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 DeformableDetrImageProcessor







class __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    int	,     _lowercase      :    Optional[int]	,     _lowercase      :    Union[str, Any]=7	,     _lowercase      :    Optional[Any]=3	,     _lowercase      :    List[str]=30	,     _lowercase      :    str=4_00	,     _lowercase      :    Optional[Any]=True	,     _lowercase      :    Tuple=None	,     _lowercase      :    str=True	,     _lowercase      :    int=[0.5, 0.5, 0.5]	,     _lowercase      :    Optional[Any]=[0.5, 0.5, 0.5]	,     _lowercase      :    Dict=True	,     _lowercase      :    Optional[Any]=1 / 2_55	,     _lowercase      :    Any=True	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				num_channels
     SCREAMING_SNAKE_CASE__      							=				min_resolution
     SCREAMING_SNAKE_CASE__      							=				max_resolution
     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				do_pad




   def      __a						(   self      :    str				):



     """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      :    str	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Optional[int]=False				):



     """simple docstring"""






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

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

     return expected_height, expected_width







@require_torch
@require_vision
class __snake_case    (						a__       ,		unittest.TestCase		):
   lowerCAmelCase_														=							DeformableDetrImageProcessor if is_vision_available() else None




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				DeformableDetrImageProcessingTester(self				)




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.image_processing_class.from_dict(self.image_processor_dict				)
     self.assertEqual(image_processor.size	,     {"""shortest_edge""": 18, """longest_edge""": 13_33}				)
     self.assertEqual(image_processor.do_pad	,     _lowerCamelCase				)

     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[Any]				):



     """simple docstring"""






     pass




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				self.image_processor_tester.get_expected_values(_lowerCamelCase	,     batched=_lowerCamelCase				)

     SCREAMING_SNAKE_CASE__      							=				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      :    Any				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowerCamelCase	,     return_tensors="""pt"""				).pixel_values

     SCREAMING_SNAKE_CASE__      							=				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      :    str				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				image_processing(_lowerCamelCase	,     return_tensors="""pt"""				).pixel_values

     SCREAMING_SNAKE_CASE__      							=				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[str]				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				{'''image_id''': 3_97_69, '''annotations''': target}

     # encode them
     SCREAMING_SNAKE_CASE__      							=				DeformableDetrImageProcessor()
     SCREAMING_SNAKE_CASE__      							=				image_processing(images=_lowerCamelCase	,     annotations=_lowerCamelCase	,     return_tensors="""pt"""				)

     # verify pixel values
     SCREAMING_SNAKE_CASE__      							=				torch.Size([1, 3, 8_00, 10_66]				)
     self.assertEqual(encoding["""pixel_values"""].shape	,     _lowerCamelCase				)

     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				torch.Size([6, 4]				)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,     _lowerCamelCase				)
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([3_97_69]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,     _lowerCamelCase				)				)
     # verify is_crowd
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,     _lowerCamelCase				)				)
     # verify class_labels
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([75, 75, 63, 65, 17, 17]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,     _lowerCamelCase				)				)
     # verify orig_size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([4_80, 6_40]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,     _lowerCamelCase				)				)
     # verify size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([8_00, 10_66]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,     _lowerCamelCase				)				)




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

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

     # encode them
     SCREAMING_SNAKE_CASE__      							=				DeformableDetrImageProcessor(format="""coco_panoptic"""				)
     SCREAMING_SNAKE_CASE__      							=				image_processing(images=_lowerCamelCase	,     annotations=_lowerCamelCase	,     masks_path=_lowerCamelCase	,     return_tensors="""pt"""				)

     # verify pixel values
     SCREAMING_SNAKE_CASE__      							=				torch.Size([1, 3, 8_00, 10_66]				)
     self.assertEqual(encoding["""pixel_values"""].shape	,     _lowerCamelCase				)

     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				torch.Size([6, 4]				)
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,     _lowerCamelCase				)
     SCREAMING_SNAKE_CASE__      							=				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
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([3_97_69]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,     _lowerCamelCase				)				)
     # verify is_crowd
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,     _lowerCamelCase				)				)
     # verify class_labels
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([17, 17, 63, 75, 75, 93]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,     _lowerCamelCase				)				)
     # verify masks
     SCREAMING_SNAKE_CASE__      							=				82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,     _lowerCamelCase				)
     # verify orig_size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([4_80, 6_40]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,     _lowerCamelCase				)				)
     # verify size
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([8_00, 10_66]				)
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,     _lowerCamelCase				)				)

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
