






'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
      from PIL import Image
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''dandelin/vilt-b32-finetuned-vqa'''
      UpperCamelCase_             =      (
          '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
          '''image containing the information, as well as a `question` which should be the question in English. It '''
          '''returns a text that is the answer to the question.'''
      )
      UpperCamelCase_             =      '''image_qa'''
      UpperCamelCase_             =      AutoProcessor
      UpperCamelCase_             =      AutoModelForVisualQuestionAnswering

      UpperCamelCase_             =      ['''image''', '''text''']
      UpperCamelCase_             =      ['''text''']






      def __init__(			self      :		List[Any]  ,   *UpperCAmelCase      :		List[str]  ,   **UpperCAmelCase      :		Dict		)	->	Dict:


       '''simple docstring'''

       requires_backends(self  ,   ['''vision''']		)
       super().__init__(*UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		"Image"  ,   UpperCAmelCase      :		str		)	->	Any:


       '''simple docstring'''

       return self.pre_processor(UpperCAmelCase  ,   UpperCAmelCase  ,   return_tensors='''pt'''		)






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		Optional[int]		)	->	Union[str, Any]:


       '''simple docstring'''

       with torch.no_grad():
        return self.model(**UpperCAmelCase		).logits






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       lowercase       : List[str]						=outputs.argmax(-1		).item()
       return self.model.config.idalabel[idx]









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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       return 1.0 / (1.0 + np.exp(-_outputs		))







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''sigmoid'''
    UpperCAmelCase__				    = '''softmax'''
    UpperCAmelCase__				    = '''none'''


@add_end_docstrings(
    UpperCAmelCase__						,   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 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = ClassificationFunction.NONE


    def __init__(     self :      Any   ,					**UpperCAmelCase__ :      Optional[Any])		->str:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple=None   ,					UpperCAmelCase__ :      Any=None   ,					UpperCAmelCase__ :      int=""   ,					**UpperCAmelCase__ :      Any)		->int:





           '''simple docstring'''

           A__ 					=       tokenizer_kwargs

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

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__) or top_k is None:
                  A__ 					=       top_k
                  A__ 					=       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:
                         A__ 					=       None
                  else:
                         A__ 					=       1

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  A__ 					=       ClassificationFunction[function_to_apply.upper()]

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


    def __call__(     self :      str   ,					*UpperCAmelCase__ :      List[Any]   ,					**UpperCAmelCase__ :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       super().__call__(*UpperCAmelCase__   ,					**UpperCAmelCase__)
           # TODO try and retrieve it in a nicer way from _sanitize_parameters.
           A__ 					=       '''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 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Any   ,					**UpperCAmelCase__ :      str)		->Dict[str, GenericTensor]:





           '''simple docstring'''

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple)		->Tuple:





           '''simple docstring'''

           return self.model(**UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Optional[int]=None   ,					UpperCAmelCase__ :      List[Any]=1   ,					UpperCAmelCase__ :      str=True)		->Dict:





           '''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__ 					=       ClassificationFunction.SIGMOID
                  elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                         A__ 					=       ClassificationFunction.SOFTMAX
                  elif hasattr(self.model.config   ,					'''function_to_apply''') and function_to_apply is None:
                         A__ 					=       self.model.config.function_to_apply
                  else:
                         A__ 					=       ClassificationFunction.NONE

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

           if function_to_apply == ClassificationFunction.SIGMOID:
                  A__ 					=       sigmoid(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.SOFTMAX:
                  A__ 					=       softmax(UpperCAmelCase__)
           elif function_to_apply == ClassificationFunction.NONE:
                  A__ 					=       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__ 					=       [
               {'''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:
                         A__ 					=       dict_scores[:top_k]
           return dict_scores


