





'''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 DeformableDetrImageProcessor







class __a			( unittest.TestCase      ):





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
      __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   UpperCAmelCase__  (       self					:      Tuple     ):

      '''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					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      List[Any]=False     ):

      '''simple docstring'''

      if not batched:
         __SCREAMING_SNAKE_CASE   				=							image_inputs[0]
         if isinstance(lowerCamelCase					,Image.Image     ):
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							image.size
         else:
            __SCREAMING_SNAKE_CASE				,  __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				,  __SCREAMING_SNAKE_CASE   				=							self.get_expected_values([image]     )
            expected_values.append((expected_height, expected_width)     )
         __SCREAMING_SNAKE_CASE   				=							max(lowerCamelCase					,key=lambda lowerCamelCase     : item[0]     )[0]
         __SCREAMING_SNAKE_CASE   				=							max(lowerCamelCase					,key=lambda lowerCamelCase     : item[1]     )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class __a			( _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						DeformableDetrImageProcessor if is_vision_available() else None





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							DeformableDetrImageProcessingTester(self     )





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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""": 1333}     )
      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   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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				,  __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				,  __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   UpperCAmelCase__  (       self					:      str     ):

      '''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				,  __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				,  __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   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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				,  __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				,  __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   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''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_9769, """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, 800, 1066]     )
      self.assertEqual(encoding["""pixel_values"""].shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,lowerCamelCase					,atol=1E-4     )     )

      # verify area
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]     )
      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([0.5_503, 0.2_765, 0.0_604, 0.2_215]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,lowerCamelCase					,atol=1E-3     )     )
      # verify image_id
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([3_9769]     )
      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([480, 640]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,lowerCamelCase     )     )
      # verify size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([800, 1066]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,lowerCamelCase     )     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''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_9769, """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, 800, 1066]     )
      self.assertEqual(encoding["""pixel_values"""].shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([0.2_796, 0.3_138, 0.3_481]     )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]					,lowerCamelCase					,atol=1E-4     )     )

      # verify area
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]     )
      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([0.2_625, 0.5_437, 0.4_688, 0.8_625]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,lowerCamelCase					,atol=1E-3     )     )
      # verify image_id
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([3_9769]     )
      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_2873
      self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,lowerCamelCase     )
      # verify orig_size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([480, 640]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,lowerCamelCase     )     )
      # verify size
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([800, 1066]     )
      self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,lowerCamelCase     )     )












'''simple docstring'''

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						''
   __UpperCamelCase :			int						   =						'hf-legacy'  # "hf://"" is reserved for hffs





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[DatasetInfo] = None					,lowerCamelCase					:      Optional[str] = None					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(self					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							repo_info
      __SCREAMING_SNAKE_CASE   				=							token
      __SCREAMING_SNAKE_CASE   				=							None





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      if self.dir_cache is None:
         __SCREAMING_SNAKE_CASE   				=							{}
         for hf_file in self.repo_info.siblings:
            # TODO(QL): add sizes
            __SCREAMING_SNAKE_CASE   				=							{
                """name""": hf_file.rfilename,
                """size""": None,
                """type""": """file""",
            }
            self.dir_cache.update(
                {
                    str(lowerCamelCase     ): {"""name""": str(lowerCamelCase     ), """size""": None, """type""": """directory"""}
                    for d in list(PurePosixPath(hf_file.rfilename     ).parents     )[:-1]
                }     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      str					,lowerCamelCase					:      str = "rb"					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      if not isinstance(self.repo_info					,lowerCamelCase     ):
         raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""     )
      __SCREAMING_SNAKE_CASE   				=							hf_hub_url(self.repo_info.id					,lowerCamelCase					,revision=self.repo_info.sha     )
      return fsspec.open(
          lowerCamelCase					,mode=lowerCamelCase					,headers=get_authentication_headers_for_url(lowerCamelCase					,use_auth_token=self.token     )					,client_kwargs={"""trust_env""": True}					,).open()





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Any					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							self._strip_protocol(lowerCamelCase     )
      if path in self.dir_cache:
         return self.dir_cache[path]
      else:
         raise FileNotFoundError(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      Any					,lowerCamelCase					:      str=False					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							PurePosixPath(path.strip("""/"""     )     )
      __SCREAMING_SNAKE_CASE   				=							{}
      for p, f in self.dir_cache.items():
         __SCREAMING_SNAKE_CASE   				=							PurePosixPath(p.strip("""/"""     )     )
         __SCREAMING_SNAKE_CASE   				=							p.parent
         if root == path:
            __SCREAMING_SNAKE_CASE   				=							f
      __SCREAMING_SNAKE_CASE   				=							list(paths.values()     )
      if detail:
         return out
      else:
         return sorted(f["""name"""] for f in out     )





