'''simple docstring'''
from argparse import ArgumentParser

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


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







def  _UpperCamelCase (     _a	:	str			):




    """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  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  pipeline(
        task=args.task				,     model=args.model if args.model else None				,     config=args.config				,     tokenizer=args.tokenizer				,     device=args.device				,     )
    __UpperCamelCase	:	int		    =  try_infer_format_from_ext(args.input			) if args.format == 'infer' else args.format
    __UpperCamelCase	:	Dict		    =  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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  nlp
         __UpperCamelCase	:	int		    =  reader







     @staticmethod
     def lowerCAmelCase     (		_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  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 lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	Tuple		    =  self._nlp, []

         for entry in self._reader:
             __UpperCamelCase	:	Dict		    =  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	:	Optional[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    )




'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



