






'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetaImageProcessor
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]=7				,     __lowerCamelCase     :     List[str]=3				,     __lowerCamelCase     :     List[Any]=30				,     __lowerCamelCase     :     Any=400				,     __lowerCamelCase     :     Union[str, Any]=True				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     int=True				,     __lowerCamelCase     :     str=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     int=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     Optional[int]=True				,     __lowerCamelCase     :     Dict=1 / 255				,     __lowerCamelCase     :     Union[str, Any]=True				,     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     min_resolution
     lowerCamelCase__  =     max_resolution
     lowerCamelCase__  =     do_resize
     lowerCamelCase__  =     size
     lowerCamelCase__  =     do_normalize
     lowerCamelCase__  =     image_mean
     lowerCamelCase__  =     image_std
     lowerCamelCase__  =     do_rescale
     lowerCamelCase__  =     rescale_factor
     lowerCamelCase__  =     do_pad

    def    a__				(					self     :     Optional[int]     )							->							Dict:

     '''simple docstring'''




     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }




    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Union[str, Any]=False     )							->							List[str]:

     '''simple docstring'''




     if not batched:
      lowerCamelCase__  =     image_inputs[0]
      if isinstance(__lowerCamelCase				,     Image.Image     ):
       lowerCamelCase__  ,	lowerCamelCase__  =     image.size
      else:
       lowerCamelCase__  ,	lowerCamelCase__  =     image.shape[1], image.shape[2]
      if w < h:
       lowerCamelCase__  =     int(self.size["shortest_edge"] * h / w     )
       lowerCamelCase__  =     self.size["shortest_edge"]
      elif w > h:
       lowerCamelCase__  =     self.size["shortest_edge"]
       lowerCamelCase__  =     int(self.size["shortest_edge"] * w / h     )
      else:
       lowerCamelCase__  =     self.size["shortest_edge"]
       lowerCamelCase__  =     self.size["shortest_edge"]

     else:
      lowerCamelCase__  =     []
      for image in image_inputs:
       lowerCamelCase__  ,	lowerCamelCase__  =     self.get_expected_values([image]     )
       expected_values.append((expected_height, expected_width)     )
      lowerCamelCase__  =     max(__lowerCamelCase				,     key=lambda __lowerCamelCase     : item[0]     )[0]
      lowerCamelCase__  =     max(__lowerCamelCase				,     key=lambda __lowerCamelCase     : item[1]     )[1]

     return expected_height, expected_width
@require_torch
@require_vision
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							DetaImageProcessor if is_vision_available() else None

    def    a__				(					self     :     List[str]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     DetaImageProcessingTester(self     )

    @property
    def    a__				(					self     :     Any     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

    def    a__				(					self     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "image_mean"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "image_std"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_normalize"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_resize"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_rescale"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "do_pad"     )     )
     self.assertTrue(hasattr(__lowerCamelCase				,     "size"     )     )

    def    a__				(					self     :     Any     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size				,     {"shortest_edge": 18, "longest_edge": 1333}     )
     self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

    def    a__				(					self     :     Optional[int]     )							->							Dict:

     '''simple docstring'''




     pass

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

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     Image.Image     )

     # Test not batched input
     lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

     self.assertEqual(
         encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

     # Test batched
     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values
     self.assertEqual(
         encoded_images.shape				,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,     )

    def    a__				(					self     :     Dict     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     numpify=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     np.ndarray     )

     # Test not batched input
     lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

     self.assertEqual(
         encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

     # Test batched
     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

     self.assertEqual(
         encoded_images.shape				,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,     )

    def    a__				(					self     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=__lowerCamelCase				,     torchify=__lowerCamelCase     )
     for image in image_inputs:
      self.assertIsInstance(__lowerCamelCase				,     torch.Tensor     )

     # Test not batched input
     lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

     self.assertEqual(
         encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

     # Test batched
     lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

     lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

     self.assertEqual(
         encoded_images.shape				,     (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )				,     )

    @slow
    def    a__				(					self     :     List[Any]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,     "r"     ) as f:
      lowerCamelCase__  =     json.loads(f.read()     )

     lowerCamelCase__  =     {"image_id": 39769, "annotations": target}

     # encode them
     lowerCamelCase__  =     DetaImageProcessor()
     lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     return_tensors="pt"     )

     # verify pixel values
     lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
     self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

     lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     __lowerCamelCase				,     atol=1E-4     )     )

