




'''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__				(      __lowercase				):
     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__				(      __lowercase				):
     snake_case			:      Optional[int]        =			pipeline(
         task=args.task     ,   model=args.model if args.model else None     ,   config=args.config     ,   tokenizer=args.tokenizer     ,   device=args.device     ,   )
     snake_case			:      Optional[Any]        =			try_infer_format_from_ext(args.input				) if args.format == """infer""" else args.format
     snake_case			:      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__	):





     '''simple docstring'''



     def __init__(				self							,__a							,__a							)      ->				Tuple:
          snake_case			:      Any        =			nlp
          snake_case			:      Any        =			reader







     @staticmethod
     def 			snake_case_      (				__a							)      ->				List[Any]:
          snake_case			:      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 			snake_case_      (				self							)      ->				List[Any]:
          snake_case ,		snake_case			:      List[Any]        =			self._nlp, []

          for entry in self._reader:
               snake_case			:      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:
               snake_case			:      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'''







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*__a							,**__a							)
               return

          # Otherwise, first make sure the optimizer was stepped.
          if not self.gradient_state.sync_gradients:
               if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
               return

          for opt in self.optimizers:
               if opt.step_was_skipped:
                    return
          if self.split_batches:
               # Split batches -> the training dataloader batch size is not changed so one step per training step
               self.scheduler.step(*__a							,**__a							)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               snake_case			:      List[str]        =			AcceleratorState().num_processes
               for _ in range(__a							):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler							,"""total_steps"""							):
                         if self.scheduler._step_count <= self.scheduler.total_steps:
                              self.scheduler.step(*__a							,**__a							)
                    else:
                         self.scheduler.step(*__a							,**__a							)







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







     def 			snake_case_      (				self							)      ->				int:
          return self.scheduler.state_dict()







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







     def 			snake_case_      (				self							)      ->				Any:
          return self.scheduler.get_lr()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          return self.scheduler.print_lr(*__a							,**__a							)



