






"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

lowercase_				     =     TypeVar('T')


class 					snake_case		(							Generic[T]						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     T  ):

     '''simple docstring'''
     __A									= data
     __A									= self
     __A									= 0





class 					snake_case		(							Generic[T]						):


   '''simple docstring'''

   def __init__(	self :     Dict  ):

     '''simple docstring'''
     # map from node name to the node object
     __A									= {}


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     T  ):

     '''simple docstring'''
     # create a new set with x as its member
     __A									= DisjointSetTreeNode(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     T  ):

     '''simple docstring'''
     # find the set x belongs to (with path-compression)
     __A									= self.map[data]
     if elem_ref != elem_ref.parent:
       __A									= self.find_set(elem_ref.parent.data  )
     return elem_ref.parent


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     DisjointSetTreeNode[T],       _lowerCamelCase :     DisjointSetTreeNode[T]  ):

     '''simple docstring'''
     # helper function for union operation
     if nodea.rank > nodea.rank:
       __A									= nodea
     else:
       __A									= nodea
       if nodea.rank == nodea.rank:
         nodea.rank += 1






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     T,       _lowerCamelCase :     T  ):

     '''simple docstring'''
     # merge 2 disjoint sets
     self.link(self.find_set(_lowerCamelCase  ),       self.find_set(_lowerCamelCase  )  )





class 					snake_case		(							Generic[T]						):


   '''simple docstring'''

   def __init__(	self :     Dict  ):

     '''simple docstring'''
     # connections: map from the node to the neighbouring nodes (with weights)
     __A									= {}


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     T  ):

     '''simple docstring'''
     # add a node ONLY if its not present in the graph
     if node not in self.connections:
       __A									= {}


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     T,       _lowerCamelCase :     T,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     # add an edge with the given weight
     self.add_node(_lowerCamelCase  )
     self.add_node(_lowerCamelCase  )
     __A									= weight
     __A									= weight






   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= []
     __A									= set()
     for start in self.connections:
       for end in self.connections[start]:
         if (start, end) not in seen:
           seen.add((end, start)  )
           edges.append((start, end, self.connections[start][end])  )
     edges.sort(key=lambda _lowerCamelCase  : x[2]  )

     # creating the disjoint set
     __A									= DisjointSetTree[T]()
     for node in self.connections:
       disjoint_set.make_set(_lowerCamelCase  )

     # MST generation
     __A									= 0
     __A									= 0
     __A									= GraphUndirectedWeighted[T]()
     while num_edges < len(self.connections  ) - 1:
       __A			,    __A			,    __A									= edges[index]
       index += 1
       __A									= disjoint_set.find_set(_lowerCamelCase  )
       __A									= disjoint_set.find_set(_lowerCamelCase  )
       if parent_u != parent_v:
         num_edges += 1
         graph.add_edge(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  )
         disjoint_set.union(_lowerCamelCase,       _lowerCamelCase  )
     return graph












"""simple docstring"""


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 ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''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,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




