
from argparse import ArgumentParser

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


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


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__lowerCamelCase    ):
                     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					(    __lowerCamelCase     : Tuple    ):
       snake_case       :   Any							=      pipeline(
           task=args.task				,					model=args.model if args.model else None				,					config=args.config				,					tokenizer=args.tokenizer				,					device=args.device				,					)
       snake_case       :   int							=      try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       snake_case       :   Optional[int]							=      PipelineDataFormat.from_str(
           format=__lowerCamelCase				,					output_path=args.output				,					input_path=args.input				,					column=args.column if args.column else nlp.default_input_names				,					overwrite=args.overwrite				,					)
       return RunCommand(__lowerCamelCase				,					__lowerCamelCase    )






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					str    ,						snake_case__		:					Pipeline    ,						snake_case__		:					PipelineDataFormat )		->	Dict:


          '''simple docstring'''


          snake_case       :   int							=      nlp
          snake_case       :   Union[str, Any]							=      reader



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					ArgumentParser )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      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=snake_case__    ,						help="Path to the file to use for inference" )
          run_parser.add_argument("--output"    ,						type=snake_case__    ,						help="Path to the file that will be used post to write results." )
          run_parser.add_argument("--model"    ,						type=snake_case__    ,						help="Name or path to the model to instantiate." )
          run_parser.add_argument("--config"    ,						type=snake_case__    ,						help="Name or path to the model's config to instantiate." )
          run_parser.add_argument(
              "--tokenizer"    ,						type=snake_case__    ,						help="Name of the tokenizer to use. (default: same as the model name)" )
          run_parser.add_argument(
              "--column"    ,						type=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=snake_case__    ,						default="infer"    ,						choices=PipelineDataFormat.SUPPORTED_FORMATS    ,						help="Input format to read from"    ,						)
          run_parser.add_argument(
              "--device"    ,						type=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=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self._nlp, []

          for entry in self._reader:
                 snake_case       :   Optional[Any]							=      nlp(**snake_case__ ) if self._reader.is_multi_columns else nlp(snake_case__ )
                 if isinstance(snake_case__    ,						snake_case__ ):
                        outputs.append(snake_case__ )
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 snake_case       :   str							=      self._reader.save_binary(snake_case__ )
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
          else:
                 self._reader.save(snake_case__ )













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GroupViTModel",
         "GroupViTPreTrainedModel",
         "GroupViTTextModel",
         "GroupViTVisionModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFGroupViTModel",
         "TFGroupViTPreTrainedModel",
         "TFGroupViTTextModel",
         "TFGroupViTVisionModel",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

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


