


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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[int]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Union[str, Any]=1_8     ,					_lowerCAmelCase							:							Optional[int]=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Dict=True     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							Tuple=True     ,					_lowerCAmelCase							:							Optional[Any]=None     ,					_lowerCAmelCase							:							List[str]=True     ,					_lowerCAmelCase							:							str=[0.5, 0.5, 0.5]     ,					_lowerCAmelCase							:							Union[str, Any]=[0.5, 0.5, 0.5]     ,					_lowerCAmelCase							:							Tuple=False     ,					) ->			str:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 2_0, "width": 2_0}
     snake_case_            =       crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_center_crop
     snake_case_            =       crop_size
     snake_case_            =       do_normalize
     snake_case_            =       image_mean
     snake_case_            =       image_std
     snake_case_            =       do_reduce_labels



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			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       _lowerCAmelCase    (				)->Union[str, Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_ade20k"		,    split="test"				)

   snake_case_            =       Image.open(dataset[0]["file"]				)
   snake_case_            =       Image.open(dataset[1]["file"]				)

   return image, map



def       _lowerCAmelCase    (				)->List[str]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_ade20k"		,    split="test"				)

   snake_case_            =       Image.open(ds[0]["file"]				)
   snake_case_            =       Image.open(ds[1]["file"]				)
   snake_case_            =       Image.open(ds[2]["file"]				)
   snake_case_            =       Image.open(ds[3]["file"]				)

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



@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							BeitImageProcessor if is_vision_available() else None



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




     """simple docstring"""
     snake_case_            =       BeitImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Tuple:




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



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 2_0, "width": 2_0}	)
     self.assertEqual(image_processor.crop_size     ,					{"height": 1_8, "width": 1_8}	)
     self.assertEqual(image_processor.do_reduce_labels     ,					_lowerCAmelCase	)

     snake_case_            =       self.image_processing_class.from_dict(
         self.image_processor_dict     ,					size=4_2     ,					crop_size=8_4     ,					reduce_labels=_lowerCAmelCase	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)
     self.assertEqual(image_processor.crop_size     ,					{"height": 8_4, "width": 8_4}	)
     self.assertEqual(image_processor.do_reduce_labels     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			List[Any]:




     """simple docstring"""
     pass



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     # Initialize image_processing
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     snake_case_            =       prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_lowerCAmelCase	)
     for image in image_inputs:
        self.assertIsInstance(_lowerCAmelCase     ,					Image.Image	)

     # Test not batched input
     snake_case_            =       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
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Any	) ->			Optional[Any]:




     """simple docstring"""
     # Initialize image_processing
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     snake_case_            =       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
     snake_case_            =       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
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     # Initialize image_processing
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     snake_case_            =       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
     snake_case_            =       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
     snake_case_            =       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        lowerCAmelCase__	(  self							:							str	) ->			List[str]:




     """simple docstring"""
     # Initialize image_processing
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     snake_case_            =       prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_lowerCAmelCase     ,					torchify=_lowerCAmelCase	)
     snake_case_            =       []
     for image in image_inputs:
        self.assertIsInstance(_lowerCAmelCase     ,					torch.Tensor	)
        maps.append(torch.zeros(image.shape[-2:]	).long()	)

     # Test not batched input
     snake_case_            =       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_5_5	)

     # Test batched
     snake_case_            =       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() <= 2_5_5	)

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

     snake_case_            =       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() <= 2_5_5	)

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

     snake_case_            =       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() <= 2_5_5	)



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




     """simple docstring"""
     # Initialize image_processing
     snake_case_            =       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
     snake_case_     ,     snake_case_            =       prepare_semantic_single_inputs()
     snake_case_            =       image_processing(_lowerCAmelCase     ,					_lowerCAmelCase     ,					return_tensors="pt"	)
     self.assertTrue(encoding["labels"].min().item() >= 0	)
     self.assertTrue(encoding["labels"].max().item() <= 1_5_0	)

     snake_case_            =       True
     snake_case_            =       image_processing(_lowerCAmelCase     ,					_lowerCAmelCase     ,					return_tensors="pt"	)
     self.assertTrue(encoding["labels"].min().item() >= 0	)
     self.assertTrue(encoding["labels"].max().item() <= 2_5_5	)








import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  parse(importlib.metadata.version('''torch'''))



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Version]		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   if operation not in STR_OPERATION_TO_FUNC.keys():
      raise ValueError(F'''`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()				)}, received {operation}'''				)
   snake_case_            =       STR_OPERATION_TO_FUNC[operation]
   if isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      snake_case_            =       parse(importlib.metadata.version(lowerCAmelCase_				)				)
   return operation(lowerCAmelCase_		,    parse(lowerCAmelCase_				)				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Any:
   '''simple docstring'''






   return compare_versions(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)




