






'''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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=7 ,   lowerCamelCase=3 ,   lowerCamelCase=18 ,   lowerCamelCase=30 ,   lowerCamelCase=400 ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=[0.5, 0.5, 0.5] ,   lowerCamelCase=[0.5, 0.5, 0.5] ,   lowerCamelCase=False ,   )							->Optional[Any]:



         '''simple docstring'''
         __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      )							->List[str]:



         '''simple docstring'''
         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    __UpperCAmelCase	(				)  ->			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    __UpperCAmelCase	(				)  ->			int:

   """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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=BeitImageProcessor if is_vision_available() else None




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    BeitImageProcessingTester(self      )




      @property
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         return self.image_processor_tester.prepare_image_processor_dict()




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_resize'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'size'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_center_crop'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'center_crop'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_normalize'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'image_mean'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'image_std'      )      )




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __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 ,   lowerCamelCase      )

         __a				     =    self.image_processing_class.from_dict(
             self.image_processor_dict ,   size=42 ,   crop_size=84 ,   reduce_labels=lowerCamelCase      )
         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 ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         pass




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         # 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=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   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(lowerCamelCase ,   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[str]:



         '''simple docstring'''
         # 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=lowerCamelCase ,   numpify=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   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(lowerCamelCase ,   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      )							->Optional[int]:



         '''simple docstring'''
         # 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=lowerCamelCase ,   torchify=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   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(lowerCamelCase ,   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[str]:



         '''simple docstring'''
         # 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=lowerCamelCase ,   torchify=lowerCamelCase      )
         __a				     =    []
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   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() <= 255      )

         # Test batched
         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   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() <= 255      )

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

         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   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() <= 255      )

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

         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   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() <= 255      )






      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         # 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(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertTrue(encoding['labels'].min().item() >= 0      )
         self.assertTrue(encoding['labels'].max().item() <= 150      )

         __a				     =    True
         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertTrue(encoding['labels'].min().item() >= 0      )
         self.assertTrue(encoding['labels'].max().item() <= 255      )







'''simple docstring'''



__UpperCamelCase      :       Optional[Any]      			=      [
    """DownloadConfig""",
    """DownloadManager""",
    """DownloadMode""",
    """StreamingDownloadManager""",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager