
'''simple docstring'''




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowercase     :							Any       						=     logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    if not path:
        return "pipe"

    for ext in PipelineDataFormat.SUPPORTED_FORMATS:
        if path.endswith(__lowercase		):
            return ext

    raise Exception(
        F'Unable to determine file format from file extension {path}. '
        F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Optional[int]   =		pipeline(
        task=args.task						,							model=args.model if args.model else None						,							config=args.config						,							tokenizer=args.tokenizer						,							device=args.device						,							)
    A			:  Optional[Any]   =		try_infer_format_from_ext(args.input		) if args.format == """infer""" else args.format
    A			:  Union[str, Any]   =		PipelineDataFormat.from_str(
        format=__lowercase						,							output_path=args.output						,							input_path=args.input						,							column=args.column if args.column else nlp.default_input_names						,							overwrite=args.overwrite						,							)
    return RunCommand(__lowercase						,							__lowercase		)







class 					A							(				a__		):





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  Any   =		nlp
         A			:  Any   =		reader





     @staticmethod
     def 							__lowerCAmelCase				(				SCREAMING_SNAKE_CASE      )					->							List[Any]:






         """simple docstring"""






         A			:  List[Any]   =		parser.add_parser('''run'''		,     help='''Run a pipeline through the CLI'''      )
         run_parser.add_argument('''--task'''		,     choices=get_supported_tasks()		,     help='''Task to run'''      )
         run_parser.add_argument('''--input'''		,     type=__a		,     help='''Path to the file to use for inference'''      )
         run_parser.add_argument('''--output'''		,     type=__a		,     help='''Path to the file that will be used post to write results.'''      )
         run_parser.add_argument('''--model'''		,     type=__a		,     help='''Name or path to the model to instantiate.'''      )
         run_parser.add_argument('''--config'''		,     type=__a		,     help='''Name or path to the model\'s config to instantiate.'''      )
         run_parser.add_argument(
             '''--tokenizer'''		,     type=__a		,     help='''Name of the tokenizer to use. (default: same as the model name)'''      )
         run_parser.add_argument(
             '''--column'''		,     type=__a		,     help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''		,     )
         run_parser.add_argument(
             '''--format'''		,     type=__a		,     default='''infer'''		,     choices=PipelineDataFormat.SUPPORTED_FORMATS		,     help='''Input format to read from'''		,     )
         run_parser.add_argument(
             '''--device'''		,     type=__a		,     default=-1		,     help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''		,     )
         run_parser.add_argument('''--overwrite'''		,     action='''store_true'''		,     help='''Allow overwriting the output file.'''      )
         run_parser.set_defaults(func=__a      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  List[Any]   =		self._nlp, []

         for entry in self._reader:
             A			:  Union[str, Any]   =		nlp(**__a      ) if self._reader.is_multi_columns else nlp(__a      )
             if isinstance(__a		,     __a      ):
                 outputs.append(__a      )
             else:
                 outputs += output

        # Saving data
         if self._nlp.binary_output:
             A			:  str   =		self._reader.save_binary(__a      )
             logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}'      )
         else:
             self._reader.save(__a      )










'''simple docstring'''







from typing import List, Optional

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


lowercase      :   Optional[Any]      =       logging.get_logger(__name__)

lowercase      :   Any      =       {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[str]          =					"""autoformer"""
     lowerCAmelCase_  :     int          =					{
         """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 = True							,__a = 0							,__a = 0							,__a = 0							,__a = 0							,__a = None							,__a = None							,__a = 64							,__a = 2							,__a = 2							,__a = 2							,__a = 2							,__a = 32							,__a = 32							,__a = "gelu"							,__a = 0.1							,__a = 0.1							,__a = 0.1							,__a = 0.1							,__a = 0.1							,__a = 100							,__a = 0.02							,__a = True							,__a=True							,__a = 10							,__a = 25							,__a = 3							,**__a							,)      ->				Union[str, Any]:
          # time series specific configuration
          snake_case			:      List[str]        =			prediction_length
          snake_case			:      str        =			context_length if context_length is not None else prediction_length
          snake_case			:      List[Any]        =			distribution_output
          snake_case			:      Any        =			loss
          snake_case			:      Union[str, Any]        =			input_size
          snake_case			:      Optional[int]        =			num_time_features
          snake_case			:      Union[str, Any]        =			lags_sequence
          snake_case			:      Any        =			scaling
          snake_case			:      str        =			num_dynamic_real_features
          snake_case			:      Union[str, Any]        =			num_static_real_features
          snake_case			:      Optional[int]        =			num_static_categorical_features
          if cardinality is not None 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			:      int        =			cardinality
          else:
               snake_case			:      Optional[Any]        =			[0]
          if embedding_dimension is not None 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			:      int        =			embedding_dimension
          else:
               snake_case			:      List[str]        =			[min(50							,(cat + 1) // 2							) for cat in self.cardinality]
          snake_case			:      Optional[int]        =			num_parallel_samples

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

          snake_case			:      str        =			dropout
          snake_case			:      Union[str, Any]        =			attention_dropout
          snake_case			:      Optional[Any]        =			activation_dropout
          snake_case			:      Tuple        =			encoder_layerdrop
          snake_case			:      Any        =			decoder_layerdrop

          snake_case			:      Any        =			activation_function
          snake_case			:      int        =			init_std

          snake_case			:      Optional[Any]        =			use_cache

          # Autoformer
          snake_case			:      int        =			label_length
          snake_case			:      str        =			moving_average
          snake_case			:      Tuple        =			autocorrelation_factor

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







     @property
     def 			snake_case_      (				self							)      ->				int:
          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
          )



