"""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 DetrImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     List[Any]      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     List[Any]=7      , __lowerCAmelCase		:     Dict=3      , __lowerCAmelCase		:     Union[str, Any]=30      , __lowerCAmelCase		:     Any=400      , __lowerCAmelCase		:     Dict=True      , __lowerCAmelCase		:     List[Any]=None      , __lowerCAmelCase		:     str=True      , __lowerCAmelCase		:     Optional[Any]=1 / 255      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     Optional[int]=[0.5, 0.5, 0.5]      , __lowerCAmelCase		:     Tuple=[0.5, 0.5, 0.5]      , __lowerCAmelCase		:     Any=True      , ):
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      _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		:     Union[str, Any]    ):
      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		:     Any      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     str=False    ):
      if not batched:
        _UpperCAmelCase         =					image_inputs[0]
        if isinstance(_lowerCamelCase      , Image.Image    ):
          _UpperCAmelCase         =					image.size
        else:
          _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         =					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 (						_snake_case		,							unittest.TestCase      ):
    _snake_case   :     str   							=							DetrImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					DetrImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _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[Any]    ):
      _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		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _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         =					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         =					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		:     Tuple    ):
      # Initialize image_processing
      _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         =					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         =					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    ):
      # Initialize image_processing
      _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         =					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         =					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		:     Tuple    ):
      # prepare image and target
      _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([0.2_796, 0.3_138, 0.3_481]    )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      , _lowerCamelCase      , atol=1e-4    )    )

      # verify area
      _UpperCAmelCase         =					torch.tensor([5_887.9_600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]    )
      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([0.5_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
      _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    ):
      # prepare image, target and masks_path
      _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([0.2_796, 0.3_138, 0.3_481]    )
      self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      , _lowerCamelCase      , atol=1e-4    )    )

      # verify area
      _UpperCAmelCase         =					torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5_879.6_562, 7_634.1_147]    )
      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([0.2_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
      _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    )    )




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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.dir_cache is None:
             a						:Dict 							=       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   a						:List[Any] 							=       {
                       '''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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = "rb"					,  **_lowerCamelCase					,  ):
       if not isinstance(self.repo_info					,  _lowerCamelCase    ):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
       a						:Optional[int] 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:str 							=       PurePosixPath(path.strip('''/'''    )    )
       a						:Tuple 							=       {}
       for p, f in self.dir_cache.items():
             a						:Optional[int] 							=       PurePosixPath(p.strip('''/'''    )    )
             a						:str 							=       p.parent
             if root == path:
                   a						:List[str] 							=       f
       a						:Any 							=       list(paths.values()    )
       if detail:
             return out
       else:
             return sorted(f['''name'''] for f in out    )