     # verify area
     lowerCamelCase__  =     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
     # verify boxes
     lowerCamelCase__  =     torch.Size([6, 4]     )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
     lowerCamelCase__  =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     __lowerCamelCase				,     atol=1E-3     )     )
     # verify image_id
     lowerCamelCase__  =     torch.tensor([39769]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
     # verify is_crowd
     lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
     # verify class_labels
     lowerCamelCase__  =     torch.tensor([75, 75, 63, 65, 17, 17]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
     # verify orig_size
     lowerCamelCase__  =     torch.tensor([480, 640]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
     # verify size
     lowerCamelCase__  =     torch.tensor([800, 1066]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )




    @slow
    def    a__				(					self     :     int     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,     "r"     ) as f:
      lowerCamelCase__  =     json.loads(f.read()     )

     lowerCamelCase__  =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

     lowerCamelCase__  =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

     # encode them
     lowerCamelCase__  =     DetaImageProcessor(format="coco_panoptic"     )
     lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     masks_path=__lowerCamelCase				,     return_tensors="pt"     )

     # verify pixel values
     lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
     self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

     lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     __lowerCamelCase				,     atol=1E-4     )     )

     # verify area
     lowerCamelCase__  =     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
     # verify boxes
     lowerCamelCase__  =     torch.Size([6, 4]     )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
     lowerCamelCase__  =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     __lowerCamelCase				,     atol=1E-3     )     )
     # verify image_id
     lowerCamelCase__  =     torch.tensor([39769]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
     # verify is_crowd
     lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
     # verify class_labels
     lowerCamelCase__  =     torch.tensor([17, 17, 63, 75, 75, 93]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
     # verify masks
     lowerCamelCase__  =     822873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     __lowerCamelCase     )
     # verify orig_size
     lowerCamelCase__  =     torch.tensor([480, 640]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
     # verify size
     lowerCamelCase__  =     torch.tensor([800, 1066]     )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )









'''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

__A							:							Any  							=		get_logger()

__A							:							Optional[dict]  							=		None
class 				lowercase       (							TensorFormatter[Mapping, "jax.Array", Mapping]      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     Dict=None				,     **__lowerCamelCase     :     str     )							->							List[Any]:

     '''simple docstring'''




     super().__init__(features=__lowerCamelCase     )
     import jax
     from jaxlib.xla_client import Device

     if isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
      raise ValueError(
          f'''Expected {device} to be a `str` not {type(__lowerCamelCase     )}, 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(__lowerCamelCase				,     __lowerCamelCase     ) 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    a__				(					)							->							Dict[str, "jaxlib.xla_extension.Device"]:

     '''simple docstring'''




     import jax

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

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




     import jax
     import jax.numpy as jnp

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

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     import jax
     import jax.numpy as jnp

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

     lowerCamelCase__  =     {}

     if isinstance(__lowerCamelCase				,     (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(__lowerCamelCase				,     (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(__lowerCamelCase				,     PIL.Image.Image     ):
       lowerCamelCase__  =     np.asarray(__lowerCamelCase     )

        # 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(__lowerCamelCase				,     **{**default_dtype, **self.jnp_array_kwargs}     )

    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     import jax

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

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

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     dict     )							->							int:

     '''simple docstring'''




     return map_nested(self._recursive_tensorize				,     __lowerCamelCase				,     map_list=__lowerCamelCase     )

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     pa.Table     )							->							Mapping:

     '''simple docstring'''




     lowerCamelCase__  =     self.numpy_arrow_extractor().extract_row(__lowerCamelCase     )
     lowerCamelCase__  =     self.python_features_decoder.decode_row(__lowerCamelCase     )
     return self.recursive_tensorize(__lowerCamelCase     )

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     pa.Table     )							->							"jax.Array":

     '''simple docstring'''




     lowerCamelCase__  =     self.numpy_arrow_extractor().extract_column(__lowerCamelCase     )
     lowerCamelCase__  =     self.python_features_decoder.decode_column(__lowerCamelCase				,     pa_table.column_names[0]     )
     lowerCamelCase__  =     self.recursive_tensorize(__lowerCamelCase     )
     lowerCamelCase__  =     self._consolidate(__lowerCamelCase     )
     return column




    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     pa.Table     )							->							Mapping:

     '''simple docstring'''




     lowerCamelCase__  =     self.numpy_arrow_extractor().extract_batch(__lowerCamelCase     )
     lowerCamelCase__  =     self.python_features_decoder.decode_batch(__lowerCamelCase     )
     lowerCamelCase__  =     self.recursive_tensorize(__lowerCamelCase     )
     for column_name in batch:
      lowerCamelCase__  =     self._consolidate(batch[column_name]     )
     return batch

