





"""simple docstring"""





from typing import Any, Dict, List, Union

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


if is_vision_available():
  from ..image_utils import load_image


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__A											=						logging.get_logger(__name__)


__A											=						Dict[str, Any]
__A											=						List[Prediction]






@add_end_docstrings(a			)
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         super().__init__(*__UpperCAmelCase       ,					**__UpperCAmelCase )

         if self.framework == "tf":
           raise ValueError(F"The {self.__class__} is only available in PyTorch." )

         requires_backends(self       ,					'vision' )
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				{}
         if "threshold" in kwargs:
           lowerCAmelCase__     :Optional[int]											=				kwargs['threshold']
         return {}, {}, postprocess_kwargs






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



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				load_image(__UpperCAmelCase )
         lowerCAmelCase__     :int											=				torch.IntTensor([[image.height, image.width]] )
         lowerCAmelCase__     :Dict											=				self.image_processor(images=[image]       ,					return_tensors='pt' )
         if self.tokenizer is not None:
           lowerCAmelCase__     :Dict											=				self.tokenizer(text=inputs['words']       ,					boxes=inputs['boxes']       ,					return_tensors='pt' )
         lowerCAmelCase__     :Dict											=				target_size
         return inputs






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				model_inputs.pop('target_size' )
         lowerCAmelCase__     :int											=				self.model(**__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				outputs.__class__({'target_size': target_size, **outputs} )
         if self.tokenizer is not None:
           lowerCAmelCase__     :Any											=				model_inputs['bbox']
         return model_outputs






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0.9 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				model_outputs['target_size']
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				target_size[0].tolist()

           def unnormalize(__UpperCAmelCase ):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1_0_0_0),
                         (height * bbox[1] / 1_0_0_0),
                         (width * bbox[2] / 1_0_0_0),
                         (height * bbox[3] / 1_0_0_0),
                     ] ) )

           lowerCAmelCase__				,							lowerCAmelCase__     :str											=				model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
           lowerCAmelCase__     :int											=				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           lowerCAmelCase__     :List[str]											=				[unnormalize(__UpperCAmelCase ) for bbox in model_outputs['bbox'].squeeze(0 )]
           lowerCAmelCase__     :Union[str, Any]											=				['score', 'label', 'box']
           lowerCAmelCase__     :Optional[int]											=				[dict(zip(__UpperCAmelCase       ,					__UpperCAmelCase ) ) for vals in zip(scores.tolist()       ,					__UpperCAmelCase       ,					__UpperCAmelCase ) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           lowerCAmelCase__     :Dict											=				self.image_processor.post_process_object_detection(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
           lowerCAmelCase__     :str											=				raw_annotations[0]
           lowerCAmelCase__     :Tuple											=				raw_annotation['scores']
           lowerCAmelCase__     :Optional[int]											=				raw_annotation['labels']
           lowerCAmelCase__     :str											=				raw_annotation['boxes']

           lowerCAmelCase__     :List[Any]											=				scores.tolist()
           lowerCAmelCase__     :Tuple											=				[self.model.config.idalabel[label.item()] for label in labels]
           lowerCAmelCase__     :List[str]											=				[self._get_bounding_box(__UpperCAmelCase ) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           lowerCAmelCase__     :Optional[Any]											=				['score', 'label', 'box']
           lowerCAmelCase__     :Union[str, Any]											=				[
               dict(zip(__UpperCAmelCase       ,					__UpperCAmelCase ) )
               for vals in zip(raw_annotation['scores']       ,					raw_annotation['labels']       ,					raw_annotation['boxes'] )
           ]

         return annotation






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         if self.framework != "pt":
           raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				box.int().tolist()
         lowerCAmelCase__     :str											=				{
             'xmin': xmin,
             'ymin': ymin,
             'xmax': xmax,
             'ymax': ymax,
         }
         return bbox





import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:


							class   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							Any					,       **_snake_case       :							Optional[int]       ):






															"""simple docstring"""


															pass


@is_pipeline_test
@require_torch
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     [
								    {
								        'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
								        'question': 'How many cats are there?',
								    },
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'question': 'How many cats are there?',
								    },
								]
								return vqa_pipeline, examples



					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     vqa_pipeline(_snake_case					,       top_k=1       )
								self.assertEqual(
								    _snake_case					,       [
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								    ]					,       )



					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question='How many cats are there?'					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )



					@slow
					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='dandelin/vilt-b32-finetuned-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question=_snake_case					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline(
								    [{'image': image, 'question': question}, {'image': image, 'question': question}]					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2					,       )



					@require_tf
					@unittest.skip('Visual question answering not implemented in TF'       )
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								pass




