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]   ):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__magic_name__  ,     torch.Tensor   ):
                    return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
        if hasattr(__magic_name__  ,     "__array__"   ) and not isinstance(__magic_name__  ,     jax.Array   ):
              SCREAMING_SNAKE_CASE_          =  data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__magic_name__  ,     np.ndarray   ):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        elif isinstance(__magic_name__  ,     (list, tuple)   ):
              return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        return self._tensorize(__magic_name__   )







  def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
        return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
        return self.recursive_tensorize(__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
        return column







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        for column_name in batch:
              SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
        return batch


