

import unittest

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 ConvNextImageProcessor
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       def __init__(						self       :						str    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						Optional[int]=7    ,		lowerCAmelCase__       :						Dict=3    ,		lowerCAmelCase__       :						Tuple=18    ,		lowerCAmelCase__       :						Optional[Any]=30    ,		lowerCAmelCase__       :						Any=400    ,		lowerCAmelCase__       :						str=True    ,		lowerCAmelCase__       :						int=None    ,		lowerCAmelCase__       :						List[str]=0.875    ,		lowerCAmelCase__       :						Union[str, Any]=True    ,		lowerCAmelCase__       :						str=[0.5, 0.5, 0.5]    ,		lowerCAmelCase__       :						List[str]=[0.5, 0.5, 0.5]    ,		)			->    Any:
             snake_case__					=   size if size is not None else {"""shortest_edge""": 20}
             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__					=   crop_pct
             snake_case__					=   do_normalize
             snake_case__					=   image_mean
             snake_case__					=   image_std

       def UpperCAmelCase_						(						self       :						int							)			->    str:
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_pct": self.crop_pct,
             }



@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE__     (						__a ,	unittest.TestCase		):





       '''simple docstring'''




       UpperCamelCase__ :     List[Any]				    =       ConvNextImageProcessor if is_vision_available() else None

       def UpperCAmelCase_						(						self       :						int							)			->    Optional[Any]:
             snake_case__					=   ConvNextImageProcessingTester(self							)

       @property
       def UpperCAmelCase_						(						self       :						Any							)			->    Union[str, Any]:
             return self.image_processor_tester.prepare_image_processor_dict()

       def UpperCAmelCase_						(						self       :						Dict							)			->    Any:
             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__    ,		"""crop_pct"""							)							)
             self.assertTrue(hasattr(lowerCAmelCase__    ,		"""do_normalize"""							)							)
             self.assertTrue(hasattr(lowerCAmelCase__    ,		"""image_mean"""							)							)
             self.assertTrue(hasattr(lowerCAmelCase__    ,		"""image_std"""							)							)

       def UpperCAmelCase_						(						self       :						str							)			->    Dict:
             snake_case__					=   self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size    ,		{"""shortest_edge""": 20}							)

             snake_case__					=   self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42							)
             self.assertEqual(image_processor.size    ,		{"""shortest_edge""": 42}							)

       def UpperCAmelCase_						(						self       :						str							)			->    Optional[Any]:
             pass

       def UpperCAmelCase_						(						self       :						Optional[Any]							)			->    Any:
             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)

             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)

       def UpperCAmelCase_						(						self       :						int							)			->    Any:
             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)

             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)

       def UpperCAmelCase_						(						self       :						str							)			->    List[str]:
             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)

             # 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.size["""shortest_edge"""],
                     self.image_processor_tester.size["""shortest_edge"""],
                 )    ,		)









class     SCREAMING_SNAKE_CASE__     :





       '''simple docstring'''




       def __init__(						self       :						Tuple							)			->    None:
             # numbermap keys are the number and its values are lists of indexes sorted
             # in ascending order
             snake_case__					=   {}
             # indexmap keys are an index and it's values are the number at that index
             snake_case__					=   {}

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						list | str | range    ,		lowerCAmelCase__       :						int							)			->    list[int]:
             if isinstance(lowerCAmelCase__    ,		(range, str)							):
                   snake_case__					=   list(lowerCAmelCase__							)
             elif not isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   raise TypeError(
                       """binary_search_delete() only accepts either a list, range or str"""							)

             snake_case__					=   0
             snake_case__					=   len(lowerCAmelCase__							) - 1

             while low <= high:
                   snake_case__					=   (low + high) // 2
                   if array[mid] == item:
                         array.pop(lowerCAmelCase__							)
                         return array
                   elif array[mid] < item:
                         snake_case__					=   mid + 1
                   else:
                         snake_case__					=   mid - 1
             raise ValueError(
                 """Either the item is not in the array or the array was unsorted"""							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						list | str | range    ,		lowerCAmelCase__       :						int							)			->    list[int]:
             if isinstance(lowerCAmelCase__    ,		(range, str)							):
                   snake_case__					=   list(lowerCAmelCase__							)
             elif not isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   raise TypeError(
                       """binary_search_insert() only accepts either a list, range or str"""							)

             snake_case__					=   0
             snake_case__					=   len(lowerCAmelCase__							) - 1

             while low <= high:
                   snake_case__					=   (low + high) // 2
                   if array[mid] == index:
                         # If the item already exists in the array,
                         # insert it after the existing item
                         array.insert(mid + 1    ,		lowerCAmelCase__							)
                         return array
                   elif array[mid] < index:
                         snake_case__					=   mid + 1
                   else:
                         snake_case__					=   mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
             array.insert(lowerCAmelCase__    ,		lowerCAmelCase__							)
             return array

       def UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int							)			->    None:
             # Remove previous index
             if index in self.indexmap:
                   snake_case__					=   self.indexmap[index]
                   if len(self.numbermap[n]							) == 1:
                         del self.numbermap[n]
                   else:
                         snake_case__					=   self.binary_search_delete(self.numbermap[n]    ,		lowerCAmelCase__							)

        # Set new index
             snake_case__					=   number

             # Number not seen before or empty so insert number value
             if number not in self.numbermap:
                   snake_case__					=   [index]

             # Here we need to perform a binary search insertion in order to insert
             # The item in the correct place
             else:
                   snake_case__					=   self.binary_search_insert(
                       self.numbermap[number]    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						int							)			->    int:
             return self.numbermap.get(lowerCAmelCase__    ,		[-1]							)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






