



'''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










'''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)





