


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    (      SCREAMING_SNAKE_CASE__      : str    ):
     if not path:
          return "pipe"

     for ext in PipelineDataFormat.SUPPORTED_FORMATS:
          if path.endswith(SCREAMING_SNAKE_CASE__    ):
               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    (      SCREAMING_SNAKE_CASE__      : str    ):
     __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=SCREAMING_SNAKE_CASE__      ,					output_path=args.output      ,					input_path=args.input      ,					column=args.column if args.column else nlp.default_input_names      ,					overwrite=args.overwrite      ,					)
     return RunCommand(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_   )  ->	List[Any]:
         __UpperCamelCase   							=nlp
         __UpperCamelCase   							=reader






    @staticmethod
    def _a (      A_   )  ->	Tuple:
         __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=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 _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase							,				__UpperCamelCase   							=self._nlp, []

         for entry in self._reader:
              __UpperCamelCase   							=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:
              __UpperCamelCase   							=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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





