






"""simple docstring"""





from argparse import ArgumentParser

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


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







def      lowerCamelCase__       (							_lowerCamelCase       :		Any )      ->				Union[str, Any]:
  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      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				Optional[Any]:
  lowerCamelCase_         =						pipeline(
      task=args.task		,				model=args.model if args.model else None		,				config=args.config		,				tokenizer=args.tokenizer		,				device=args.device		,				)
  lowerCamelCase_         =						try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
  lowerCamelCase_         =						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      a						(					__snake_case		):






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[str]:
      lowerCamelCase_         =						nlp
      lowerCamelCase_         =						reader






    @staticmethod
    def    UpperCamelCase       (       __SCREAMING_SNAKE_CASE     :							Optional[int]					)     ->		str:
      lowerCamelCase_         =						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    UpperCamelCase       (       self     :							Tuple					)     ->		str:
      lowerCamelCase_         =						self._nlp, []

      for entry in self._reader:
        lowerCamelCase_         =						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:
        lowerCamelCase_         =						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					)










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """ViTMSNModel""",
			    """ViTMSNForImageClassification""",
			    """ViTMSNPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_msn import (
						    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMSNForImageClassification,
						    ViTMSNModel,
						    ViTMSNPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)