






'''simple docstring'''

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


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}




class     A    (      _A						):






 """simple docstring"""





 __a			:						Union[str, Any]      =						'''open-llama'''
 def __init__(							self			,     __lowerCAmelCase=10_00_00			,     __lowerCAmelCase=40_96			,     __lowerCAmelCase=1_10_08			,     __lowerCAmelCase=32			,     __lowerCAmelCase=32			,     __lowerCAmelCase="silu"			,     __lowerCAmelCase=20_48			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1E-6			,     __lowerCAmelCase=True			,     __lowerCAmelCase=0			,     __lowerCAmelCase=1			,     __lowerCAmelCase=2			,     __lowerCAmelCase=False			,     __lowerCAmelCase=True			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=True			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   str					    =							vocab_size
       UpperCamelCase_				:   Union[str, Any]					    =							max_position_embeddings
       UpperCamelCase_				:   Union[str, Any]					    =							hidden_size
       UpperCamelCase_				:   str					    =							intermediate_size
       UpperCamelCase_				:   List[Any]					    =							num_hidden_layers
       UpperCamelCase_				:   Tuple					    =							num_attention_heads
       UpperCamelCase_				:   List[str]					    =							hidden_act
       UpperCamelCase_				:   str					    =							initializer_range
       UpperCamelCase_				:   Optional[Any]					    =							rms_norm_eps
       UpperCamelCase_				:   Union[str, Any]					    =							use_cache
       UpperCamelCase_				:   Optional[Any]					    =							kwargs.pop(
           """use_memorry_efficient_attention"""			,     __lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							hidden_dropout_prob
       UpperCamelCase_				:   str					    =							attention_dropout_prob
       UpperCamelCase_				:   List[Any]					    =							use_stable_embedding
       UpperCamelCase_				:   Optional[int]					    =							shared_input_output_embedding
       UpperCamelCase_				:   Tuple					    =							rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=__lowerCAmelCase			,     bos_token_id=__lowerCAmelCase			,     eos_token_id=__lowerCAmelCase			,     tie_word_embeddings=__lowerCAmelCase			,     **__lowerCAmelCase			,     )





 def   _UpperCAmelCase       (							self ):
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling			,     __lowerCAmelCase ) or len(self.rope_scaling ) != 2:
             raise ValueError(
                 """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
                 F"got {self.rope_scaling}" )
       UpperCamelCase_				:   int					    =							self.rope_scaling.get("""type"""			,     __lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							self.rope_scaling.get("""factor"""			,     __lowerCAmelCase )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}" )
       if rope_scaling_factor is None or not isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}" )








'''simple docstring'''

from argparse import ArgumentParser

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


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



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  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 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							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=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     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=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

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






