





"""simple docstring"""
from argparse import ArgumentParser

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


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



def      __lowerCamelCase   (							a_     :		str							)			->      Tuple:
      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      __lowerCamelCase   (							a_     :		List[str]							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Any						=  pipeline(
          task=args.task ,   model=args.model if args.model else None ,   config=args.config ,   tokenizer=args.tokenizer ,   device=args.device ,   )
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  try_infer_format_from_ext(args.input							) if args.format == '''infer''' else args.format
      __SCREAMING_SNAKE_CASE   :Any						=  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 					_SCREAMING_SNAKE_CASE(   A  ):




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  nlp
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  reader




       @staticmethod
       def     _UpperCamelCase     (						SCREAMING_SNAKE_CASE__							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :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=SCREAMING_SNAKE_CASE__		,help='''Path to the file to use for inference'''							)
             run_parser.add_argument('''--output'''		,type=SCREAMING_SNAKE_CASE__		,help='''Path to the file that will be used post to write results.'''							)
             run_parser.add_argument('''--model'''		,type=SCREAMING_SNAKE_CASE__		,help='''Name or path to the model to instantiate.'''							)
             run_parser.add_argument('''--config'''		,type=SCREAMING_SNAKE_CASE__		,help='''Name or path to the model\'s config to instantiate.'''							)
             run_parser.add_argument(
                 '''--tokenizer'''		,type=SCREAMING_SNAKE_CASE__		,help='''Name of the tokenizer to use. (default: same as the model name)'''							)
             run_parser.add_argument(
                 '''--column'''		,type=SCREAMING_SNAKE_CASE__		,help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''		,)
             run_parser.add_argument(
                 '''--format'''		,type=SCREAMING_SNAKE_CASE__		,default='''infer'''		,choices=PipelineDataFormat.SUPPORTED_FORMATS		,help='''Input format to read from'''		,)
             run_parser.add_argument(
                 '''--device'''		,type=SCREAMING_SNAKE_CASE__		,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=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[Any]						=  self._nlp, []

             for entry in self._reader:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  nlp(**SCREAMING_SNAKE_CASE__							) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE__							)
                   if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                         outputs.append(SCREAMING_SNAKE_CASE__							)
                   else:
                         outputs += output

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






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  '''llama'''
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  ['''past_key_values''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_20_00		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=1_10_08		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="silu"		,SCREAMING_SNAKE_CASE__=20_48		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-6		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :int						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :List[str]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_attention_heads

             # for backward compatibility
             if num_key_value_heads is None:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  num_attention_heads

             __SCREAMING_SNAKE_CASE   :str						=  num_key_value_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :List[str]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  rms_norm_eps
             __SCREAMING_SNAKE_CASE   :Dict						=  pretraining_tp
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  use_cache
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  rope_scaling
             self._rope_scaling_validation()

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




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





             """simple docstring"""






             if self.rope_scaling is None:
                   return

             if not isinstance(self.rope_scaling		,SCREAMING_SNAKE_CASE__							) 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}'''							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.rope_scaling.get('''type'''		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.rope_scaling.get('''factor'''		,SCREAMING_SNAKE_CASE__							)
             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(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) or rope_scaling_factor <= 1.0:
                   raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)