from argparse import ArgumentParser

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


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







def  __UpperCamelCase    (						A							):
     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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      pipeline(
         task=args.task ,							model=args.model if args.model else None ,							config=args.config ,							tokenizer=args.tokenizer ,							device=args.device ,							)
     UpperCamelCase__										=      try_infer_format_from_ext(args.input							) if args.format == '''infer''' else args.format
     UpperCamelCase__										=      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   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      nlp
       UpperCamelCase__										=      reader






  @staticmethod
  def 		_a					(SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      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 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self._nlp, []

       for entry in self._reader:
            UpperCamelCase__										=      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:
            UpperCamelCase__										=      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_	)




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



