




from argparse import ArgumentParser

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


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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    pipeline(
           task=args.task ,				model=args.model if args.model else None ,				config=args.config ,				tokenizer=args.tokenizer ,				device=args.device ,				)
       A__       =    try_infer_format_from_ext(args.input						) if args.format == '''infer''' else args.format
       A__       =    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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	List[Any]					, UpperCAmelCase__      :	Pipeline					, UpperCAmelCase__      :	PipelineDataFormat)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    nlp
           A__       =    reader


    @staticmethod
    def        SCREAMING_SNAKE_CASE							(			UpperCAmelCase__      :	ArgumentParser)						->Any:





           '''simple docstring'''

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





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





           '''simple docstring'''

           A__ ,							A__       =    self._nlp, []

           for entry in self._reader:
                  A__       =    nlp(**UpperCAmelCase__) if self._reader.is_multi_columns else nlp(UpperCAmelCase__)
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         outputs.append(UpperCAmelCase__)
                  else:
                         outputs += output

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








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


