from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__		:       str       						=       {
    "configuration_layoutlmv3": [
        "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LayoutLMv3Config",
        "LayoutLMv3OnnxConfig",
    ],
    "processing_layoutlmv3": ["LayoutLMv3Processor"],
    "tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       int       						=       ["LayoutLMv3TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       Dict       						=       [
          "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LayoutLMv3ForQuestionAnswering",
          "LayoutLMv3ForSequenceClassification",
          "LayoutLMv3ForTokenClassification",
          "LayoutLMv3Model",
          "LayoutLMv3PreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       Any       						=       [
          "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLayoutLMv3ForQuestionAnswering",
          "TFLayoutLMv3ForSequenceClassification",
          "TFLayoutLMv3ForTokenClassification",
          "TFLayoutLMv3Model",
          "TFLayoutLMv3PreTrainedModel",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       List[str]       						=       ["LayoutLMv3FeatureExtractor"]
      SCREAMING_SNAKE_CASE__		:       List[str]       						=       ["LayoutLMv3ImageProcessor"]


if TYPE_CHECKING:
      from .configuration_layoutlmva import (
          LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LayoutLMvaConfig,
          LayoutLMvaOnnxConfig,
      )
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_layoutlmva import (
                TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLayoutLMvaForQuestionAnswering,
                TFLayoutLMvaForSequenceClassification,
                TFLayoutLMvaForTokenClassification,
                TFLayoutLMvaModel,
                TFLayoutLMvaPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
            from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
      import sys

      SCREAMING_SNAKE_CASE__		:       Any       						=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


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

UpperCamelCase_		:     str          =		{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		"""ibert"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=30_522   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-12   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE="absolute"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE="none"   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= hidden_act
          _snake_case				= intermediate_size
          _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				= position_embedding_type
          _snake_case				= quant_mode
          _snake_case				= force_dequant







class   _a							(		__lowerCAmelCase				):






     @property
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case				= {0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case				= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]       )


