


import unittest

import numpy as np
import requests

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

 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 __snake_case		:int   	=  False

if is_vision_available():
 from PIL import Image

 from transformers import PixaStructImageProcessor

class 					_A (			unittest.TestCase					):



       def __init__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Dict=7  ,	__SCREAMING_SNAKE_CASE							:  List[str]=3  ,	__SCREAMING_SNAKE_CASE							:  Tuple=18  ,	__SCREAMING_SNAKE_CASE							:  str=30  ,	__SCREAMING_SNAKE_CASE							:  Any=400  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Any=True  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	):


           '''simple docstring'''

           __a		 =       size if size is not None else {"height": 20, "width": 20}
           __a		 =       parent
           __a		 =       batch_size
           __a		 =       num_channels
           __a		 =       image_size
           __a		 =       min_resolution
           __a		 =       max_resolution
           __a		 =       size
           __a		 =       do_normalize
           __a		 =       do_convert_rgb
           __a		 =       [512, 1_024, 2_048, 4_096]
           __a		 =       patch_size if patch_size is not None else {"height": 16, "width": 16}



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
           __a		 =       Image.open(requests.get(lowerCAmelCase__  ,	stream=lowerCAmelCase__).raw).convert('''RGB''')
           return raw_image


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''   ,)
@require_torch
@require_vision
class 					_A (			a__   ,unittest.TestCase					):
       UpperCamelCase__						:					Optional[int]		      =		PixaStructImageProcessor if is_vision_available() else None



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


           '''simple docstring'''

           __a		 =       PixaStructImageProcessingTester(self)



       @property
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



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


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(lowerCAmelCase__  ,	'''do_normalize'''))
           self.assertTrue(hasattr(lowerCAmelCase__  ,	'''do_convert_rgb'''))



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.image_processor_tester.prepare_dummy_image()

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           __a		 =       2_048

           __a		 =       image_processor(lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__)
           self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,	torch.tensor(0.06_06)  ,	atol=1E-3  ,	rtol=1E-3))



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


           '''simple docstring'''

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

           # Test not batched input
           __a		 =       (
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __a		 =       image_processor(
                   image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(1, max_patch, expected_hidden_dim)  ,	)

               # Test batched
               __a		 =       image_processor(
                   lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,	)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

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

           # Test not batched input
           __a		 =       (
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           __a		 =       True

           for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               with self.assertRaises(lowerCAmelCase__):
                   __a		 =       image_processor(
                       image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches

               __a		 =       "Hello"

               __a		 =       image_processor(
                   image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__  ,	header_text=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(1, max_patch, expected_hidden_dim)  ,	)

               # Test batched
               __a		 =       image_processor(
                   lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__  ,	header_text=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,	)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=lowerCAmelCase__  ,	numpify=lowerCAmelCase__)
           for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__  ,	np.ndarray)

           __a		 =       (
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __a		 =       image_processor(
                   image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(1, max_patch, expected_hidden_dim)  ,	)

               # Test batched
               __a		 =       image_processor(
                   lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,	)





       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       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
           __a		 =       (
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __a		 =       image_processor(
                   image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(1, max_patch, expected_hidden_dim)  ,	)

               # Test batched
               __a		 =       image_processor(
                   lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,	)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''   ,)
@require_torch
@require_vision
class 					_A (			a__   ,unittest.TestCase					):
       UpperCamelCase__						:					Optional[int]		      =		PixaStructImageProcessor if is_vision_available() else None



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       PixaStructImageProcessingTester(self  ,	num_channels=4)
           __a		 =       3



       @property
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(lowerCAmelCase__  ,	'''do_normalize'''))
           self.assertTrue(hasattr(lowerCAmelCase__  ,	'''do_convert_rgb'''))





       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

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

           # Test not batched input
           __a		 =       (
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * (self.image_processor_tester.num_channels - 1)
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __a		 =       image_processor(
                   image_inputs[0]  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(1, max_patch, expected_hidden_dim)  ,	)

               # Test batched
               __a		 =       image_processor(
                   lowerCAmelCase__  ,	return_tensors='''pt'''  ,	max_patches=lowerCAmelCase__).flattened_patches
               self.assertEqual(
                   encoded_images.shape  ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,	)










from __future__ import annotations





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





