


from argparse import ArgumentParser

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


lowerCAmelCase__  :List[Any]             =							logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	lowerCAmelCase__			(      a__:		str	)  ->		int:


							'''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 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		Tuple:


							'''simple docstring'''





							_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				(    __snake_case      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      nlp
									_UpperCAmelCase					=      reader
		@staticmethod
		def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""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=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      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      self._nlp, []

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
