






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =  TypeVar("DatasetType", Dataset, IterableDataset)





def        _snake_case   (_snake_case				:       List[DatasetType]      ,    _snake_case				:       Optional[List[float]] = None      ,    _snake_case				:       Optional[int] = None      ,    _snake_case				:       Optional[DatasetInfo] = None      ,    _snake_case				:       Optional[NamedSplit] = None      ,    _snake_case				:       Literal["first_exhausted", "all_exhausted"] = "first_exhausted"      ,    )					->	DatasetType:
   from .arrow_dataset import Dataset
   from .iterable_dataset import IterableDataset

   if not datasets:
      raise ValueError('Unable to interleave an empty list of datasets.')
   for i, dataset in enumerate(_snake_case):
      if not isinstance(_snake_case      ,    (Dataset, IterableDataset)):
         if isinstance(_snake_case      ,    (DatasetDict, IterableDatasetDict)):
            if not dataset:
               raise ValueError(
                   f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
                   'is an empty dataset dictionary.')
            raise ValueError(
                f'''Dataset at position {i} has at least one split: {list(_snake_case)}\n'''
                f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_snake_case))}\']''')
         raise ValueError(
             f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_snake_case).__name__}.''')
      if i == 0:
         _lowercase       ,       _lowercase              =(
             (Dataset, IterableDataset) if isinstance(_snake_case      ,    _snake_case) else (IterableDataset, Dataset)
         )
      elif not isinstance(_snake_case      ,    _snake_case):
         raise ValueError(
             f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''')
   if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
      raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''')
   if dataset_type is Dataset:
      return _interleave_map_style_datasets(
          _snake_case      ,    _snake_case      ,    _snake_case      ,    info=_snake_case      ,    split=_snake_case      ,    stopping_strategy=_snake_case)
   else:
      return _interleave_iterable_datasets(
          _snake_case      ,    _snake_case      ,    _snake_case      ,    info=_snake_case      ,    split=_snake_case      ,    stopping_strategy=_snake_case)





def        _snake_case   (_snake_case				:       List[DatasetType]      ,    _snake_case				:       Optional[DatasetInfo] = None      ,    _snake_case				:       Optional[NamedSplit] = None      ,    _snake_case				:       int = 0      ,    )					->	DatasetType:

   if not dsets:
      raise ValueError('Unable to concatenate an empty list of datasets.')
   for i, dataset in enumerate(_snake_case):
      if not isinstance(_snake_case      ,    (Dataset, IterableDataset)):
         if isinstance(_snake_case      ,    (DatasetDict, IterableDatasetDict)):
            if not dataset:
               raise ValueError(
                   f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
                   'is an empty dataset dictionary.')
            raise ValueError(
                f'''Dataset at position {i} has at least one split: {list(_snake_case)}\n'''
                f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_snake_case))}\']''')
         raise ValueError(
             f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_snake_case).__name__}.''')
      if i == 0:
         _lowercase       ,       _lowercase              =(
             (Dataset, IterableDataset) if isinstance(_snake_case      ,    _snake_case) else (IterableDataset, Dataset)
         )
      elif not isinstance(_snake_case      ,    _snake_case):
         raise ValueError(
             f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''')
   if dataset_type is Dataset:
      return _concatenate_map_style_datasets(_snake_case      ,    info=_snake_case      ,    split=_snake_case      ,    axis=_snake_case)
   else:
      return _concatenate_iterable_datasets(_snake_case      ,    info=_snake_case      ,    split=_snake_case      ,    axis=_snake_case)










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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					='''layoutlmv3'''





       def __init__(				self	:Optional[int],      snake_case	:int=5_0265,      snake_case	:int=768,      snake_case	:Any=12,      snake_case	:Union[str, Any]=12,      snake_case	:List[str]=3072,      snake_case	:List[str]="gelu",      snake_case	:List[str]=0.1,      snake_case	:Optional[int]=0.1,      snake_case	:Optional[int]=512,      snake_case	:Tuple=2,      snake_case	:Optional[Any]=0.0_2,      snake_case	:Optional[int]=1e-5,      snake_case	:Union[str, Any]=1,      snake_case	:Dict=0,      snake_case	:Tuple=2,      snake_case	:Tuple=1024,      snake_case	:Optional[Any]=128,      snake_case	:Optional[Any]=128,      snake_case	:List[str]=True,      snake_case	:str=32,      snake_case	:Optional[int]=128,      snake_case	:Dict=64,      snake_case	:List[str]=256,      snake_case	:Optional[Any]=True,      snake_case	:Optional[Any]=True,      snake_case	:Dict=True,      snake_case	:Optional[Any]=224,      snake_case	:int=3,      snake_case	:int=16,      snake_case	:str=None,      **snake_case	:Dict,      ):



          """simple docstring"""

          super().__init__(
              vocab_size=snake_case,      hidden_size=snake_case,      num_hidden_layers=snake_case,      num_attention_heads=snake_case,      intermediate_size=snake_case,      hidden_act=snake_case,      hidden_dropout_prob=snake_case,      attention_probs_dropout_prob=snake_case,      max_position_embeddings=snake_case,      type_vocab_size=snake_case,      initializer_range=snake_case,      layer_norm_eps=snake_case,      pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      **snake_case,      )
          _lowercase              =max_ad_position_embeddings
          _lowercase              =coordinate_size
          _lowercase              =shape_size
          _lowercase              =has_relative_attention_bias
          _lowercase              =rel_pos_bins
          _lowercase              =max_rel_pos
          _lowercase              =has_spatial_attention_bias
          _lowercase              =rel_ad_pos_bins
          _lowercase              =max_rel_ad_pos
          _lowercase              =text_embed
          _lowercase              =visual_embed
          _lowercase              =input_size
          _lowercase              =num_channels
          _lowercase              =patch_size
          _lowercase              =classifier_dropout






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=version.parse('''1.12''' )





       @property
       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          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 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 1e-5





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 12





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:"ProcessorMixin",      snake_case	:int = -1,      snake_case	:int = -1,      snake_case	:bool = False,      snake_case	:Optional["TensorType"] = None,      snake_case	:int = 3,      snake_case	:int = 40,      snake_case	:int = 40,      ):



          """simple docstring"""

          setattr(processor.image_processor,      'apply_ocr',      snake_case)

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

          # Generate dummy bounding boxes
          _lowercase              =[[[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)
          _lowercase              =self._generate_dummy_images(snake_case,      snake_case,      snake_case,      snake_case)

          _lowercase              =dict(
              processor(
                  snake_case,      text=snake_case,      boxes=snake_case,      return_tensors=snake_case,      ))

          return inputs


