



'''simple docstring'''





from typing import Mapping

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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			'umt5'
 __lowerCamelCase       :			Optional[int]		=			['past_key_values']



 def __init__(self					,  A=250_112					,  A=512					,  A=64					,  A=1_024					,  A=8					,  A=None					,  A=6					,  A=32					,  A=128					,  A=0.1					,  A=1E-6					,  A=1.0					,  A="gated-gelu"					,  A=True					,  A=True					,  A="T5Tokenizer"					,  A=True					,  A=0					,  A=1					,  A=0					,  **A					,  )       ->	Any:


      """simple docstring"""



      super().__init__(
          is_encoder_decoder=A					,  tokenizer_class=A					,  tie_word_embeddings=A					,  pad_token_id=A					,  eos_token_id=A					,  decoder_start_token_id=A					,  **A					,  )
      _a           =    vocab_size
      _a           =    d_model
      _a           =    d_kv
      _a           =    d_ff
      _a           =    num_layers
      _a           =    (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      _a           =    num_heads
      _a           =    relative_attention_num_buckets
      _a           =    relative_attention_max_distance
      _a           =    dropout_rate
      _a           =    layer_norm_epsilon
      _a           =    initializer_factor
      _a           =    feed_forward_proj
      _a           =    use_cache

      _a           =    self.feed_forward_proj.split('''-'''			)
      _a           =    act_info[-1]
      _a           =    act_info[0] == '''gated'''

      if len(A			) > 1 and act_info[0] != "gated" or len(A			) > 2:
           raise ValueError(
               f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\''''			)

      if feed_forward_proj == "gated-gelu":
           _a           =    '''gelu_new'''



 @property
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      return self.d_model



 @property
 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      return self.num_heads



 @property
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      return self.num_layers






class __A     (    A		):



 '''simple docstring'''







 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    a__    (self			)       ->	Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a           =    {
          '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
          '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
      }
      if self.use_past:
           _a           =    '''past_encoder_sequence + sequence'''
           _a           =    {0: '''batch'''}
           _a           =    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
      else:
           _a           =    {0: '''batch''', 1: '''decoder_sequence'''}
           _a           =    {0: '''batch''', 1: '''decoder_sequence'''}

      if self.use_past:
           self.fill_with_past_key_values_(A					,  direction='''inputs'''			)

      return common_inputs



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return 13



 @property
 def    a__    (self			)       ->	float:


      """simple docstring"""



      return 5E-4








"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     else:
          raise ValueError(f'Task {task} not supported.'							)

     print(f'Building PyTorch model from configuration: {config}'							)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

     print("Used relative position embeddings:"		,    model.config.reset_position_index_per_cell							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase							:   Tuple				=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )
