

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


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""gptsan-japanese"""
  _a		    =						[
      """past_key_values""",
  ]
  _a		    =						{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }




  def __init__(       self							,     lowerCAmelCase=36_000							,     lowerCAmelCase=1_280							,     lowerCAmelCase=1_024							,     lowerCAmelCase=8_192							,     lowerCAmelCase=4_096							,     lowerCAmelCase=128							,     lowerCAmelCase=10							,     lowerCAmelCase=0							,     lowerCAmelCase=16							,     lowerCAmelCase=16							,     lowerCAmelCase=128							,     lowerCAmelCase=0.0							,     lowerCAmelCase=1e-5							,     lowerCAmelCase=False							,     lowerCAmelCase=0.0							,     lowerCAmelCase="float32"							,     lowerCAmelCase=False							,     lowerCAmelCase=False							,     lowerCAmelCase=False							,     lowerCAmelCase=0.002							,     lowerCAmelCase=False							,     lowerCAmelCase=True							,     lowerCAmelCase=35_998							,     lowerCAmelCase=35_995							,     lowerCAmelCase=35_999							,     **lowerCAmelCase							,     )						->				str:
        '''simple docstring'''






        _lowercase			 =vocab_size
        _lowercase			 =max_position_embeddings
        _lowercase			 =d_model
        _lowercase			 =d_ff
        _lowercase			 =d_ext
        _lowercase			 =d_spout
        _lowercase			 =num_switch_layers
        _lowercase			 =num_ext_layers
        _lowercase			 =num_switch_layers + num_ext_layers
        _lowercase			 =num_heads
        _lowercase			 =num_experts
        _lowercase			 =expert_capacity
        _lowercase			 =dropout_rate
        _lowercase			 =layer_norm_epsilon
        _lowercase			 =router_bias
        _lowercase			 =router_jitter_noise
        _lowercase			 =router_dtype
        _lowercase			 =router_ignore_padding_tokens
        _lowercase			 =output_hidden_states
        _lowercase			 =output_attentions
        _lowercase			 =initializer_factor
        _lowercase			 =output_router_logits
        _lowercase			 =use_cache

        super().__init__(
            separator_token_id=lowerCAmelCase							,     pad_token_id=lowerCAmelCase							,     eos_token_id=lowerCAmelCase							,     **lowerCAmelCase							,     )





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   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _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			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _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__":
  lowercase_             =     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.'
  )
  lowercase_             =     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,
  )


