





'''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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


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

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






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

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

          return expected_height, expected_width




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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			DeformableDetrImageProcessingTester(self)


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_UpperCAmelCase		,      'image_mean'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'image_std'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_normalize'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_resize'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_rescale'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'do_pad'))
          self.assertTrue(hasattr(_UpperCAmelCase		,      'size'))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			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		,      _UpperCAmelCase)

          __A		:					int						=			self.image_processing_class.from_dict(
              self.image_processor_dict		,      size=42		,      max_size=84		,      pad_and_return_pixel_mask=_UpperCAmelCase)
          self.assertEqual(image_processor.size		,      {'shortest_edge': 42, 'longest_edge': 84})
          self.assertEqual(image_processor.do_pad		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          # Test not batched input
          __A		:					Optional[int]						=			image_processing(image_inputs[0]		,      return_tensors='pt').pixel_values

          __A					,__A		:					Optional[int]						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase)

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

          # Test batched
          __A					,__A		:					int						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase		,      batched=_UpperCAmelCase)

          __A		:					Dict						=			image_processing(_UpperCAmelCase		,      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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          __A		:					Tuple						=			prepare_image_inputs(self.image_processor_tester		,      equal_resolution=_UpperCAmelCase		,      numpify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase		,      np.ndarray)

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

          __A					,__A		:					Union[str, Any]						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase)

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

          # Test batched
          __A		:					Tuple						=			image_processing(_UpperCAmelCase		,      return_tensors='pt').pixel_values

          __A					,__A		:					Tuple						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase		,      batched=_UpperCAmelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          __A		:					List[Any]						=			prepare_image_inputs(self.image_processor_tester		,      equal_resolution=_UpperCAmelCase		,      torchify=_UpperCAmelCase)
          for image in image_inputs:
               self.assertIsInstance(_UpperCAmelCase		,      torch.Tensor)

          # Test not batched input
          __A		:					Dict						=			image_processing(image_inputs[0]		,      return_tensors='pt').pixel_values

          __A					,__A		:					Optional[int]						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase)

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

          # Test batched
          __A		:					str						=			image_processing(_UpperCAmelCase		,      return_tensors='pt').pixel_values

          __A					,__A		:					Tuple						=			self.image_processor_tester.get_expected_values(_UpperCAmelCase		,      batched=_UpperCAmelCase)

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


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,      'r') as f:
               __A		:					Dict						=			json.loads(f.read())

          __A		:					str						=			{'image_id': 3_9769, 'annotations': target}

          # encode them
          __A		:					List[Any]						=			DeformableDetrImageProcessor()
          __A		:					Optional[int]						=			image_processing(images=_UpperCAmelCase		,      annotations=_UpperCAmelCase		,      return_tensors='pt')

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

          __A		:					Tuple						=			torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,      _UpperCAmelCase		,      atol=1e-4))

          # verify area
          __A		:					Tuple						=			torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,      _UpperCAmelCase))
          # verify boxes
          __A		:					Tuple						=			torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape		,      _UpperCAmelCase)
          __A		:					Any						=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,      _UpperCAmelCase		,      atol=1e-3))
          # verify image_id
          __A		:					Tuple						=			torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,      _UpperCAmelCase))
          # verify is_crowd
          __A		:					Optional[Any]						=			torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,      _UpperCAmelCase))
          # verify class_labels
          __A		:					Optional[Any]						=			torch.tensor([75, 75, 63, 65, 17, 17])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,      _UpperCAmelCase))
          # verify orig_size
          __A		:					Any						=			torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,      _UpperCAmelCase))
          # verify size
          __A		:					Dict						=			torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,      _UpperCAmelCase))


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,      'r') as f:
               __A		:					Optional[int]						=			json.loads(f.read())

          __A		:					int						=			{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

          __A		:					Tuple						=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

          # encode them
          __A		:					str						=			DeformableDetrImageProcessor(format='coco_panoptic')
          __A		:					Any						=			image_processing(images=_UpperCAmelCase		,      annotations=_UpperCAmelCase		,      masks_path=_UpperCAmelCase		,      return_tensors='pt')

          # verify pixel values
          __A		:					str						=			torch.Size([1, 3, 800, 1066])
          self.assertEqual(encoding['pixel_values'].shape		,      _UpperCAmelCase)

          __A		:					List[str]						=			torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,      _UpperCAmelCase		,      atol=1e-4))

          # verify area
          __A		:					Any						=			torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,      _UpperCAmelCase))
          # verify boxes
          __A		:					Optional[int]						=			torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape		,      _UpperCAmelCase)
          __A		:					Optional[Any]						=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,      _UpperCAmelCase		,      atol=1e-3))
          # verify image_id
          __A		:					List[Any]						=			torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,      _UpperCAmelCase))
          # verify is_crowd
          __A		:					Optional[int]						=			torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,      _UpperCAmelCase))
          # verify class_labels
          __A		:					List[str]						=			torch.tensor([17, 17, 63, 75, 75, 93])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,      _UpperCAmelCase))
          # verify masks
          __A		:					Dict						=			82_2873
          self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,      _UpperCAmelCase)
          # verify orig_size
          __A		:					Optional[int]						=			torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,      _UpperCAmelCase))
          # verify size
          __A		:					int						=			torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,      _UpperCAmelCase))

"""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__					(						A__       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



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







      '''simple docstring'''



      super().__init__(self      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=repo_info
      UpperCamelCase_     :					Any   	=token
      UpperCamelCase_     :					Tuple   	=None



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.dir_cache is None:
       UpperCamelCase_     :					Any   	={}
       for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        UpperCamelCase_     :					Optional[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  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str = "rb"      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  _lowerCamelCase   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					List[Any]   	=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  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Tuple   	=self._strip_protocol(_lowerCamelCase   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=False      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



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