
'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def __init__(		self      :     str							,_a      :     int							,_a      :     Union[str, Any]=7							,_a      :     Union[str, Any]=3							,_a      :     Dict=30							,_a      :     Union[str, Any]=400							,_a      :     str=True							,_a      :     Tuple=None							,_a      :     List[str]=True							,_a      :     Optional[int]=[0.5, 0.5, 0.5]							,_a      :     Union[str, Any]=[0.5, 0.5, 0.5]							,_a      :     Optional[int]=True							,_a      :     Optional[int]=1 / 255							,_a      :     int=True							,):


     '''simple docstring'''






     _a				:	List[str]			=   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
     _a				:	List[str]			=   parent
     _a				:	Any			=   batch_size
     _a				:	str			=   num_channels
     _a				:	Optional[Any]			=   min_resolution
     _a				:	Any			=   max_resolution
     _a				:	Dict			=   do_resize
     _a				:	int			=   size
     _a				:	List[Any]			=   do_normalize
     _a				:	Union[str, Any]			=   image_mean
     _a				:	Union[str, Any]			=   image_std
     _a				:	Union[str, Any]			=   do_rescale
     _a				:	Tuple			=   rescale_factor
     _a				:	Any			=   do_pad


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }


  def   __lowercase		(		self      :     Any							,_a      :     Optional[Any]							,_a      :     Dict=False       ):


     '''simple docstring'''






     if not batched:
        _a				:	Optional[Any]			=   image_inputs[0]
        if isinstance(_a							,Image.Image       ):
           _a,  _a				:	List[str]			=   image.size
        else:
           _a,  _a				:	List[Any]			=   image.shape[1], image.shape[2]
        if w < h:
           _a				:	List[Any]			=   int(self.size['shortest_edge'] * h / w       )
           _a				:	Any			=   self.size['shortest_edge']
        elif w > h:
           _a				:	Dict			=   self.size['shortest_edge']
           _a				:	str			=   int(self.size['shortest_edge'] * w / h       )
        else:
           _a				:	str			=   self.size['shortest_edge']
           _a				:	Optional[int]			=   self.size['shortest_edge']

     else:
        _a				:	Optional[int]			=   []
        for image in image_inputs:
           _a,  _a				:	Dict			=   self.get_expected_values([image]       )
           expected_values.append((expected_height, expected_width)       )
        _a				:	int			=   max(_a							,key=lambda _a       : item[0]       )[0]
        _a				:	Dict			=   max(_a							,key=lambda _a       : item[1]       )[1]

     return expected_height, expected_width







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





  """simple docstring"""

  __UpperCAmelCase				:				List[str]   =					ConditionalDetrImageProcessor if is_vision_available() else None


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   ConditionalDetrImageProcessingTester(self       )


  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     return self.image_processor_tester.prepare_image_processor_dict()


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Any			=   self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(_a							,'image_mean'       )       )
     self.assertTrue(hasattr(_a							,'image_std'       )       )
     self.assertTrue(hasattr(_a							,'do_normalize'       )       )
     self.assertTrue(hasattr(_a							,'do_resize'       )       )
     self.assertTrue(hasattr(_a							,'size'       )       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size							,{'shortest_edge': 18, 'longest_edge': 1333}       )
     self.assertEqual(image_processor.do_pad							,_a       )

     _a				:	Optional[int]			=   self.image_processing_class.from_dict(
         self.image_processor_dict							,size=42							,max_size=84							,pad_and_return_pixel_mask=_a       )
     self.assertEqual(image_processor.size							,{'shortest_edge': 42, 'longest_edge': 84}       )
     self.assertEqual(image_processor.do_pad							,_a       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






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

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

     _a,  _a				:	List[str]			=   self.image_processor_tester.get_expected_values(_a       )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _a,  _a				:	str			=   self.image_processor_tester.get_expected_values(_a							,batched=_a       )

     _a				:	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,
             expected_height,
             expected_width,
         )							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	str			=   self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     _a				:	Union[str, 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
     _a				:	Any			=   image_processing(image_inputs[0]							,return_tensors='pt'       ).pixel_values

     _a,  _a				:	Optional[Any]			=   self.image_processor_tester.get_expected_values(_a       )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _a				:	List[Any]			=   image_processing(_a							,return_tensors='pt'       ).pixel_values

