



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   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      # Initialise PyTorch model.
      # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
      # TapasConfig to False.

      # initialize configuration from json file
      lowercase__     =  TapasConfig.from_json_file(A__	)
      # set absolute/relative position embeddings parameter
      lowercase__     =  reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            lowercase__     =  TapasForQuestionAnswering(config=A__	)
      elif task == "WTQ":
            # run_task_main.py hparams
            lowercase__     =  4
            lowercase__     =  True
            # hparam_utils.py hparams
            lowercase__     =  0.66_46_94
            lowercase__     =  0.20_79_51
            lowercase__     =  0.12_11_94
            lowercase__     =  True
            lowercase__     =  True
            lowercase__     =  False
            lowercase__     =  0.0_35_25_13

            lowercase__     =  TapasForQuestionAnswering(config=A__	)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            lowercase__     =  4
            lowercase__     =  False
            # hparam_utils.py hparams
            lowercase__     =  36.45_19
            lowercase__     =  0.90_34_21
            lowercase__     =  2_22.0_88
            lowercase__     =  True
            lowercase__     =  True
            lowercase__     =  True
            lowercase__     =  0.76_31_41

            lowercase__     =  TapasForQuestionAnswering(config=A__	)
      elif task == "TABFACT":
            lowercase__     =  TapasForSequenceClassification(config=A__	)
      elif task == "MLM":
            lowercase__     =  TapasForMaskedLM(config=A__	)
      elif task == "INTERMEDIATE_PRETRAINING":
            lowercase__     =  TapasModel(config=A__	)
      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(A__						,  A__						,  A__	)

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

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
      lowercase__     =  TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'						,  model_max_length=512	)
      tokenizer.save_pretrained(A__	)

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


if __name__ == "__main__":
       a__      :				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."
       )
       a__      :				Dict   	=			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,
       )





from typing import Mapping

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


a__      :				Union[str, Any]   	=			logging.get_logger(__name__)

a__      :				Optional[Any]   	=			{
    "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       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    "umt5"
   A    :  List[str]       				=    ["past_key_values"]


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int]=25_01_12			,				lowerCAmelCase  :			str=5_12			,				lowerCAmelCase  :			List[Any]=64			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Union[str, Any]=8			,				lowerCAmelCase  :			Tuple=None			,				lowerCAmelCase  :			Optional[Any]=6			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=1_28			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			List[str]=1E-6			,				lowerCAmelCase  :			Optional[int]=1.0			,				lowerCAmelCase  :			Optional[Any]="gated-gelu"			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			List[Any]="T5Tokenizer"			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			Any=0			,				**lowerCAmelCase  :			int			,				)	->				str:




         """simple docstring"""

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

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

         if len(lowerCAmelCase) > 1 and act_info[0] != "gated" or len(lowerCAmelCase) > 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":
               lowercase__     =  'gelu_new'


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return self.d_model


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         return self.num_heads



   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         return self.num_layers



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




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




         """simple docstring"""

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

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

         return common_inputs


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




         """simple docstring"""

         return 13



   @property
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				float:




         """simple docstring"""

         return 5E-4
