






from typing import List, Optional, Union

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


__lowerCamelCase	:		List[Any]										=   logging.get_logger(__name__)

__lowerCamelCase	:		Optional[Any]										=   {
    """huggingface/time-series-transformer-tourism-monthly""": (
        """https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class  _lowercase   (   _A						):
      _a							:						Any										=							'time_series_transformer'
      _a							:						List[str]										=							{
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
          'num_hidden_layers': 'encoder_layers',
      }
      def __init__(     self				,			a = None				,			a = None				,			a = "student_t"				,			a = "nll"				,			a = 1				,			a = [1, 2, 3, 4, 5, 6, 7]				,			a = "mean"				,			a = 0				,			a = 0				,			a = 0				,			a = 0				,			a = None				,			a = None				,			a = 3_2				,			a = 3_2				,			a = 2				,			a = 2				,			a = 2				,			a = 2				,			a = True				,			a = "gelu"				,			a = 6_4				,			a = 0.1				,			a = 0.1				,			a = 0.1				,			a = 0.1				,			a = 0.1				,			a = 1_0_0				,			a = 0.02				,			a=True				,			**a				,			):
       # time series specific configuration
       snake_case__  :    List[str]							       =prediction_length
       snake_case__  :    int							       =context_length or prediction_length
       snake_case__  :    List[str]							       =distribution_output
       snake_case__  :    List[str]							       =loss
       snake_case__  :    List[str]							       =input_size
       snake_case__  :    Tuple							       =num_time_features
       snake_case__  :    Any							       =lags_sequence
       snake_case__  :    Optional[int]							       =scaling
       snake_case__  :    Tuple							       =num_dynamic_real_features
       snake_case__  :    int							       =num_static_real_features
       snake_case__  :    Optional[int]							       =num_static_categorical_features
       if cardinality and num_static_categorical_features > 0:
        if len(a   ) != num_static_categorical_features:
         raise ValueError(
             """The cardinality should be a list of the same length as `num_static_categorical_features`"""   )
        snake_case__  :    List[str]							       =cardinality
       else:
        snake_case__  :    Any							       =[0]
       if embedding_dimension and num_static_categorical_features > 0:
        if len(a   ) != num_static_categorical_features:
         raise ValueError(
             """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""   )
        snake_case__  :    List[Any]							       =embedding_dimension
       else:
        snake_case__  :    Optional[int]							       =[min(5_0				,			(cat + 1) // 2   ) for cat in self.cardinality]
       snake_case__  :    Tuple							       =num_parallel_samples

       # Transformer architecture configuration
       snake_case__  :    int							       =input_size * len(a   ) + self._number_of_features
       snake_case__  :    Union[str, Any]							       =d_model
       snake_case__  :    str							       =encoder_attention_heads
       snake_case__  :    str							       =decoder_attention_heads
       snake_case__  :    List[Any]							       =encoder_ffn_dim
       snake_case__  :    Optional[Any]							       =decoder_ffn_dim
       snake_case__  :    Dict							       =encoder_layers
       snake_case__  :    int							       =decoder_layers

       snake_case__  :    int							       =dropout
       snake_case__  :    Dict							       =attention_dropout
       snake_case__  :    Tuple							       =activation_dropout
       snake_case__  :    List[Any]							       =encoder_layerdrop
       snake_case__  :    str							       =decoder_layerdrop

       snake_case__  :    int							       =activation_function
       snake_case__  :    Dict							       =init_std

       snake_case__  :    List[str]							       =use_cache

       super().__init__(is_encoder_decoder=a				,			**a   )



      @property
      def 						lowercase__					(     self   ):
       return (
           sum(self.embedding_dimension   )
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   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(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT 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."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





