


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 				UpperCAmelCase_					(							__snake_case						)     ->			Tuple:







 """simple docstring"""


 return 1.0 / (1.0 + np.exp(-_outputs						))
def 				UpperCAmelCase_					(							__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =np.max(_outputs     ,						axis=-1     ,						keepdims=__snake_case						)
 _lowercase							      =np.exp(_outputs - maxes						)
 return shifted_exp / shifted_exp.sum(axis=-1     ,						keepdims=__snake_case						)







class       lowerCamelCase__   (       _snake_case):
     SCREAMING_SNAKE_CASE__      			=			'''sigmoid'''
     SCREAMING_SNAKE_CASE__      			=			'''softmax'''
     SCREAMING_SNAKE_CASE__      			=			'''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):
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			ClassificationFunction.NONE





     def __init__(self		,     **UpperCAmelCase       )     ->   Tuple:
      super().__init__(**_UpperCamelCase       )

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





     def      __A					(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=""		,     **UpperCAmelCase       )     ->   Dict:
      # Using "" as default argument because we're going to use `top_k=None` in user code to declare
      # "No top_k"
      _lowercase							      =tokenizer_kwargs

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

      if isinstance(_UpperCamelCase		,     _UpperCamelCase       ) or top_k is None:
       _lowercase							      =top_k
       _lowercase							      =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`.'''		,     _UpperCamelCase		,     )
       if return_all_scores:
        _lowercase							      =None
       else:
        _lowercase							      =1

      if isinstance(_UpperCamelCase		,     _UpperCamelCase       ):
       _lowercase							      =ClassificationFunction[function_to_apply.upper()]

      if function_to_apply is not None:
       _lowercase							      =function_to_apply
      return preprocess_params, {}, postprocess_params





     def __call__(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   List[Any]:
      _lowercase							      =super().__call__(*_UpperCamelCase		,     **_UpperCamelCase       )
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      _lowercase							      ='top_k' not in kwargs
      if isinstance(args[0]		,     _UpperCamelCase       ) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
      else:
       return result





     def      __A					(self		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict[str, GenericTensor]:
      _lowercase							      =self.framework
      if isinstance(_UpperCamelCase		,     _UpperCamelCase       ):
       return self.tokenizer(**_UpperCamelCase		,     return_tensors=_UpperCamelCase		,     **_UpperCamelCase       )
      elif isinstance(_UpperCamelCase		,     _UpperCamelCase       ) and len(_UpperCamelCase       ) == 1 and isinstance(inputs[0]		,     _UpperCamelCase       ) 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=_UpperCamelCase		,     **_UpperCamelCase       )
      elif isinstance(_UpperCamelCase		,     _UpperCamelCase       ):
       # 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(_UpperCamelCase		,     return_tensors=_UpperCamelCase		,     **_UpperCamelCase       )





     def      __A					(self		,     UpperCAmelCase       )     ->   Tuple:
      return self.model(**_UpperCamelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=1		,     UpperCAmelCase=True       )     ->   str:
      # `_legacy` is used to determine if we're running the naked pipeline and in backward
      # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
      # the more natural result containing the list.
      # Default value before `set_parameters`
      if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
        _lowercase							      =ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
        _lowercase							      =ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config		,     '''function_to_apply'''       ) and function_to_apply is None:
        _lowercase							      =self.model.config.function_to_apply
       else:
        _lowercase							      =ClassificationFunction.NONE

      _lowercase							      =model_outputs['logits'][0]
      _lowercase							      =outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
       _lowercase							      =sigmoid(_UpperCamelCase       )
      elif function_to_apply == ClassificationFunction.SOFTMAX:
       _lowercase							      =softmax(_UpperCamelCase       )
      elif function_to_apply == ClassificationFunction.NONE:
       _lowercase							      =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()}

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









from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

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

__UpperCAmelCase								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )
							self.check_model_type(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						{}, {}
							if padding is not None:
												UpperCAmelCase_    :      List[str]            =						padding
							if truncation is not None:
												UpperCAmelCase_    :      Tuple            =						truncation
							if top_k is not None:
												UpperCAmelCase_    :      Dict            =						top_k
							return preprocess_params, {}, postprocess_params



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							if isinstance(_UpperCamelCase				,		(Image.Image, str)   ) and isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'question': question}
							else:
												UpperCAmelCase_    :      List[str]            =						image
							UpperCAmelCase_    :      Optional[Any]            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=False   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      Dict            =						self.tokenizer(
							    inputs['question']				,		return_tensors=self.framework				,		padding=_UpperCamelCase				,		truncation=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.image_processor(images=_UpperCamelCase				,		return_tensors=self.framework   )
							model_inputs.update(_UpperCamelCase   )
							return model_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      Any            =						self.model(**_UpperCamelCase   )
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=5   )	->							str:
							if top_k > self.model.config.num_labels:
												UpperCAmelCase_    :      Union[str, Any]            =						self.model.config.num_labels

							if self.framework == "pt":
												UpperCAmelCase_    :      List[str]            =						model_outputs.logits.sigmoid()[0]
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						probs.topk(_UpperCamelCase   )
							else:
												raise ValueError(f"Unsupported framework: {self.framework}"   )

							UpperCAmelCase_    :      Optional[Any]            =						scores.tolist()
							UpperCAmelCase_    :      Tuple            =						ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase				,		_UpperCamelCase   )]






