


'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
       import jax
       import jaxlib

__snake_case    						=get_logger()

__snake_case    						=None

class 		UpperCAmelCase_      (  TensorFormatter[Mapping, '''jax.Array''', Mapping]		):





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      List[Any]=None    ,      **UpperCAmelCase__       :      Dict    )				->  int:
        super().__init__(features=UpperCAmelCase__    )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
           raise ValueError(
               F'''Expected {device} to be a `str` not {type(UpperCAmelCase__    )}, as `jaxlib.xla_extension.Device` '''
               'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
               'the device with `str()` to get its string identifier that will be internally mapped '
               'to the actual `jaxlib.xla_extension.Device`.'    )
        lowerCAmelCase     =			device if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else str(jax.devices()[0]    )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
           lowerCAmelCase     =			self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()    ):
           logger.warning(
               F'''Device with string identifier {self.device} not listed among the available '''
               F'''devices: {list(DEVICE_MAPPING.keys()    )}, so falling back to the default '''
               F'''device: {str(jax.devices()[0]    )}.'''    )
           lowerCAmelCase     =			str(jax.devices()[0]    )
        lowerCAmelCase     =			jnp_array_kwargs





     @staticmethod
     def 							__UpperCAmelCase      ( )				->  Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(UpperCAmelCase__    ): device for device in jax.devices()}





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      Optional[Any]    )				->  List[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) and column:
           if all(
               isinstance(UpperCAmelCase__    ,      jax.Array    ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column    ):
              return jnp.stack(UpperCAmelCase__    ,      axis=0    )
        return column





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  List[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(UpperCAmelCase__    ,      (str, bytes, type(UpperCAmelCase__    ))    ):
           return value
        elif isinstance(UpperCAmelCase__    ,      (np.character, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.character    ):
           return value.tolist()

        lowerCAmelCase     =			{}

        if isinstance(UpperCAmelCase__    ,      (np.number, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.integer    ):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
              lowerCAmelCase     =			{'dtype': jnp.intaa}
           else:
              lowerCAmelCase     =			{'dtype': jnp.intaa}
        elif isinstance(UpperCAmelCase__    ,      (np.number, np.ndarray)    ) and np.issubdtype(value.dtype    ,      np.floating    ):
           lowerCAmelCase     =			{'dtype': jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(UpperCAmelCase__    ,      PIL.Image.Image    ):
              lowerCAmelCase     =			np.asarray(UpperCAmelCase__    )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
           lowerCAmelCase     =			self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]    ):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(UpperCAmelCase__    ,      **{**default_dtype, **self.jnp_array_kwargs}    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[int]    )				->  str:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(UpperCAmelCase__    ,      torch.Tensor    ):
              return self._tensorize(data_struct.detach().cpu().numpy()[()]    )
        if hasattr(UpperCAmelCase__    ,      '__array__'    ) and not isinstance(UpperCAmelCase__    ,      jax.Array    ):
           lowerCAmelCase     =			data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(UpperCAmelCase__    ,      np.ndarray    ):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(UpperCAmelCase__    ) for substruct in data_struct]    )
        elif isinstance(UpperCAmelCase__    ,      (list, tuple)    ):
           return self._consolidate([self.recursive_tensorize(UpperCAmelCase__    ) for substruct in data_struct]    )
        return self._tensorize(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      dict    )				->  str:
        return map_nested(self._recursive_tensorize    ,      UpperCAmelCase__    ,      map_list=UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      pa.Table    )				->  Mapping:
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_row(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_row(UpperCAmelCase__    )
        return self.recursive_tensorize(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      pa.Table    )				->  "jax.Array":
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_column(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_column(UpperCAmelCase__    ,      pa_table.column_names[0]    )
        lowerCAmelCase     =			self.recursive_tensorize(UpperCAmelCase__    )
        lowerCAmelCase     =			self._consolidate(UpperCAmelCase__    )
        return column





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      pa.Table    )				->  Mapping:
        lowerCAmelCase     =			self.numpy_arrow_extractor().extract_batch(UpperCAmelCase__    )
        lowerCAmelCase     =			self.python_features_decoder.decode_batch(UpperCAmelCase__    )
        lowerCAmelCase     =			self.recursive_tensorize(UpperCAmelCase__    )
        for column_name in batch:
           lowerCAmelCase     =			self._consolidate(batch[column_name]    )
        return batch












'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:


  class  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       MODEL_FOR_OBJECT_DETECTION_MAPPING


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'						,	threshold=0.0)

          self.assertGreater(len(_UpperCAmelCase)						,	0)
          for detected_object in outputs:
               self.assertEqual(
                   _UpperCAmelCase						,	{
                       'score': ANY(_UpperCAmelCase),
                       'label': ANY(_UpperCAmelCase),
                       'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                   }						,	)

          import datasets

          __A						: Tuple      =       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,	'image'						,	split='test')

          __A						: List[str]      =       [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'),
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              # RGBA
              dataset[0]['file'],
              # LA
              dataset[1]['file'],
              # L
              dataset[2]['file'],
          ]
          __A						: Union[str, Any]      =       object_detector(_UpperCAmelCase						,	threshold=0.0)

          self.assertEqual(len(_UpperCAmelCase)						,	len(_UpperCAmelCase))
          for outputs in batch_outputs:
               self.assertGreater(len(_UpperCAmelCase)						,	0)
               for detected_object in outputs:
                    self.assertEqual(
                        _UpperCAmelCase						,	{
                            'score': ANY(_UpperCAmelCase),
                            'label': ANY(_UpperCAmelCase),
                            'box': {'xmin': ANY(_UpperCAmelCase), 'ymin': ANY(_UpperCAmelCase), 'xmax': ANY(_UpperCAmelCase), 'ymax': ANY(_UpperCAmelCase)},
                        }						,	)


     @require_tf
     @unittest.skip('Object detection not implemented in TF')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       'hf-internal-testing/tiny-detr-mobilenetsv3'

          __A						: Any      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: int      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: Any      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Dict      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=0.0)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
              ]						,	)

          __A						: Optional[Any]      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ]						,	threshold=0.0						,	)

          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
                  [
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                      {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       'facebook/detr-resnet-50'

          __A						: Union[str, Any]      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: Tuple      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: List[Any]      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Union[str, Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: Tuple      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: Any      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)

          __A						: str      =       object_detector(
              [
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
              ])
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
                  [
                      {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                      {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                      {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                      {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                      {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
                  ],
              ]						,	)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       0.9985
          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: List[str]      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: List[Any]      =       object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,	threshold=_UpperCAmelCase)
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
              ]						,	)


     @require_torch
     @require_pytesseract
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       'Narsil/layoutlmv3-finetuned-funsd'
          __A						: Tuple      =       0.9993

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase						,	threshold=_UpperCAmelCase)

          __A						: Optional[int]      =       object_detector(
              'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png')
          self.assertEqual(
              nested_simplify(_UpperCAmelCase						,	decimals=4)						,	[
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
                  {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
              ]						,	)