code: 
  
  
  
  '''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__						:			Optional[Any]										=			logging.get_logger(__name__)
  
  a__						:			Optional[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        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''layoutlm'''
  
  
  
   def __init__(  self		,	lowercase=3_0_5_2_2		,	lowercase=7_6_8		,	lowercase=1_2		,	lowercase=1_2		,	lowercase=3_0_7_2		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=2		,	lowercase=0.02		,	lowercase=1E-12		,	lowercase=0		,	lowercase="absolute"		,	lowercase=True		,	lowercase=1_0_2_4		,	**lowercase		,	)  ->							Dict:
    super().__init__(pad_token_id=lowercase		,	**lowercase			)
    __UpperCamelCase				      =  vocab_size
    __UpperCamelCase				      =  hidden_size
    __UpperCamelCase				      =  num_hidden_layers
    __UpperCamelCase				      =  num_attention_heads
    __UpperCamelCase				      =  hidden_act
    __UpperCamelCase				      =  intermediate_size
    __UpperCamelCase				      =  hidden_dropout_prob
    __UpperCamelCase				      =  attention_probs_dropout_prob
    __UpperCamelCase				      =  max_position_embeddings
    __UpperCamelCase				      =  type_vocab_size
    __UpperCamelCase				      =  initializer_range
    __UpperCamelCase				      =  layer_norm_eps
    __UpperCamelCase				      =  position_embedding_type
    __UpperCamelCase				      =  use_cache
    __UpperCamelCase				      =  max_ad_position_embeddings
  
  
  class        UpperCAmelCase__  (    UpperCAmelCase_):
  
  
  
   def __init__(  self		,	lowercase		,	lowercase = "default"		,	lowercase = None		,	)  ->							Optional[Any]:
    super().__init__(lowercase		,	task=lowercase		,	patching_specs=lowercase			)
    __UpperCamelCase				      =  config.max_ad_position_embeddings - 1
  
  
  
   @property
   def    __lowerCamelCase				(  self			)  ->							Mapping[str, Mapping[int, str]]:
    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    __lowerCamelCase				(  self		,	lowercase		,	lowercase = -1		,	lowercase = -1		,	lowercase = False		,	lowercase = None		,	)  ->							Mapping[str, Any]:
    __UpperCamelCase				      =  super().generate_dummy_inputs(
        lowercase		,	batch_size=lowercase		,	seq_length=lowercase		,	is_pair=lowercase		,	framework=lowercase			)
  
    # Generate a dummy bbox
    __UpperCamelCase				      =  [4_8, 8_4, 7_3, 1_2_8]
  
    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
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  input_dict["""input_ids"""].shape
    __UpperCamelCase				      =  torch.tensor([*[box] * seq_length]			).tile(lowercase		,	1		,	1			)
    return input_dict
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  