




'''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_      :		Union[str, Any]							      =       logging.get_logger(__name__)


a_      :		Union[str, Any]							      =       Dict[str, Any]
a_      :		Optional[Any]							      =       List[Prediction]


@add_end_docstrings(lowerCamelCase__				)
class   __UpperCamelCase       ( lowerCamelCase__				):






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



              """simple docstring"""







              super().__init__(*lowerCAmelCase,		**lowerCAmelCase  )

              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      lowercase__     (							self,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							={}
              if "threshold" in kwargs:
                     lowerCamelCase_							=kwargs['''threshold''']
              return {}, {}, postprocess_kwargs






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



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=model_inputs.pop('''target_size'''  )
              lowerCamelCase_							=self.model(**lowerCAmelCase  )
              lowerCamelCase_							=outputs.__class__({'''target_size''': target_size, **outputs}  )
              if self.tokenizer is not None:
                     lowerCamelCase_							=model_inputs['''bbox''']
              return model_outputs






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=0.9  ):



              """simple docstring"""







              lowerCamelCase_							=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_							=target_size[0].tolist()

                     def unnormalize(lowerCAmelCase  ):
                            return self._get_bounding_box(
                                torch.Tensor(
                                    [
                                        (width * bbox[0] / 1_000),
                                        (height * bbox[1] / 1_000),
                                        (width * bbox[2] / 1_000),
                                        (height * bbox[3] / 1_000),
                                    ]  )  )

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

                     lowerCamelCase_							=scores.tolist()
                     lowerCamelCase_							=[self.model.config.idalabel[label.item()] for label in labels]
                     lowerCamelCase_							=[self._get_bounding_box(lowerCAmelCase  ) for box in boxes]

                     # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                     lowerCamelCase_							=['''score''', '''label''', '''box''']
                     lowerCamelCase_							=[
                         dict(zip(lowerCAmelCase,		lowerCAmelCase  )  )
                         for vals in zip(raw_annotation['''scores'''],		raw_annotation['''labels'''],		raw_annotation['''boxes''']  )
                     ]

              return annotation






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







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











import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Any   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   ["""a""", """b""", """c"""]

					# Defaults to last layer if both are None
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[2]   )

					# Out indices set to match out features
					A__      ,			A__        =   get_aligned_output_features_output_indices(["""a""", """c"""]							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features set to match out indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[0, 2]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features selected from negative indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[-3, -1]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[-3, -1]   )






			def    a_       (			self   :    Optional[Any]   )	->	Optional[Any]:







					"""simple docstring"""






					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	__lowerCAmelCase   )

					# Out features must be a list
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(("""a""", """b""")							,	(0, 1)							,	["""a""", """b"""]   )

					# Out features must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	["""a"""]   )

					# Out indices must be a list or tuple
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	0							,	["""a""", """b"""]   )

					# Out indices must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	(0, 1)							,	["""a"""]   )

					# Out features and out indices must be the same length
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0,)							,	["""a""", """b""", """c"""]   )

					# Out features should match out indices
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 2)							,	["""a""", """b""", """c"""]   )

					# Out features and out indices should be in order
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""b""", """a"""]							,	(0, 1)							,	["""a""", """b"""]   )

					# Check passes with valid inputs
					verify_out_features_out_indices(["""a""", """b""", """d"""]							,	(0, 1, -1)							,	["""a""", """b""", """c""", """d"""]   )







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







					"""simple docstring"""






					A__        =   BackboneMixin()

					A__        =   ["""a""", """b""", """c"""]
					A__        =   ["""a""", """c"""]
					A__        =   [0, 2]

					# Check that the output features and indices are set correctly
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 2]   )

					# Check out features and indices are updated correctly
					A__        =   ["""a""", """b"""]
					self.assertEqual(backbone.out_features							,	["""a""", """b"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 1]   )

					A__        =   [-3, -1]
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[-3, -1]   )