     _a,  _a				:	List[Any]			=   self.image_processor_tester.get_expected_values(_a							,batched=_a       )

     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,)


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     _a				:	int			=   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
     _a				:	List[str]			=   image_processing(image_inputs[0]							,return_tensors='pt'       ).pixel_values

     _a,  _a				:	str			=   self.image_processor_tester.get_expected_values(_a       )

     self.assertEqual(
         encoded_images.shape							,(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,)

     # Test batched
     _a				:	Tuple			=   image_processing(_a							,return_tensors='pt'       ).pixel_values

     _a,  _a				:	Optional[int]			=   self.image_processor_tester.get_expected_values(_a							,batched=_a       )

     self.assertEqual(
         encoded_images.shape							,(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,)


  @slow
  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
     with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'							,'r'       ) as f:
        _a				:	List[str]			=   json.loads(f.read()       )

     _a				:	Optional[Any]			=   {'image_id': 3_9769, 'annotations': target}

     # encode them
     _a				:	Optional[int]			=   ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'       )
     _a				:	str			=   image_processing(images=_a							,annotations=_a							,return_tensors='pt'       )

     # verify pixel values
     _a				:	Any			=   torch.Size([1, 3, 800, 1066]       )
     self.assertEqual(encoding['pixel_values'].shape							,_a       )

     _a				:	Dict			=   torch.tensor([0.2796, 0.3138, 0.3481]       )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,_a							,atol=1E-4       )       )

     # verify area
     _a				:	Dict			=   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,_a       )       )
     # verify boxes
     _a				:	Union[str, Any]			=   torch.Size([6, 4]       )
     self.assertEqual(encoding['labels'][0]['boxes'].shape							,_a       )
     _a				:	Optional[Any]			=   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,_a							,atol=1E-3       )       )
     # verify image_id
     _a				:	str			=   torch.tensor([3_9769]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,_a       )       )
     # verify is_crowd
     _a				:	Dict			=   torch.tensor([0, 0, 0, 0, 0, 0]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,_a       )       )
     # verify class_labels
     _a				:	Optional[int]			=   torch.tensor([75, 75, 63, 65, 17, 17]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,_a       )       )
     # verify orig_size
     _a				:	List[Any]			=   torch.tensor([480, 640]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,_a       )       )
     # verify size
     _a				:	Tuple			=   torch.tensor([800, 1066]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,_a       )       )


  @slow
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
     with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'							,'r'       ) as f:
        _a				:	str			=   json.loads(f.read()       )

     _a				:	str			=   {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

     _a				:	Optional[int]			=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

     # encode them
     _a				:	int			=   ConditionalDetrImageProcessor(format='coco_panoptic'       )
     _a				:	int			=   image_processing(images=_a							,annotations=_a							,masks_path=_a							,return_tensors='pt'       )

     # verify pixel values
     _a				:	Dict			=   torch.Size([1, 3, 800, 1066]       )
     self.assertEqual(encoding['pixel_values'].shape							,_a       )

     _a				:	Union[str, Any]			=   torch.tensor([0.2796, 0.3138, 0.3481]       )
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]							,_a							,atol=1E-4       )       )

     # verify area
     _a				:	int			=   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']							,_a       )       )
     # verify boxes
     _a				:	str			=   torch.Size([6, 4]       )
     self.assertEqual(encoding['labels'][0]['boxes'].shape							,_a       )
     _a				:	List[Any]			=   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]							,_a							,atol=1E-3       )       )
     # verify image_id
     _a				:	Dict			=   torch.tensor([3_9769]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']							,_a       )       )
     # verify is_crowd
     _a				:	int			=   torch.tensor([0, 0, 0, 0, 0, 0]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']							,_a       )       )
     # verify class_labels
     _a				:	Any			=   torch.tensor([17, 17, 63, 75, 75, 93]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']							,_a       )       )
     # verify masks
     _a				:	Tuple			=   82_2873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()							,_a       )
     # verify orig_size
     _a				:	Optional[Any]			=   torch.tensor([480, 640]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']							,_a       )       )
     # verify size
     _a				:	Dict			=   torch.tensor([800, 1066]       )
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']							,_a       )       )





'''simple docstring'''







from __future__ import annotations

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



