

'''simple docstring'''






import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     int=False  ,  SCREAMING_SNAKE_CASE_					:     Tuple=False  ,  SCREAMING_SNAKE_CASE_					:     List[Any]=False   )							->       Dict:
      """simple docstring"""

      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      SCREAMING_SNAKE_CASE_				:      int					=	[]
      for i in range(image_processor_tester.batch_size   ):
            if equal_resolution:
                  SCREAMING_SNAKE_CASE_				:      int					=	image_processor_tester.max_resolution
            else:
                  # To avoid getting image width/height 0
                  SCREAMING_SNAKE_CASE_				:      List[Any]					=	image_processor_tester.min_resolution
                  if getattr(_lowerCamelCase  ,  "size_divisor"  ,  _lowerCamelCase   ):
                        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                        SCREAMING_SNAKE_CASE_				:      Dict					=	max(image_processor_tester.size_divisor  ,  _lowerCamelCase   )
                  SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	np.random.choice(np.arange(_lowerCamelCase  ,  image_processor_tester.max_resolution   )  ,  2   )
            image_inputs.append(
                np.random.randint(2_5_5  ,  size=(image_processor_tester.num_channels, width, height)  ,  dtype=np.uinta   )   )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            SCREAMING_SNAKE_CASE_				:      List[str]					=	[Image.fromarray(np.moveaxis(_lowerCamelCase  ,  0  ,  -1   )   ) for image in image_inputs]

      if torchify:
            SCREAMING_SNAKE_CASE_				:      List[str]					=	[torch.from_numpy(_lowerCamelCase   ) for image in image_inputs]

      return image_inputs


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Tuple=1_0  ,  SCREAMING_SNAKE_CASE_					:     List[str]=1_0  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]=False  ,  SCREAMING_SNAKE_CASE_					:     str=False   )							->       Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	[]
      for i in range(image_processor_tester.num_frames   ):
            video.append(np.random.randint(2_5_5  ,  size=(image_processor_tester.num_channels, width, height)  ,  dtype=np.uinta   )   )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            SCREAMING_SNAKE_CASE_				:      str					=	[Image.fromarray(np.moveaxis(_lowerCamelCase  ,  0  ,  -1   )   ) for frame in video]

      if torchify:
            SCREAMING_SNAKE_CASE_				:      List[str]					=	[torch.from_numpy(_lowerCamelCase   ) for frame in video]

      return video


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any=False  ,  SCREAMING_SNAKE_CASE_					:     int=False  ,  SCREAMING_SNAKE_CASE_					:     Optional[int]=False   )							->       Union[str, Any]:
      """simple docstring"""

      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      SCREAMING_SNAKE_CASE_				:      List[str]					=	[]
      for i in range(image_processor_tester.batch_size   ):
            if equal_resolution:
                  SCREAMING_SNAKE_CASE_				:      List[str]					=	image_processor_tester.max_resolution
            else:
                  SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Tuple					=	np.random.choice(
                      np.arange(image_processor_tester.min_resolution  ,  image_processor_tester.max_resolution   )  ,  2   )
                  SCREAMING_SNAKE_CASE_				:      Dict					=	prepare_video(
                      image_processor_tester=_lowerCamelCase  ,  width=_lowerCamelCase  ,  height=_lowerCamelCase  ,  numpify=_lowerCamelCase  ,  torchify=_lowerCamelCase  ,  )
            video_inputs.append(_lowerCamelCase   )

      return video_inputs





class      SCREAMING_SNAKE_CASE__      :
    _A         =							None




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.image_processing_class(**self.image_processor_dict      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	json.loads(image_processor.to_json_string()      )
          for key, value in self.image_processor_dict.items():
                self.assertEqual(obj[key]    ,					__SCREAMING_SNAKE_CASE      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.image_processing_class(**self.image_processor_dict      )

          with tempfile.TemporaryDirectory() as tmpdirname:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	os.path.join(__SCREAMING_SNAKE_CASE    ,					"image_processor.json"      )
                image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE      )
                SCREAMING_SNAKE_CASE_				:      int					=	self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE      )

          self.assertEqual(image_processor_second.to_dict()    ,					image_processor_first.to_dict()      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.image_processing_class(**self.image_processor_dict      )

          with tempfile.TemporaryDirectory() as tmpdirname:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE      )[0]
                check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE      )
                SCREAMING_SNAKE_CASE_				:      List[str]					=	self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE      )

          self.assertEqual(image_processor_second.to_dict()    ,					image_processor_first.to_dict()      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.image_processing_class()
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )







    @require_torch
    @require_vision
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          if self.test_cast_dtype is not None:
                # Initialize image_processor
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.image_processing_class(**self.image_processor_dict      )

                # create random PyTorch tensors
                SCREAMING_SNAKE_CASE_				:      Tuple					=	prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__SCREAMING_SNAKE_CASE    ,					torchify=__SCREAMING_SNAKE_CASE      )

                SCREAMING_SNAKE_CASE_				:      Dict					=	image_processor(__SCREAMING_SNAKE_CASE    ,					return_tensors="pt"      )
                # for layoutLM compatiblity
                self.assertEqual(encoding.pixel_values.device    ,					torch.device("cpu"      )      )
                self.assertEqual(encoding.pixel_values.dtype    ,					torch.floataa      )

                SCREAMING_SNAKE_CASE_				:      Dict					=	image_processor(__SCREAMING_SNAKE_CASE    ,					return_tensors="pt"      ).to(torch.floataa      )
                self.assertEqual(encoding.pixel_values.device    ,					torch.device("cpu"      )      )
                self.assertEqual(encoding.pixel_values.dtype    ,					torch.floataa      )

                SCREAMING_SNAKE_CASE_				:      Any					=	image_processor(__SCREAMING_SNAKE_CASE    ,					return_tensors="pt"      ).to("cpu"    ,					torch.bfloataa      )
                self.assertEqual(encoding.pixel_values.device    ,					torch.device("cpu"      )      )
                self.assertEqual(encoding.pixel_values.dtype    ,					torch.bfloataa      )

                with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	image_processor(__SCREAMING_SNAKE_CASE    ,					return_tensors="pt"      ).to(torch.bfloataa    ,					"cpu"      )

                # Try with text + image feature
                SCREAMING_SNAKE_CASE_				:      Any					=	image_processor(__SCREAMING_SNAKE_CASE    ,					return_tensors="pt"      )
                encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]      )}      )
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	encoding.to(torch.floataa      )

                self.assertEqual(encoding.pixel_values.device    ,					torch.device("cpu"      )      )
                self.assertEqual(encoding.pixel_values.dtype    ,					torch.floataa      )
                self.assertEqual(encoding.input_ids.dtype    ,					torch.long      )




'''simple docstring'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

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


snake_case_					  =	{
    '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]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

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

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

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

if __name__ == "__main__":
      import doctest

      doctest.testmod()

