





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 ConditionalDetrImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=7	,							A_=3	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							A_=True	,							A_=1 / 255	,							A_=True	,							):
       '''simple docstring'''




       UpperCamelCase     : int	     =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       UpperCamelCase     : Any	     =       parent
       UpperCamelCase     : str	     =       batch_size
       UpperCamelCase     : Any	     =       num_channels
       UpperCamelCase     : Any	     =       min_resolution
       UpperCamelCase     : str	     =       max_resolution
       UpperCamelCase     : str	     =       do_resize
       UpperCamelCase     : Optional[Any]	     =       size
       UpperCamelCase     : Any	     =       do_normalize
       UpperCamelCase     : Dict	     =       image_mean
       UpperCamelCase     : Union[str, Any]	     =       image_std
       UpperCamelCase     : List[str]	     =       do_rescale
       UpperCamelCase     : str	     =       rescale_factor
       UpperCamelCase     : Any	     =       do_pad





      def 	__UpperCamelCase(			self       ):
       '''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 	__UpperCamelCase(			self	,							A_	,							A_=False       ):
       '''simple docstring'''




       if not batched:
        UpperCamelCase     : Dict	     =       image_inputs[0]
        if isinstance(A_	,							Image.Image       ):
         UpperCamelCase     ,   UpperCamelCase     : Any	     =       image.size
        else:
         UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       image.shape[1], image.shape[2]
        if w < h:
         UpperCamelCase     : Union[str, Any]	     =       int(self.size["shortest_edge"] * h / w       )
         UpperCamelCase     : Any	     =       self.size["shortest_edge"]
        elif w > h:
         UpperCamelCase     : List[Any]	     =       self.size["shortest_edge"]
         UpperCamelCase     : Union[str, Any]	     =       int(self.size["shortest_edge"] * w / h       )
        else:
         UpperCamelCase     : Tuple	     =       self.size["shortest_edge"]
         UpperCamelCase     : str	     =       self.size["shortest_edge"]

       else:
        UpperCamelCase     : Union[str, Any]	     =       []
        for image in image_inputs:
         UpperCamelCase     ,   UpperCamelCase     : Dict	     =       self.get_expected_values([image]       )
         expected_values.append((expected_height, expected_width)       )
        UpperCamelCase     : Tuple	     =       max(A_	,							key=lambda A_       : item[0]       )[0]
        UpperCamelCase     : Union[str, Any]	     =       max(A_	,							key=lambda A_       : item[1]       )[1]

       return expected_height, expected_width



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :List[Any]						   =       ConditionalDetrImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       ConditionalDetrImageProcessingTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       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	,							A_       )

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Optional[int]	     =       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

       # Test not batched input
       UpperCamelCase     : List[Any]	     =       image_processing(image_inputs[0]	,							return_tensors="pt"       ).pixel_values

       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       self.image_processor_tester.get_expected_values(A_       )

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

       # Test batched
       UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       self.image_processor_tester.get_expected_values(A_	,							batched=A_       )

       UpperCamelCase     : Optional[int]	     =       image_processing(A_	,							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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : Optional[int]	     =       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

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

       UpperCamelCase     ,   UpperCamelCase     : Any	     =       self.image_processor_tester.get_expected_values(A_       )

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

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processing(A_	,							return_tensors="pt"       ).pixel_values

       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       self.image_processor_tester.get_expected_values(A_	,							batched=A_       )

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processing(image_inputs[0]	,							return_tensors="pt"       ).pixel_values

       UpperCamelCase     ,   UpperCamelCase     : str	     =       self.image_processor_tester.get_expected_values(A_       )

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

       # Test batched
       UpperCamelCase     : List[Any]	     =       image_processing(A_	,							return_tensors="pt"       ).pixel_values

       UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       self.image_processor_tester.get_expected_values(A_	,							batched=A_       )

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





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,							"r"       ) as f:
        UpperCamelCase     : Union[str, Any]	     =       json.loads(f.read()       )

       UpperCamelCase     : Any	     =       {"image_id": 3_9769, "annotations": target}

       # encode them
       UpperCamelCase     : List[Any]	     =       ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"       )
       UpperCamelCase     : Dict	     =       image_processing(images=A_	,							annotations=A_	,							return_tensors="pt"       )

       # verify pixel values
       UpperCamelCase     : List[str]	     =       torch.Size([1, 3, 800, 1066]       )
       self.assertEqual(encoding["pixel_values"].shape	,							A_       )

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

       # verify area
       UpperCamelCase     : Any	     =       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"]	,							A_       )       )
       # verify boxes
       UpperCamelCase     : int	     =       torch.Size([6, 4]       )
       self.assertEqual(encoding["labels"][0]["boxes"].shape	,							A_       )
       UpperCamelCase     : Any	     =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							A_	,							atol=1e-3       )       )
       # verify image_id
       UpperCamelCase     : int	     =       torch.tensor([3_9769]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							A_       )       )
       # verify is_crowd
       UpperCamelCase     : Any	     =       torch.tensor([0, 0, 0, 0, 0, 0]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							A_       )       )
       # verify class_labels
       UpperCamelCase     : Any	     =       torch.tensor([75, 75, 63, 65, 17, 17]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							A_       )       )
       # verify orig_size
       UpperCamelCase     : Tuple	     =       torch.tensor([480, 640]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							A_       )       )
       # verify size
       UpperCamelCase     : str	     =       torch.tensor([800, 1066]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							A_       )       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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

       UpperCamelCase     : Optional[int]	     =       {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       UpperCamelCase     : Tuple	     =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

       # encode them
       UpperCamelCase     : Optional[Any]	     =       ConditionalDetrImageProcessor(format="coco_panoptic"       )
       UpperCamelCase     : Optional[Any]	     =       image_processing(images=A_	,							annotations=A_	,							masks_path=A_	,							return_tensors="pt"       )

       # verify pixel values
       UpperCamelCase     : List[str]	     =       torch.Size([1, 3, 800, 1066]       )
       self.assertEqual(encoding["pixel_values"].shape	,							A_       )

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

       # verify area
       UpperCamelCase     : Optional[Any]	     =       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"]	,							A_       )       )
       # verify boxes
       UpperCamelCase     : Any	     =       torch.Size([6, 4]       )
       self.assertEqual(encoding["labels"][0]["boxes"].shape	,							A_       )
       UpperCamelCase     : Any	     =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							A_	,							atol=1e-3       )       )
       # verify image_id
       UpperCamelCase     : Optional[Any]	     =       torch.tensor([3_9769]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							A_       )       )
       # verify is_crowd
       UpperCamelCase     : Any	     =       torch.tensor([0, 0, 0, 0, 0, 0]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							A_       )       )
       # verify class_labels
       UpperCamelCase     : str	     =       torch.tensor([17, 17, 63, 75, 75, 93]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							A_       )       )
       # verify masks
       UpperCamelCase     : Dict	     =       82_2873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							A_       )
       # verify orig_size
       UpperCamelCase     : Optional[int]	     =       torch.tensor([480, 640]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							A_       )       )
       # verify size
       UpperCamelCase     : List[str]	     =       torch.tensor([800, 1066]       )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							A_       )       )







from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
