


'''simple docstring'''
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


def       _lowercase							(					UpperCamelCase__							:       Any      ):
 return 1.0 / (1.0 + np.exp(-_outputs      ))


def       _lowercase							(					UpperCamelCase__							:       Dict      ):
 __A	:		int	  =      np.max(_outputs,					axis=-1,					keepdims=UpperCamelCase__      )
 __A	:		int	  =      np.exp(_outputs - maxes      )
 return shifted_exp / shifted_exp.sum(axis=-1,					keepdims=UpperCamelCase__      )

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		str     				=    '''sigmoid'''
 __lowercase					:		int     				=    '''softmax'''
 __lowercase					:		Tuple     				=    '''none'''


@add_end_docstrings(
    snake_case_     ,		r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''     ,		)
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    False
 __lowercase					:		Dict     				=    ClassificationFunction.NONE


 def __init__(   self   ,   **__lowercase    ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  self.check_model_type(
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      if self.framework == 'tf'
      else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING    )


 def 			snake_case__ (   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=""   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      tokenizer_kwargs

  __A	:		Any	  =      {}
  if hasattr(self.model.config   ,   'return_all_scores'    ) and return_all_scores is None:
   __A	:		int	  =      self.model.config.return_all_scores

  if isinstance(__lowercase   ,   __lowercase    ) or top_k is None:
   __A	:		Tuple	  =      top_k
   __A	:		Any	  =      False
  elif return_all_scores is not None:
   warnings.warn(
       '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
       ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'   ,   __lowercase   ,   )
   if return_all_scores:
    __A	:		Dict	  =      None
   else:
    __A	:		Optional[int]	  =      1

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		Any	  =      ClassificationFunction[function_to_apply.upper()]

  if function_to_apply is not None:
   __A	:		List[Any]	  =      function_to_apply
  return preprocess_params, {}, postprocess_params


 def __call__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		str	  =      super().__call__(*__lowercase   ,   **__lowercase    )
  # TODO try and retrieve it in a nicer way from _sanitize_parameters.
  __A	:		str	  =      'top_k' not in kwargs
  if isinstance(args[0]   ,   __lowercase    ) and _legacy:
   # This pipeline is odd, and return a list when single item is run
   return [result]
  else:
   return result


 def 			snake_case__ (   self   ,   __lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		str	  =      self.framework
  if isinstance(__lowercase   ,   __lowercase    ):
   return self.tokenizer(**__lowercase   ,   return_tensors=__lowercase   ,   **__lowercase    )
  elif isinstance(__lowercase   ,   __lowercase    ) and len(__lowercase    ) == 1 and isinstance(inputs[0]   ,   __lowercase    ) and len(inputs[0]    ) == 2:
   # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
   return self.tokenizer(
       text=inputs[0][0]   ,   text_pair=inputs[0][1]   ,   return_tensors=__lowercase   ,   **__lowercase    )
  elif isinstance(__lowercase   ,   __lowercase    ):
   # This is likely an invalid usage of the pipeline attempting to pass text pairs.
   raise ValueError(
       'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
       ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'    )
  return self.tokenizer(__lowercase   ,   return_tensors=__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.model(**__lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=True    ):

  """simple docstring"""







  if function_to_apply is None:
   if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
    __A	:		List[Any]	  =      ClassificationFunction.SIGMOID
   elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
    __A	:		Dict	  =      ClassificationFunction.SOFTMAX
   elif hasattr(self.model.config   ,   'function_to_apply'    ) and function_to_apply is None:
    __A	:		List[str]	  =      self.model.config.function_to_apply
   else:
    __A	:		Optional[int]	  =      ClassificationFunction.NONE

  __A	:		Tuple	  =      model_outputs['logits'][0]
  __A	:		str	  =      outputs.numpy()

  if function_to_apply == ClassificationFunction.SIGMOID:
   __A	:		str	  =      sigmoid(__lowercase    )
  elif function_to_apply == ClassificationFunction.SOFTMAX:
   __A	:		Any	  =      softmax(__lowercase    )
  elif function_to_apply == ClassificationFunction.NONE:
   __A	:		Any	  =      outputs
  else:
   raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""    )

  if top_k == 1 and _legacy:
   return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

  __A	:		Optional[int]	  =      [
      {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(__lowercase    )
  ]
  if not _legacy:
   dict_scores.sort(key=lambda __lowercase    : x["score"]   ,   reverse=__lowercase    )
   if top_k is not None:
    __A	:		Dict	  =      dict_scores[:top_k]
  return dict_scores










'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






