






'''simple docstring'''



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

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 BridgeTowerImageProcessor
class lowercase__					(    unittest.TestCase				):




      """simple docstring"""




      def __init__( self			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		int = 32     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[int, float] = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,      UpperCAmelCase			:		Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[str, Any]=7     ,      UpperCAmelCase			:		Optional[int]=30     ,      UpperCAmelCase			:		Union[str, Any]=400     ,      UpperCAmelCase			:		List[Any]=3     ,      )  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =parent
       lowercase					:   Dict				  =do_resize
       lowercase					:   str				  =size if size is not None else {'''shortest_edge''': 288}
       lowercase					:   int				  =size_divisor
       lowercase					:   Dict				  =do_rescale
       lowercase					:   int				  =rescale_factor
       lowercase					:   str				  =do_normalize
       lowercase					:   List[Any]				  =do_center_crop
       lowercase					:   Tuple				  =image_mean
       lowercase					:   int				  =image_std
       lowercase					:   Tuple				  =do_pad
       lowercase					:   List[str]				  =batch_size
       lowercase					:   Union[str, Any]				  =num_channels
       lowercase					:   List[str]				  =min_resolution
       lowercase					:   List[str]				  =max_resolution






      def 				A__      ( self			:		Tuple		)  ->  List[Any]:


       '''simple docstring'''

       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict=False		)  ->  Any:


       '''simple docstring'''

       if not batched:
        lowercase					:   Any				  =self.size['''shortest_edge''']
        lowercase					:   Union[str, Any]				  =image_inputs[0]
        if isinstance(UpperCAmelCase__     ,      Image.Image		):
         lowercase  ,		lowercase					:   Union[str, Any]				  =image.size
        else:
         lowercase  ,		lowercase					:   List[Any]				  =image.shape[1], image.shape[2]
        lowercase					:   Union[str, Any]				  =size / min(UpperCAmelCase__     ,      UpperCAmelCase__		)
        if h < w:
         lowercase  ,		lowercase					:   List[Any]				  =size, scale * w
        else:
         lowercase  ,		lowercase					:   str				  =scale * h, size

        lowercase					:   Dict				  =int((1333 / 800) * size		)
        if max(UpperCAmelCase__     ,      UpperCAmelCase__		) > max_size:
         lowercase					:   Optional[int]				  =max_size / max(UpperCAmelCase__     ,      UpperCAmelCase__		)
         lowercase					:   Optional[int]				  =newh * scale
         lowercase					:   Dict				  =neww * scale

        lowercase  ,		lowercase					:   List[str]				  =int(newh + 0.5		), int(neww + 0.5		)
        lowercase  ,		lowercase					:   str				  =(
            newh // self.size_divisor * self.size_divisor,
            neww // self.size_divisor * self.size_divisor,
        )

       else:
        lowercase					:   Tuple				  =[]
        for image in image_inputs:
         lowercase  ,		lowercase					:   Union[str, Any]				  =self.get_expected_values([image]		)
         expected_values.append((expected_height, expected_width)		)
        lowercase					:   Optional[int]				  =max(UpperCAmelCase__     ,      key=lambda UpperCAmelCase		: item[0]		)[0]
        lowercase					:   int				  =max(UpperCAmelCase__     ,      key=lambda UpperCAmelCase		: item[1]		)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class lowercase__					(    __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			BridgeTowerImageProcessor if is_vision_available() else None






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


       '''simple docstring'''

       lowercase					:   int				  =BridgeTowerImageProcessingTester(self		)






      @property
      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()






      def 				A__      ( self			:		Optional[int]		)  ->  int:


       '''simple docstring'''

       lowercase					:   str				  =self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''image_mean'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''image_std'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''do_normalize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''do_resize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''size'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase__     ,      '''size_divisor'''		)		)






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


       '''simple docstring'''

       pass






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


       '''simple docstring'''

       lowercase					:   int				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       lowercase					:   List[str]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase__		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase__     ,      Image.Image		)

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

       lowercase  ,		lowercase					:   str				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   Optional[Any]				  =image_processing(UpperCAmelCase__     ,      return_tensors='''pt'''		).pixel_values

       lowercase  ,		lowercase					:   int				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__     ,      batched=UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )






      def 				A__      ( self			:		Any		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowercase					:   Optional[Any]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase__     ,      numpify=UpperCAmelCase__		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase__     ,      np.ndarray		)

       # Test not batched input
       lowercase					:   Union[str, Any]				  =image_processing(image_inputs[0]     ,      return_tensors='''pt'''		).pixel_values

       lowercase  ,		lowercase					:   str				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   List[str]				  =image_processing(UpperCAmelCase__     ,      return_tensors='''pt'''		).pixel_values

       lowercase  ,		lowercase					:   Any				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__     ,      batched=UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )






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


       '''simple docstring'''

       lowercase					:   Any				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowercase					:   Optional[int]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase__     ,      torchify=UpperCAmelCase__		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase__     ,      torch.Tensor		)

       # Test not batched input
       lowercase					:   Optional[int]				  =image_processing(image_inputs[0]     ,      return_tensors='''pt'''		).pixel_values

       lowercase  ,		lowercase					:   Optional[int]				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   Optional[Any]				  =image_processing(UpperCAmelCase__     ,      return_tensors='''pt'''		).pixel_values

       lowercase  ,		lowercase					:   int				  =self.image_processor_tester.get_expected_values(UpperCAmelCase__     ,      batched=UpperCAmelCase__		)
       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )











'''simple docstring'''



import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


SCREAMING_SNAKE_CASE						=	parse(importlib.metadata.version('torch'))


def 							lowercase_				(    __A :       Union[str, Version]					,      __A :       str					,      __A :       str			)       ->      Union[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}'			)
 lowercase					:   Any				  =STR_OPERATION_TO_FUNC[operation]
 if isinstance(__A					,      __A			):
  lowercase					:   List[Any]				  =parse(importlib.metadata.version(__A			)			)
 return operation(__A					,      parse(__A			)			)





def 							lowercase_				(    __A :       str					,      __A :       str			)       ->      Tuple:



 """simple docstring"""




 return compare_versions(__A					,      __A					,      __A			)



