



import random
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():
   import PIL

   from transformers import FlavaImageProcessor
   from transformers.image_utils import PILImageResampling
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
else:
   __lowerCamelCase								=      __lowerCamelCase								=      __lowerCamelCase								=      __lowerCamelCase								=      None


class 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase=7  ,lowercase=3  ,lowercase=30  ,lowercase=400  ,lowercase=True  ,lowercase=None  ,lowercase=True  ,lowercase=None  ,lowercase=None  ,lowercase=True  ,lowercase=1 / 255  ,lowercase=True  ,lowercase=FLAVA_IMAGE_MEAN  ,lowercase=FLAVA_IMAGE_STD  ,lowercase=14  ,lowercase=75  ,lowercase=None  ,lowercase=16  ,lowercase=0.3  ,lowercase=None  ,lowercase=True  ,lowercase=None  ,lowercase=None  ,lowercase=True  ,lowercase=None  ,lowercase=True  ,lowercase=True  ,lowercase=FLAVA_CODEBOOK_MEAN  ,lowercase=FLAVA_CODEBOOK_STD  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Dict					   =		size if size is not None else {"height": 224, "width": 224}
             UpperCAmelCase_	:						Tuple					   =		crop_size if crop_size is not None else {"height": 224, "width": 224}
             UpperCAmelCase_	:						Any					   =		codebook_size if codebook_size is not None else {"height": 112, "width": 112}
             UpperCAmelCase_	:						List[str]					   =		codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

             UpperCAmelCase_	:						Union[str, Any]					   =		parent
             UpperCAmelCase_	:						Any					   =		batch_size
             UpperCAmelCase_	:						List[Any]					   =		num_channels
             UpperCAmelCase_	:						List[str]					   =		do_resize
             UpperCAmelCase_	:						Dict					   =		do_rescale
             UpperCAmelCase_	:						int					   =		rescale_factor
             UpperCAmelCase_	:						Optional[Any]					   =		min_resolution
             UpperCAmelCase_	:						Any					   =		max_resolution
             UpperCAmelCase_	:						List[str]					   =		size
             UpperCAmelCase_	:						int					   =		resample if resample is not None else PILImageResampling.BICUBIC
             UpperCAmelCase_	:						List[str]					   =		do_normalize
             UpperCAmelCase_	:						str					   =		image_mean
             UpperCAmelCase_	:						Union[str, Any]					   =		image_std
             UpperCAmelCase_	:						List[str]					   =		do_center_crop
             UpperCAmelCase_	:						Any					   =		crop_size

             UpperCAmelCase_	:						List[str]					   =		input_size_patches
             UpperCAmelCase_	:						Optional[Any]					   =		total_mask_patches
             UpperCAmelCase_	:						str					   =		mask_group_max_patches
             UpperCAmelCase_	:						int					   =		mask_group_min_patches
             UpperCAmelCase_	:						Dict					   =		mask_group_min_aspect_ratio
             UpperCAmelCase_	:						Union[str, Any]					   =		mask_group_max_aspect_ratio

             UpperCAmelCase_	:						Dict					   =		codebook_do_resize
             UpperCAmelCase_	:						int					   =		codebook_size
             UpperCAmelCase_	:						Any					   =		codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
             UpperCAmelCase_	:						Any					   =		codebook_do_center_crop
             UpperCAmelCase_	:						List[str]					   =		codebook_crop_size
             UpperCAmelCase_	:						Optional[Any]					   =		codebook_do_map_pixels
             UpperCAmelCase_	:						List[str]					   =		codebook_do_normalize
             UpperCAmelCase_	:						int					   =		codebook_image_mean
             UpperCAmelCase_	:						Optional[Any]					   =		codebook_image_std



       def 			A_ (  self):

             """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,
                 "resample": self.resample,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "input_size_patches": self.input_size_patches,
                 "total_mask_patches": self.total_mask_patches,
                 "mask_group_max_patches": self.mask_group_max_patches,
                 "mask_group_min_patches": self.mask_group_min_patches,
                 "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
                 "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
                 "codebook_do_resize": self.codebook_do_resize,
                 "codebook_size": self.codebook_size,
                 "codebook_resample": self.codebook_resample,
                 "codebook_do_center_crop": self.codebook_do_center_crop,
                 "codebook_crop_size": self.codebook_crop_size,
                 "codebook_do_map_pixels": self.codebook_do_map_pixels,
                 "codebook_do_normalize": self.codebook_do_normalize,
                 "codebook_image_mean": self.codebook_image_mean,
                 "codebook_image_std": self.codebook_image_std,
             }



       def 			A_ (  self):

