


'''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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   # 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
   UpperCAmelCase							=     TapasConfig.from_json_file(lowerCAmelCase_   )
   # set absolute/relative position embeddings parameter
   UpperCAmelCase							=     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      UpperCAmelCase							=     TapasForQuestionAnswering(config=lowerCAmelCase_   )
   elif task == "WTQ":
      # run_task_main.py hparams
      UpperCAmelCase							=     4
      UpperCAmelCase							=     True
      # hparam_utils.py hparams
      UpperCAmelCase							=     0.66_4694
      UpperCAmelCase							=     0.20_7951
      UpperCAmelCase							=     0.12_1194
      UpperCAmelCase							=     True
      UpperCAmelCase							=     True
      UpperCAmelCase							=     False
      UpperCAmelCase							=     0.035_2513

      UpperCAmelCase							=     TapasForQuestionAnswering(config=lowerCAmelCase_   )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      UpperCAmelCase							=     4
      UpperCAmelCase							=     False
      # hparam_utils.py hparams
      UpperCAmelCase							=     36.4519
      UpperCAmelCase							=     0.90_3421
      UpperCAmelCase							=     222.088
      UpperCAmelCase							=     True
      UpperCAmelCase							=     True
      UpperCAmelCase							=     True
      UpperCAmelCase							=     0.76_3141

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

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

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""   )
   UpperCAmelCase							=     TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""  ,						model_max_length=5_1_2   )
   tokenizer.save_pretrained(lowerCAmelCase_   )

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


if __name__ == "__main__":
 __a     =			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     =			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__a     =			logging.get_logger(__name__)

__a     =			{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''nllb-moe'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]=1_2_8_1_1_2					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					Optional[int]=1_2					,       __lowerCamelCase						:					Union[str, Any]=4_0_9_6					,       __lowerCamelCase						:					List[str]=1_6					,       __lowerCamelCase						:					List[str]=1_2					,       __lowerCamelCase						:					int=4_0_9_6					,       __lowerCamelCase						:					Tuple=1_6					,       __lowerCamelCase						:					str=0.05					,       __lowerCamelCase						:					List[str]=0.05					,       __lowerCamelCase						:					List[Any]=True					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					str="relu"					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					List[str]=0.1					,       __lowerCamelCase						:					Optional[int]=0.1					,       __lowerCamelCase						:					List[Any]=0.0					,       __lowerCamelCase						:					Optional[Any]=0.02					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Tuple="float32"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=1_2_8					,       __lowerCamelCase						:					List[str]=6_4					,       __lowerCamelCase						:					List[Any]=4					,       __lowerCamelCase						:					Tuple=4					,       __lowerCamelCase						:					str=0.001					,       __lowerCamelCase						:					Optional[int]=0.001					,       __lowerCamelCase						:					Tuple="all"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					List[str]=1.0					,       __lowerCamelCase						:					Dict=0.2					,       __lowerCamelCase						:					Union[str, Any]=1					,       __lowerCamelCase						:					int=0					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					int=False					,       **__lowerCamelCase						:					str					,       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     max_position_embeddings
         UpperCAmelCase							=     d_model
         UpperCAmelCase							=     encoder_ffn_dim
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     encoder_attention_heads
         UpperCAmelCase							=     decoder_ffn_dim
         UpperCAmelCase							=     decoder_layers
         UpperCAmelCase							=     decoder_attention_heads
         UpperCAmelCase							=     dropout
         UpperCAmelCase							=     attention_dropout
         UpperCAmelCase							=     activation_dropout
         UpperCAmelCase							=     activation_function
         UpperCAmelCase							=     init_std
         UpperCAmelCase							=     encoder_layerdrop
         UpperCAmelCase							=     decoder_layerdrop
         UpperCAmelCase							=     use_cache
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase							=     router_z_loss_coef
         UpperCAmelCase							=     router_aux_loss_coef
         UpperCAmelCase							=     decoder_sparse_step
         UpperCAmelCase							=     encoder_sparse_step
         UpperCAmelCase							=     num_experts
         UpperCAmelCase							=     expert_capacity
         UpperCAmelCase							=     router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""       )
         UpperCAmelCase							=     router_dtype

         UpperCAmelCase							=     router_ignore_padding_tokens
         UpperCAmelCase							=     batch_prioritized_routing
         UpperCAmelCase							=     second_expert_policy
         UpperCAmelCase							=     normalize_router_prob_before_dropping
         UpperCAmelCase							=     moe_eval_capacity_token_fraction
         UpperCAmelCase							=     moe_token_dropout
         UpperCAmelCase							=     output_router_logits
         super().__init__(
             pad_token_id=__lowerCamelCase					,       bos_token_id=__lowerCamelCase					,       eos_token_id=__lowerCamelCase					,       is_encoder_decoder=__lowerCamelCase					,       decoder_start_token_id=__lowerCamelCase					,       **__lowerCamelCase					,       )



