






from argparse import ArgumentParser

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


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



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Dict:
								snake_case_           =				nlp
								snake_case_           =				reader




	@staticmethod
	def 				_UpperCamelCase     (			a   )			->       Dict:
								snake_case_           =				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 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_     ,						snake_case_           =				self._nlp, []

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













from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





