
"""simple docstring"""




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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


_A      :	Dict       		=	logging.get_logger(__name__)

_A      :	Union[str, Any]       		=	{
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """layoutlm"""
   def __init__(      self     ,   _a=30_522     ,   _a=768     ,   _a=12     ,   _a=12     ,   _a=3_072     ,   _a="gelu"     ,   _a=0.1     ,   _a=0.1     ,   _a=512     ,   _a=2     ,   _a=0.0_2     ,   _a=1E-12     ,   _a=0     ,   _a="absolute"     ,   _a=True     ,   _a=1_024     ,   **_a     ,   ):
          super().__init__(pad_token_id=_a     ,   **_a    )
          lowercase   :    Dict			=							vocab_size
          lowercase   :    Tuple			=							hidden_size
          lowercase   :    Optional[int]			=							num_hidden_layers
          lowercase   :    Optional[int]			=							num_attention_heads
          lowercase   :    int			=							hidden_act
          lowercase   :    List[str]			=							intermediate_size
          lowercase   :    int			=							hidden_dropout_prob
          lowercase   :    Any			=							attention_probs_dropout_prob
          lowercase   :    Any			=							max_position_embeddings
          lowercase   :    List[str]			=							type_vocab_size
          lowercase   :    Optional[Any]			=							initializer_range
          lowercase   :    Tuple			=							layer_norm_eps
          lowercase   :    Dict			=							position_embedding_type
          lowercase   :    List[Any]			=							use_cache
          lowercase   :    List[str]			=							max_ad_position_embeddings


class 		a__						(				a_						):
   def __init__(      self     ,   _a     ,   _a = "default"     ,   _a = None     ,   ):
          super().__init__(_a     ,   task=_a     ,   patching_specs=_a    )
          lowercase   :    int			=							config.max_ad_position_embeddings - 1
   @property
   def       __magic_name__      (      self    ):
          return OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "sequence"}),
                  ("bbox", {0: "batch", 1: "sequence"}),
                  ("attention_mask", {0: "batch", 1: "sequence"}),
                  ("token_type_ids", {0: "batch", 1: "sequence"}),
              ]    )

   def       __magic_name__      (      self     ,   _a     ,   _a = -1     ,   _a = -1     ,   _a = False     ,   _a = None     ,   ):
          lowercase   :    Tuple			=							super().generate_dummy_inputs(
              _a     ,   batch_size=_a     ,   seq_length=_a     ,   is_pair=_a     ,   framework=_a    )

          # Generate a dummy bbox
          lowercase   :    str			=							[48, 84, 73, 128]

          if not framework == TensorType.PYTORCH:
                 raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."    )

          if not is_torch_available():
                 raise ValueError("Cannot generate dummy inputs without PyTorch installed."    )
          import torch

          lowercase		,   lowercase   :    List[str]			=							input_dict["input_ids"].shape
          lowercase   :    Union[str, Any]			=							torch.tensor([*[box] * seq_length]    ).tile(_a     ,   1     ,   1    )
          return input_dict




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A      :	str       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """tvlt"""
   def __init__(      self     ,   _a=224     ,   _a=2_048     ,   _a=128     ,   _a=[16, 16]     ,   _a=[16, 16]     ,   _a=3     ,   _a=1     ,   _a=8     ,   _a=768     ,   _a=12     ,   _a=12     ,   _a=3_072     ,   _a="gelu"     ,   _a=0.0     ,   _a=0.0     ,   _a=0.0_2     ,   _a=1E-6     ,   _a=True     ,   _a=False     ,   _a=16     ,   _a=512     ,   _a=8     ,   _a=2_048     ,   _a=0.7_5     ,   _a=0.1_5     ,   _a="frame-level"     ,   _a=True     ,   _a=True     ,   _a="classification"     ,   **_a     ,   ):
          super().__init__(**_a    )

          if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
                     f"""got {audio_mask_type}"""    )

          lowercase   :    Union[str, Any]			=							image_size
          lowercase   :    Dict			=							spectrogram_length
          lowercase   :    Union[str, Any]			=							frequency_length
          lowercase   :    Union[str, Any]			=							image_patch_size
          lowercase   :    Any			=							audio_patch_size
          lowercase   :    int			=							num_image_channels
          lowercase   :    Union[str, Any]			=							num_audio_channels
          lowercase   :    Any			=							num_frames

          lowercase   :    List[str]			=							hidden_size
          lowercase   :    List[str]			=							num_hidden_layers
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    int			=							intermediate_size
          lowercase   :    Tuple			=							hidden_act
          lowercase   :    int			=							hidden_dropout_prob
          lowercase   :    Dict			=							attention_probs_dropout_prob
          lowercase   :    List[Any]			=							initializer_range
          lowercase   :    Union[str, Any]			=							layer_norm_eps
          lowercase   :    Dict			=							qkv_bias
          lowercase   :    List[Any]			=							use_mean_pooling

          lowercase   :    List[Any]			=							decoder_num_attention_heads
          lowercase   :    Union[str, Any]			=							decoder_hidden_size
          lowercase   :    Dict			=							decoder_num_hidden_layers
          lowercase   :    Union[str, Any]			=							decoder_intermediate_size
          lowercase   :    List[str]			=							pixel_mask_ratio
          lowercase   :    str			=							audio_mask_ratio
          lowercase   :    Any			=							audio_mask_type

          lowercase   :    Union[str, Any]			=							task_matching
          lowercase   :    Optional[Any]			=							task_mae
          lowercase   :    List[str]			=							loss_type


