





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 DetrImageProcessor

class      A       (	unittest.TestCase						):



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

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



      def 	lowerCAmelCase__    (	self:						Dict       )						->    str:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[Any]=False       )						->    List[str]:
         '''simple docstring'''

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

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

         return expected_height, expected_width

@require_torch
@require_vision
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =DetrImageProcessor if is_vision_available() else None



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=DetrImageProcessingTester(self       )



      @property
      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=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_rescale"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "rescale_factor"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_resize"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "size"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_pad"       )       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=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       )

         UpperCAmelCase_											=self.image_processing_class.from_dict(
             self.image_processor_dict		,  size=42		,  max_size=84		,  pad_and_return_pixel_mask=_lowerCAmelCase       )
         self.assertEqual(image_processor.size		,  {"shortest_edge": 42, "longest_edge": 84}       )
         self.assertEqual(image_processor.do_pad		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						str       )						->    Dict:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						str       )						->    Tuple:
         '''simple docstring'''

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

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

         UpperCAmelCase_						,       UpperCAmelCase_											=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
         UpperCAmelCase_						,       UpperCAmelCase_											=self.image_processor_tester.get_expected_values(_lowerCAmelCase		,  batched=_lowerCAmelCase       )

         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Dict       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         UpperCAmelCase_											=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
         UpperCAmelCase_											=image_processing(image_inputs[0]		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=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
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Dict       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         UpperCAmelCase_											=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
         UpperCAmelCase_											=image_processing(image_inputs[0]		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=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
         UpperCAmelCase_											=image_processing(_lowerCAmelCase		,  return_tensors="pt"       ).pixel_values

         UpperCAmelCase_						,       UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						str       )						->    List[Any]:
         '''simple docstring'''

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

         UpperCAmelCase_											={"image_id": 3_9769, "annotations": target}

         # encode them
         UpperCAmelCase_											=DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"       )
         UpperCAmelCase_											=image_processing(images=_lowerCAmelCase		,  annotations=_lowerCAmelCase		,  return_tensors="pt"       )

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

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,  _lowerCAmelCase       )       )
         # verify boxes
         UpperCAmelCase_											=torch.Size([6, 4]       )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,  _lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  _lowerCAmelCase       )       )
         # verify is_crowd
         UpperCAmelCase_											=torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  _lowerCAmelCase       )       )
         # verify class_labels
         UpperCAmelCase_											=torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  _lowerCAmelCase       )       )
         # verify orig_size
         UpperCAmelCase_											=torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  _lowerCAmelCase       )       )
         # verify size
         UpperCAmelCase_											=torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )






      @slow
      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[int]:
         '''simple docstring'''

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

         UpperCAmelCase_											={"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

         # encode them
         UpperCAmelCase_											=DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"       )
         UpperCAmelCase_											=image_processing(images=_lowerCAmelCase		,  annotations=_lowerCAmelCase		,  masks_path=_lowerCAmelCase		,  return_tensors="pt"       )

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

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,  _lowerCAmelCase       )       )
         # verify boxes
         UpperCAmelCase_											=torch.Size([6, 4]       )
         self.assertEqual(encoding["labels"][0]["boxes"].shape		,  _lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,  _lowerCAmelCase       )       )
         # verify is_crowd
         UpperCAmelCase_											=torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,  _lowerCAmelCase       )       )
         # verify class_labels
         UpperCAmelCase_											=torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,  _lowerCAmelCase       )       )
         # verify masks
         UpperCAmelCase_											=82_2873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,  _lowerCAmelCase       )
         # verify orig_size
         UpperCAmelCase_											=torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,  _lowerCAmelCase       )       )
         # verify size
         UpperCAmelCase_											=torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )











__lowercase  :   Dict						      ="""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich




