'''simple docstring'''





from argparse import ArgumentParser

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


lowerCAmelCase			:							Optional[int]     =logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						A__  (					__A	):







    '''simple docstring'''


    if not path:
        return "pipe"

    for ext in PipelineDataFormat.SUPPORTED_FORMATS:
        if path.endswith(_UpperCamelCase	):
            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 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						pipeline(
        task=args.task   , model=args.model if args.model else None   , config=args.config   , tokenizer=args.tokenizer   , device=args.device   , )
    _lowerCamelCase     :		int 						=						try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
    _lowerCamelCase     :		List[str] 						=						PipelineDataFormat.from_str(
        format=_UpperCamelCase   , output_path=args.output   , input_path=args.input   , column=args.column if args.column else nlp.default_input_names   , overwrite=args.overwrite   , )
    return RunCommand(_UpperCamelCase   , _UpperCamelCase	)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Any    ,  _UpperCamelCase     :				Pipeline    ,  _UpperCamelCase     :				PipelineDataFormat)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						nlp
          _lowerCamelCase     :		int 						=						reader



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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						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=UpperCAmelCase__    ,  help="""Path to the file to use for inference""")
          run_parser.add_argument("""--output"""    ,  type=UpperCAmelCase__    ,  help="""Path to the file that will be used post to write results.""")
          run_parser.add_argument("""--model"""    ,  type=UpperCAmelCase__    ,  help="""Name or path to the model to instantiate.""")
          run_parser.add_argument("""--config"""    ,  type=UpperCAmelCase__    ,  help="""Name or path to the model\'s config to instantiate.""")
          run_parser.add_argument(
              """--tokenizer"""    ,  type=UpperCAmelCase__    ,  help="""Name of the tokenizer to use. (default: same as the model name)""")
          run_parser.add_argument(
              """--column"""    ,  type=UpperCAmelCase__    ,  help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""    ,  )
          run_parser.add_argument(
              """--format"""    ,  type=UpperCAmelCase__    ,  default="""infer"""    ,  choices=PipelineDataFormat.SUPPORTED_FORMATS    ,  help="""Input format to read from"""    ,  )
          run_parser.add_argument(
              """--device"""    ,  type=UpperCAmelCase__    ,  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=UpperCAmelCase__)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						self._nlp, []

          for entry in self._reader:
              _lowerCamelCase     :		int 						=						nlp(**UpperCAmelCase__) if self._reader.is_multi_columns else nlp(UpperCAmelCase__)
              if isinstance(UpperCAmelCase__    ,  UpperCAmelCase__):
                  outputs.append(UpperCAmelCase__)
              else:
                  outputs += output

        # Saving data
          if self._nlp.binary_output:
              _lowerCamelCase     :		Optional[int] 						=						self._reader.save_binary(UpperCAmelCase__)
              logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}""")
          else:
              self._reader.save(UpperCAmelCase__)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				str    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          requires_backends(self    ,  """vision""")
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == """tf"""
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[str]=None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{}
          if top_k is not None:
              _lowerCamelCase     :		str 						=						top_k
          return {}, {}, postprocess_params



      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,  **_UpperCamelCase     :				Optional[int])				->Dict:


          """simple docstring"""


          return super().__call__(_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						load_image(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						self.image_processor(images=_UpperCamelCase    ,  return_tensors=self.framework)
          return model_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model(**_UpperCamelCase)
          return model_outputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]=5)				->str:


          """simple docstring"""


          if top_k > self.model.config.num_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						self.model.config.num_labels

          if self.framework == "pt":
              _lowerCamelCase     :		Optional[Any] 						=						model_outputs.logits.softmax(-1)[0]
              _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						probs.topk(_UpperCamelCase)
          elif self.framework == "tf":
              _lowerCamelCase     :		List[Any] 						=						stable_softmax(model_outputs.logits    ,  axis=-1)[0]
              _lowerCamelCase     :		List[Any] 						=						tf.math.top_k(_UpperCamelCase    ,  k=_UpperCamelCase)
              _lowerCamelCase						,							_lowerCamelCase     :		str 						=						topk.values.numpy(), topk.indices.numpy()
          else:
              raise ValueError(F"""Unsupported framework: {self.framework}""")

          _lowerCamelCase     :		str 						=						scores.tolist()
          _lowerCamelCase     :		str 						=						ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase    ,  _UpperCamelCase)]




