





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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		str					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		List[Any]=7					,  lowerCAmelCase_ :		Optional[Any]=3					,  lowerCAmelCase_ :		Dict=3_0					,  lowerCAmelCase_ :		Optional[int]=4_0_0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		str=None					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=1 / 2_5_5					,  lowerCAmelCase_ :		Optional[Any]=True					,  lowerCAmelCase_ :		List[str]=[0.5, 0.5, 0.5]					,  lowerCAmelCase_ :		Union[str, Any]=[0.5, 0.5, 0.5]					,  lowerCAmelCase_ :		Dict=True					,  )							->						Optional[int]:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     __lowerCAmelCase								=	size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	min_resolution
     __lowerCAmelCase								=	max_resolution
     __lowerCAmelCase								=	do_resize
     __lowerCAmelCase								=	size
     __lowerCAmelCase								=	do_rescale
     __lowerCAmelCase								=	rescale_factor
     __lowerCAmelCase								=	do_normalize
     __lowerCAmelCase								=	image_mean
     __lowerCAmelCase								=	image_std
     __lowerCAmelCase								=	do_pad






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

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

     return expected_height, expected_width





@require_torch
@require_vision
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = DetrImageProcessor if is_vision_available() else None






  def 				lowercase		(				self :		List[Any]     )							->						int:
     __lowerCAmelCase								=	DetrImageProcessingTester(self     )






  @property
  def 				lowercase		(				self :		Any     )							->						List[str]:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 				lowercase		(				self :		List[Any]     )							->						int:
     __lowerCAmelCase								=	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 				lowercase		(				self :		str     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size					,  {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}     )
     self.assertEqual(image_processor.do_pad					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	self.image_processing_class.from_dict(
         self.image_processor_dict					,  size=4_2					,  max_size=8_4					,  pad_and_return_pixel_mask=lowerCAmelCase_     )
     self.assertEqual(image_processor.size					,  {'shortest_edge': 4_2, 'longest_edge': 8_4}     )
     self.assertEqual(image_processor.do_pad					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						Optional[Any]:
     pass






  def 				lowercase		(				self :		int     )							->						Any:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processor_tester					,  equal_resolution=lowerCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_					,  Image.Image     )

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

     __lowerCAmelCase	,	__lowerCAmelCase								=	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
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processor_tester.get_expected_values(lowerCAmelCase_					,  batched=lowerCAmelCase_     )

     __lowerCAmelCase								=	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 				lowercase		(				self :		Dict     )							->						List[Any]:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	image_processing(image_inputs[0]					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	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
     __lowerCAmelCase								=	image_processing(lowerCAmelCase_					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	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 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     # Initialize image_processing
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	image_processing(image_inputs[0]					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	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
     __lowerCAmelCase								=	image_processing(lowerCAmelCase_					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	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 				lowercase		(				self :		Tuple     )							->						str:
     # prepare image and target
     __lowerCAmelCase								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'					,  'r'     ) as f:
        __lowerCAmelCase								=	json.loads(f.read()     )

     __lowerCAmelCase								=	{'image_id': 3_9_7_6_9, 'annotations': target}

     # encode them
     __lowerCAmelCase								=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'     )
     __lowerCAmelCase								=	image_processing(images=lowerCAmelCase_					,  annotations=lowerCAmelCase_					,  return_tensors='pt'     )

     # verify pixel values
     __lowerCAmelCase								=	torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
     self.assertEqual(encoding['pixel_values'].shape					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	torch.Size([6, 4]     )
     self.assertEqual(encoding['labels'][0]['boxes'].shape					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	torch.tensor([3_9_7_6_9]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,  lowerCAmelCase_     )     )
     # verify is_crowd
     __lowerCAmelCase								=	torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,  lowerCAmelCase_     )     )
     # verify class_labels
     __lowerCAmelCase								=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,  lowerCAmelCase_     )     )
     # verify orig_size
     __lowerCAmelCase								=	torch.tensor([4_8_0, 6_4_0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,  lowerCAmelCase_     )     )
     # verify size
     __lowerCAmelCase								=	torch.tensor([8_0_0, 1_0_6_6]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,  lowerCAmelCase_     )     )






  @slow
  def 				lowercase		(				self :		Optional[int]     )							->						str:
     # prepare image, target and masks_path
     __lowerCAmelCase								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'					,  'r'     ) as f:
        __lowerCAmelCase								=	json.loads(f.read()     )

     __lowerCAmelCase								=	{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

     __lowerCAmelCase								=	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

     # encode them
     __lowerCAmelCase								=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'     )
     __lowerCAmelCase								=	image_processing(images=lowerCAmelCase_					,  annotations=lowerCAmelCase_					,  masks_path=lowerCAmelCase_					,  return_tensors='pt'     )

     # verify pixel values
     __lowerCAmelCase								=	torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
     self.assertEqual(encoding['pixel_values'].shape					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	torch.Size([6, 4]     )
     self.assertEqual(encoding['labels'][0]['boxes'].shape					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	torch.tensor([3_9_7_6_9]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,  lowerCAmelCase_     )     )
     # verify is_crowd
     __lowerCAmelCase								=	torch.tensor([0, 0, 0, 0, 0, 0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,  lowerCAmelCase_     )     )
     # verify class_labels
     __lowerCAmelCase								=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,  lowerCAmelCase_     )     )
     # verify masks
     __lowerCAmelCase								=	8_2_2_8_7_3
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,  lowerCAmelCase_     )
     # verify orig_size
     __lowerCAmelCase								=	torch.tensor([4_8_0, 6_4_0]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,  lowerCAmelCase_     )     )
     # verify size
     __lowerCAmelCase								=	torch.tensor([8_0_0, 1_0_6_6]     )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,  lowerCAmelCase_     )     )








import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

_snake_case  :    List[compression.BaseCompressedFileFileSystem]						    =  [
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(F"""A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.""")
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	not is_remote_filesystem(lowerCAmelCase_	)
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase_	),	fs._strip_protocol(lowerCAmelCase_	)	)
   else:
      fs.mv(lowerCAmelCase_,	lowerCAmelCase_,	recursive=lowerCAmelCase_	)




def   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

