


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	):





      def __init__(		self    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Dict[str, int] = None       ,							__lowerCamelCase    :       int = 3_2       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[int, float] = 1 / 2_5_5       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[str, Any]=7       ,							__lowerCamelCase    :       List[str]=3_0       ,							__lowerCamelCase    :       str=4_0_0       ,							__lowerCamelCase    :       Any=3       ,							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  do_resize
         UpperCAmelCase      					=  size if size is not None else {"""shortest_edge""": 2_8_8}
         UpperCAmelCase      					=  size_divisor
         UpperCAmelCase      					=  do_rescale
         UpperCAmelCase      					=  rescale_factor
         UpperCAmelCase      					=  do_normalize
         UpperCAmelCase      					=  do_center_crop
         UpperCAmelCase      					=  image_mean
         UpperCAmelCase      					=  image_std
         UpperCAmelCase      					=  do_pad
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  min_resolution
         UpperCAmelCase      					=  max_resolution





      def 							_lowercase					(		self    :       Any							)     -> 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 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=False							)     -> int:






         """simple docstring"""







         if not batched:
            UpperCAmelCase      					=  self.size["""shortest_edge"""]
            UpperCAmelCase      					=  image_inputs[0]
            if isinstance(__lowerCamelCase       ,							Image.Image							):
               UpperCAmelCase					,	UpperCAmelCase      					=  image.size
            else:
               UpperCAmelCase					,	UpperCAmelCase      					=  image.shape[1], image.shape[2]
            UpperCAmelCase      					=  size / min(__lowerCamelCase       ,							__lowerCamelCase							)
            if h < w:
               UpperCAmelCase					,	UpperCAmelCase      					=  size, scale * w
            else:
               UpperCAmelCase					,	UpperCAmelCase      					=  scale * h, size

            UpperCAmelCase      					=  int((1_3_3_3 / 8_0_0) * size							)
            if max(__lowerCamelCase       ,							__lowerCamelCase							) > max_size:
               UpperCAmelCase      					=  max_size / max(__lowerCamelCase       ,							__lowerCamelCase							)
               UpperCAmelCase      					=  newh * scale
               UpperCAmelCase      					=  neww * scale

            UpperCAmelCase					,	UpperCAmelCase      					=  int(newh + 0.5							), int(neww + 0.5							)
            UpperCAmelCase					,	UpperCAmelCase      					=  (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

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

         return expected_height, expected_width






@require_torch
@require_vision
class 	__lowercase					(	__snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							BridgeTowerImageProcessor if is_vision_available() else None





      def 							_lowercase					(		self    :       Dict							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  BridgeTowerImageProcessingTester(self							)





      @property
      def 							_lowercase					(		self    :       Any							)     -> Optional[int]:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  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"""							)							)
         self.assertTrue(hasattr(__lowerCamelCase       ,							"""size_divisor"""							)							)





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






         """simple docstring"""







         pass





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






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         UpperCAmelCase      					=  prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=__lowerCamelCase							)
         for image in image_inputs:
            self.assertIsInstance(__lowerCamelCase       ,							Image.Image							)

         # Test not batched input
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

         # Test batched
         UpperCAmelCase      					=  image_processing(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase       ,							batched=__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)





      def 							_lowercase					(		self    :       Dict							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

         # Test batched
         UpperCAmelCase      					=  image_processing(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase       ,							batched=__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)





      def 							_lowercase					(		self    :       Dict							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

         # Test batched
         UpperCAmelCase      					=  image_processing(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase       ,							batched=__lowerCamelCase							)
         self.assertEqual(
             encoded_images.shape       ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,							)







class 	__lowercase					:





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






         """simple docstring"""







         UpperCAmelCase      					=  {}





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         print(self.vertex							)
         for i in self.vertex:
            print(__lowerCamelCase       ,							""" -> """       ,							""" -> """.join([str(__lowerCamelCase							) for j in self.vertex[i]]							)							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       int							)     -> None:






         """simple docstring"""







         if from_vertex in self.vertex:
            self.vertex[from_vertex].append(__lowerCamelCase							)
         else:
            # else make a new vertex
            UpperCAmelCase      					=  [to_vertex]





      def 							_lowercase					(		self    :       str							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  [False] * len(self.vertex							)

         # call the recursive helper function
         for i in range(len(self.vertex							)							):
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       list							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  True

         print(__lowerCamelCase       ,							end=""" """							)

         # Recur for all the vertices that are adjacent to this node
         for i in self.vertex:
            if not visited[i]:
               self.dfs_recursive(__lowerCamelCase       ,							__lowerCamelCase							)


if __name__ == "__main__":
 __a	 =      Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 print("""DFS:""")
 g.dfs()

 # OUTPUT:
 # 0  ->  1 -> 2
 # 1  ->  2
 # 2  ->  0 -> 3
 # 3  ->  3
 # DFS:
 #  0 1 2 3



