





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

lowercase				:	str      						=  logging.get_logger(__name__)


lowercase				:	Optional[int]      						=  Dict[str, Any]
lowercase				:	Any      						=  List[Prediction]





@add_end_docstrings(__SCREAMING_SNAKE_CASE     )
class 			A__   (							__SCREAMING_SNAKE_CASE     ):




   """simple docstring"""



   def __init__( self ,  *lowercase ,  **lowercase)      ->						Dict:







          '''simple docstring'''

          super().__init__(*_snake_case ,  **_snake_case)

          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 ,  **lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     List[str]			=    {}
          if "threshold" in kwargs:
                 a__						:     Any			=    kwargs["threshold"]
          return {}, {}, postprocess_kwargs

   def __call__( self ,  *lowercase ,  **lowercase)      ->						int:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    model_inputs.pop('target_size')
          a__						:     Union[str, Any]			=    self.model(**_snake_case)
          a__						:     Optional[Any]			=    outputs.__class__({'target_size': target_size, **outputs})
          if self.tokenizer is not None:
                 a__						:     Union[str, Any]			=    model_inputs["bbox"]
          return model_outputs

   def 							__lowercase		( self ,  lowercase ,  lowercase=0.9)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    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.
                 a__						:     Union[str, Any]			=    target_size[0].tolist()

                 def unnormalize(lowercase):
                        return self._get_bounding_box(
                            torch.Tensor(
                                [
                                    (width * bbox[0] / 1000),
                                    (height * bbox[1] / 1000),
                                    (width * bbox[2] / 1000),
                                    (height * bbox[3] / 1000),
                                ]))

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

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

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

          return annotation

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







          '''simple docstring'''

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









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


