


'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor




class 	a       (       unittest.TestCase    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=7 ,			__magic_name__=3 ,			__magic_name__=18 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=True ,			__magic_name__=None ,			__magic_name__=True ,			__magic_name__=None ,			__magic_name__=True ,			__magic_name__=[0.5, 0.5, 0.5] ,			__magic_name__=[0.5, 0.5, 0.5] ,			__magic_name__=False ,			)   ->							List[str]:
      _a      =							size if size is not None else {'height': 20, 'width': 20}
      _a      =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
      _a      =							parent
      _a      =							batch_size
      _a      =							num_channels
      _a      =							image_size
      _a      =							min_resolution
      _a      =							max_resolution
      _a      =							do_resize
      _a      =							size
      _a      =							do_center_crop
      _a      =							crop_size
      _a      =							do_normalize
      _a      =							image_mean
      _a      =							image_std
      _a      =							do_reduce_labels




 def 							__UpperCAmelCase						(	self  )   ->							str:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 		_A			()				->					int:




     '''simple docstring'''



     _a      =							load_dataset('hf-internal-testing/fixtures_ade20k'			,  split='test'						)

     _a      =							Image.open(dataset[0]['file']						)
     _a      =							Image.open(dataset[1]['file']						)

     return image, map






def 		_A			()				->					Tuple:




     '''simple docstring'''



     _a      =							load_dataset('hf-internal-testing/fixtures_ade20k'			,  split='test'						)

     _a      =							Image.open(ds[0]['file']						)
     _a      =							Image.open(ds[1]['file']						)
     _a      =							Image.open(ds[2]['file']						)
     _a      =							Image.open(ds[3]['file']						)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		BeitImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							BeitImageProcessingTester(self  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							str:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(__magic_name__ ,			'do_resize'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'size'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'do_center_crop'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'center_crop'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'do_normalize'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'image_mean'  )  )
      self.assertTrue(hasattr(__magic_name__ ,			'image_std'  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size ,			{'height': 20, 'width': 20}  )
      self.assertEqual(image_processor.crop_size ,			{'height': 18, 'width': 18}  )
      self.assertEqual(image_processor.do_reduce_labels ,			__magic_name__  )

      _a      =							self.image_processing_class.from_dict(
          self.image_processor_dict ,			size=42 ,			crop_size=84 ,			reduce_labels=__magic_name__  )
      self.assertEqual(image_processor.size ,			{'height': 42, 'width': 42}  )
      self.assertEqual(image_processor.crop_size ,			{'height': 84, 'width': 84}  )
      self.assertEqual(image_processor.do_reduce_labels ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__magic_name__  )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__ ,			Image.Image  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)

      # Test batched
      _a      =							image_processing(__magic_name__ ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
 def 							__UpperCAmelCase						(	self  )   ->							int:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__magic_name__ ,			numpify=__magic_name__  )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__ ,			np.ndarray  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)

      # Test batched
      _a      =							image_processing(__magic_name__ ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__magic_name__ ,			torchify=__magic_name__  )
      for image in image_inputs:
           self.assertIsInstance(__magic_name__ ,			torch.Tensor  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)

      # Test batched
      _a      =							image_processing(__magic_name__ ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__magic_name__ ,			torchify=__magic_name__  )
      _a      =							[]
      for image in image_inputs:
           self.assertIsInstance(__magic_name__ ,			torch.Tensor  )
           maps.append(torch.zeros(image.shape[-2:]  ).long()  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0] ,			maps[0] ,			return_tensors='pt'  )
      self.assertEqual(
          encoding['pixel_values'].shape ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(
          encoding['labels'].shape ,			(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(encoding['labels'].dtype ,			torch.long  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )

      # Test batched
      _a      =							image_processing(__magic_name__ ,			__magic_name__ ,			return_tensors='pt'  )
      self.assertEqual(
          encoding['pixel_values'].shape ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(
          encoding['labels'].shape ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(encoding['labels'].dtype ,			torch.long  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )

      # Test not batched input (PIL images)
      _a ,				_a      =							prepare_semantic_single_inputs()

      _a      =							image_processing(__magic_name__ ,			__magic_name__ ,			return_tensors='pt'  )
      self.assertEqual(
          encoding['pixel_values'].shape ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(
          encoding['labels'].shape ,			(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(encoding['labels'].dtype ,			torch.long  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )

      # Test batched input (PIL images)
      _a ,				_a      =							prepare_semantic_batch_inputs()

      _a      =							image_processing(__magic_name__ ,			__magic_name__ ,			return_tensors='pt'  )
      self.assertEqual(
          encoding['pixel_values'].shape ,			(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(
          encoding['labels'].shape ,			(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          ) ,			)
      self.assertEqual(encoding['labels'].dtype ,			torch.long  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _a ,				_a      =							prepare_semantic_single_inputs()
      _a      =							image_processing(__magic_name__ ,			__magic_name__ ,			return_tensors='pt'  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 1_50  )

      _a      =							True
      _a      =							image_processing(__magic_name__ ,			__magic_name__ ,			return_tensors='pt'  )
      self.assertTrue(encoding['labels'].min().item() >= 0  )
      self.assertTrue(encoding['labels'].max().item() <= 2_55  )





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline






@pytest.mark.integration
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
          with pytest.raises(lowerCAmelCase__						):
               requests.request('GET'			,  'https://huggingface.co'						)
          with pytest.raises(requests.exceptions.ConnectTimeout						):
               requests.request('GET'			,  'https://huggingface.co'			,  timeout=1.0						)






@pytest.mark.integration
def 		_A			()				->					Any:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_FAILS						):
          with pytest.raises(requests.exceptions.ConnectionError						):
               requests.request('GET'			,  'https://huggingface.co'						)






def 		_A			()				->					Dict:




     '''simple docstring'''



     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
          with pytest.raises(lowerCAmelCase__						):
               http_head('https://huggingface.co'						)

