



'''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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_=7  ,a_=3  ,a_=18  ,a_=30  ,a_=400  ,a_=True  ,a_=None  ,a_=True  ,a_=None  ,a_=True  ,a_=[0.5, 0.5, 0.5]  ,a_=[0.5, 0.5, 0.5]  ,a_=False  ,)		->   List[str]:
        _UpperCAmelCase  :						List[Any]			     = size if size is not None else {"""height""": 20, """width""": 20}
        _UpperCAmelCase  :						List[Any]			     = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
        _UpperCAmelCase  :						Union[str, Any]			     = parent
        _UpperCAmelCase  :						str			     = batch_size
        _UpperCAmelCase  :						Union[str, Any]			     = num_channels
        _UpperCAmelCase  :						Optional[int]			     = image_size
        _UpperCAmelCase  :						Optional[Any]			     = min_resolution
        _UpperCAmelCase  :						Tuple			     = max_resolution
        _UpperCAmelCase  :						Any			     = do_resize
        _UpperCAmelCase  :						Dict			     = size
        _UpperCAmelCase  :						List[str]			     = do_center_crop
        _UpperCAmelCase  :						Optional[Any]			     = crop_size
        _UpperCAmelCase  :						Tuple			     = do_normalize
        _UpperCAmelCase  :						Optional[Any]			     = image_mean
        _UpperCAmelCase  :						List[str]			     = image_std
        _UpperCAmelCase  :						Any			     = do_reduce_labels







 def _snake_case (							self							)		->   List[Any]:
        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     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,   split="""test"""		)

       _UpperCAmelCase  :						str			     = Image.open(dataset[0]["""file"""]		)
       _UpperCAmelCase  :						Optional[int]			     = Image.open(dataset[1]["""file"""]		)

       return image, map




def     snake_case_					(     )->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,   split="""test"""		)

       _UpperCAmelCase  :						Dict			     = Image.open(ds[0]["""file"""]		)
       _UpperCAmelCase  :						List[str]			     = Image.open(ds[1]["""file"""]		)
       _UpperCAmelCase  :						Optional[Any]			     = Image.open(ds[2]["""file"""]		)
       _UpperCAmelCase  :						Optional[Any]			     = Image.open(ds[3]["""file"""]		)

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



@require_torch
@require_vision
class 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  BeitImageProcessor if is_vision_available() else None







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Dict			     = BeitImageProcessingTester(self							)







 @property
 def _snake_case (							self							)		->   Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Dict			     = self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(a_  ,"""do_resize"""							)							)
        self.assertTrue(hasattr(a_  ,"""size"""							)							)
        self.assertTrue(hasattr(a_  ,"""do_center_crop"""							)							)
        self.assertTrue(hasattr(a_  ,"""center_crop"""							)							)
        self.assertTrue(hasattr(a_  ,"""do_normalize"""							)							)
        self.assertTrue(hasattr(a_  ,"""image_mean"""							)							)
        self.assertTrue(hasattr(a_  ,"""image_std"""							)							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						str			     = 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  ,a_							)

        _UpperCAmelCase  :						Optional[int]			     = self.image_processing_class.from_dict(
            self.image_processor_dict  ,size=42  ,crop_size=84  ,reduce_labels=a_							)
        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  ,a_							)







 def _snake_case (							self							)		->   Dict:
        pass







 def _snake_case (							self							)		->   Optional[Any]:
        # Initialize image_processing
        _UpperCAmelCase  :						Optional[Any]			     = self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _UpperCAmelCase  :						List[Any]			     = prepare_image_inputs(self.image_processor_tester  ,equal_resolution=a_							)
        for image in image_inputs:
               self.assertIsInstance(a_  ,Image.Image							)

        # Test not batched input
        _UpperCAmelCase  :						str			     = 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
        _UpperCAmelCase  :						Optional[Any]			     = image_processing(a_  ,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 _snake_case (							self							)		->   Optional[int]:
        # Initialize image_processing
        _UpperCAmelCase  :						str			     = self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        _UpperCAmelCase  :						Optional[Any]			     = prepare_image_inputs(self.image_processor_tester  ,equal_resolution=a_  ,numpify=a_							)
        for image in image_inputs:
               self.assertIsInstance(a_  ,np.ndarray							)

        # Test not batched input
        _UpperCAmelCase  :						Dict			     = 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
        _UpperCAmelCase  :						int			     = image_processing(a_  ,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 _snake_case (							self							)		->   List[str]:
        # Initialize image_processing
        _UpperCAmelCase  :						List[Any]			     = self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _UpperCAmelCase  :						List[str]			     = prepare_image_inputs(self.image_processor_tester  ,equal_resolution=a_  ,torchify=a_							)
        for image in image_inputs:
               self.assertIsInstance(a_  ,torch.Tensor							)

        # Test not batched input
        _UpperCAmelCase  :						Optional[Any]			     = 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
        _UpperCAmelCase  :						Optional[int]			     = image_processing(a_  ,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 _snake_case (							self							)		->   List[str]:
        # Initialize image_processing
        _UpperCAmelCase  :						Optional[Any]			     = self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _UpperCAmelCase  :						Union[str, Any]			     = prepare_image_inputs(self.image_processor_tester  ,equal_resolution=a_  ,torchify=a_							)
        _UpperCAmelCase  :						Dict			     = []
        for image in image_inputs:
               self.assertIsInstance(a_  ,torch.Tensor							)
               maps.append(torch.zeros(image.shape[-2:]							).long()							)

        # Test not batched input
        _UpperCAmelCase  :						Optional[int]			     = 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
        _UpperCAmelCase  :						str			     = image_processing(a_  ,a_  ,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)
        _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = prepare_semantic_single_inputs()

        _UpperCAmelCase  :						Optional[Any]			     = image_processing(a_  ,a_  ,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)
        _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = prepare_semantic_batch_inputs()

        _UpperCAmelCase  :						int			     = image_processing(a_  ,a_  ,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 _snake_case (							self							)		->   Dict:
        # Initialize image_processing
        _UpperCAmelCase  :						Union[str, Any]			     = 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
        _UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = prepare_semantic_single_inputs()
        _UpperCAmelCase  :						Dict			     = image_processing(a_  ,a_  ,return_tensors="""pt"""							)
        self.assertTrue(encoding["""labels"""].min().item() >= 0							)
        self.assertTrue(encoding["""labels"""].max().item() <= 150							)

        _UpperCAmelCase  :						Tuple			     = True
        _UpperCAmelCase  :						Optional[int]			     = image_processing(a_  ,a_  ,return_tensors="""pt"""							)
        self.assertTrue(encoding["""labels"""].min().item() >= 0							)
        self.assertTrue(encoding["""labels"""].max().item() <= 255							)








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