             """simple docstring"""





             return (self.size["height"], self.size["width"])



       def 			A_ (  self):

             """simple docstring"""





             return (
                 (self.input_size_patches, self.input_size_patches)
                 if not isinstance(self.input_size_patches  ,lowercase)
                 else self.input_size_patches
             )



       def 			A_ (  self):

             """simple docstring"""





             return (self.codebook_size["height"], self.codebook_size["width"])


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




       """simple docstring"""






       _lowerCamelCase           =					FlavaImageProcessor if is_vision_available() else None
       _lowerCamelCase           =					None



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						str					   =		FlavaImageProcessingTester(self)



       @property
       def 			A_ (  self):

             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(lowercase  ,"image_mean"))
             self.assertTrue(hasattr(lowercase  ,"image_std"))
             self.assertTrue(hasattr(lowercase  ,"do_normalize"))
             self.assertTrue(hasattr(lowercase  ,"do_resize"))
             self.assertTrue(hasattr(lowercase  ,"resample"))
             self.assertTrue(hasattr(lowercase  ,"crop_size"))
             self.assertTrue(hasattr(lowercase  ,"do_center_crop"))
             self.assertTrue(hasattr(lowercase  ,"do_rescale"))
             self.assertTrue(hasattr(lowercase  ,"rescale_factor"))
             self.assertTrue(hasattr(lowercase  ,"masking_generator"))
             self.assertTrue(hasattr(lowercase  ,"codebook_do_resize"))
             self.assertTrue(hasattr(lowercase  ,"codebook_size"))
             self.assertTrue(hasattr(lowercase  ,"codebook_resample"))
             self.assertTrue(hasattr(lowercase  ,"codebook_do_center_crop"))
             self.assertTrue(hasattr(lowercase  ,"codebook_crop_size"))
             self.assertTrue(hasattr(lowercase  ,"codebook_do_map_pixels"))
             self.assertTrue(hasattr(lowercase  ,"codebook_do_normalize"))
             self.assertTrue(hasattr(lowercase  ,"codebook_image_mean"))
             self.assertTrue(hasattr(lowercase  ,"codebook_image_std"))



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		self.image_processing_class.from_dict(self.image_processor_dict)
             self.assertEqual(image_processor.size  ,{"height": 224, "width": 224})
             self.assertEqual(image_processor.crop_size  ,{"height": 224, "width": 224})
             self.assertEqual(image_processor.codebook_size  ,{"height": 112, "width": 112})
             self.assertEqual(image_processor.codebook_crop_size  ,{"height": 112, "width": 112})

             UpperCAmelCase_	:						Optional[Any]					   =		self.image_processing_class.from_dict(
                 self.image_processor_dict  ,size=42  ,crop_size=84  ,codebook_size=33  ,codebook_crop_size=66)
             self.assertEqual(image_processor.size  ,{"height": 42, "width": 42})
             self.assertEqual(image_processor.crop_size  ,{"height": 84, "width": 84})
             self.assertEqual(image_processor.codebook_size  ,{"height": 33, "width": 33})
             self.assertEqual(image_processor.codebook_crop_size  ,{"height": 66, "width": 66})



       def 			A_ (  self):

             """simple docstring"""





             pass



       def 			A_ (  self):

             """simple docstring"""





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

             # Test not batched input
             UpperCAmelCase_	:						Union[str, Any]					   =		image_processing(image_inputs[0]  ,return_tensors="pt")

             # Test no bool masked pos
             self.assertFalse("bool_masked_pos" in encoded_images)

             UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[Any]					   =		self.image_processor_tester.get_expected_image_size()

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

             # Test batched
             UpperCAmelCase_	:						str					   =		image_processing(lowercase  ,return_tensors="pt")
             UpperCAmelCase_   ,   UpperCAmelCase_	:						str					   =		self.image_processor_tester.get_expected_image_size()

             # Test no bool masked pos
             self.assertFalse("bool_masked_pos" in encoded_images)

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



       def 			A_ (  self  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						List[str]					   =		self.image_processing_class(**self.image_processor_dict)
             # create random tensors
             UpperCAmelCase_	:						List[str]					   =		prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase  ,**lowercase)
             for image in image_inputs:
                   self.assertIsInstance(lowercase  ,lowercase)

             # Test not batched input
             UpperCAmelCase_	:						Optional[Any]					   =		image_processing(image_inputs[0]  ,return_tensors="pt")

             UpperCAmelCase_   ,   UpperCAmelCase_	:						Tuple					   =		self.image_processor_tester.get_expected_image_size()
             self.assertEqual(
                 encoded_images.pixel_values.shape  ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,)

             UpperCAmelCase_	:						Optional[Any]					   =		image_processing(lowercase  ,return_image_mask=lowercase  ,return_tensors="pt")

             UpperCAmelCase_   ,   UpperCAmelCase_	:						str					   =		self.image_processor_tester.get_expected_image_size()
             self.assertEqual(
                 encoded_images.pixel_values.shape  ,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,)

             UpperCAmelCase_   ,   UpperCAmelCase_	:						int					   =		self.image_processor_tester.get_expected_mask_size()
             self.assertEqual(
                 encoded_images.bool_masked_pos.shape  ,(
                     self.image_processor_tester.batch_size,
                     expected_height,
                     expected_width,
                 )  ,)

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

             UpperCAmelCase_   ,   UpperCAmelCase_	:						Any					   =		self.image_processor_tester.get_expected_image_size()
             self.assertEqual(
                 encoded_images.shape  ,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,)

             # Test masking
             UpperCAmelCase_	:						Tuple					   =		image_processing(lowercase  ,return_image_mask=lowercase  ,return_tensors="pt")

             UpperCAmelCase_   ,   UpperCAmelCase_	:						Optional[Any]					   =		self.image_processor_tester.get_expected_image_size()
             self.assertEqual(
                 encoded_images.pixel_values.shape  ,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,)

             UpperCAmelCase_   ,   UpperCAmelCase_	:						str					   =		self.image_processor_tester.get_expected_mask_size()
             self.assertEqual(
                 encoded_images.bool_masked_pos.shape  ,(
                     self.image_processor_tester.batch_size,
                     expected_height,
                     expected_width,
                 )  ,)



       def 			A_ (  self):

             """simple docstring"""





             self._test_call_framework(np.ndarray  ,prepare_kwargs={"numpify": True})



       def 			A_ (  self):

             """simple docstring"""





             self._test_call_framework(torch.Tensor  ,prepare_kwargs={"torchify": True})



       def 			A_ (  self):

             """simple docstring"""





             random.seed(1234)
             UpperCAmelCase_	:						List[str]					   =		self.image_processing_class(**self.image_processor_dict)
             UpperCAmelCase_	:						Optional[int]					   =		prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase  ,torchify=lowercase)

             # Test not batched input
             UpperCAmelCase_	:						Tuple					   =		image_processing(image_inputs[0]  ,return_image_mask=lowercase  ,return_tensors="pt")
             self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,75)



       def 			A_ (  self):

             """simple docstring"""





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

             # Test not batched input
             UpperCAmelCase_	:						Optional[int]					   =		image_processing(image_inputs[0]  ,return_codebook_pixels=lowercase  ,return_tensors="pt")
             UpperCAmelCase_   ,   UpperCAmelCase_	:						Tuple					   =		self.image_processor_tester.get_expected_codebook_image_size()
             self.assertEqual(
                 encoded_images.codebook_pixel_values.shape  ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,)

             # Test batched
             UpperCAmelCase_	:						str					   =		image_processing(lowercase  ,return_codebook_pixels=lowercase  ,return_tensors="pt")
             UpperCAmelCase_   ,   UpperCAmelCase_	:						List[Any]					   =		self.image_processor_tester.get_expected_codebook_image_size()
             self.assertEqual(
                 encoded_images.codebook_pixel_values.shape  ,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )  ,)





import heapq







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[(0, start)]  # cost from start node,end node
      UpperCAmelCase_	:						List[Any]					   =		set()
      while heap:
            ((UpperCAmelCase_)   ,   (UpperCAmelCase_))	:						str					   =		heapq.heappop(__snake_case     )
            if u in visited:
                  continue
            visited.add(__snake_case     )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  UpperCAmelCase_	:						Tuple					   =		cost + c
                  heapq.heappush(__snake_case       ,	(next_item, v)     )
      return -1


__lowerCamelCase								=      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase								=      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()
