
from __future__ import annotations

from typing import Any




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Any											=					num_of_nodes
           UpperCamelCase__     : list[list[int]]											=					[]
           UpperCamelCase__     : dict[int, int]											=					{}






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           self.m_edges.append([u_node, v_node, weight]	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if self.m_component[u_node] == u_node:
                 return u_node
           return self.find_component(self.m_component[u_node]	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           if self.m_component[u_node] != u_node:
                 for k in self.m_component:
                       UpperCamelCase__     : Any											=					self.find_component(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           if component_size[u_node] <= component_size[v_node]:
                 UpperCamelCase__     : int											=					v_node
                 component_size[v_node] += component_size[u_node]
                 self.set_component(__SCREAMING_SNAKE_CASE	)

           elif component_size[u_node] >= component_size[v_node]:
                 UpperCamelCase__     : int											=					self.find_component(__SCREAMING_SNAKE_CASE	)
                 component_size[u_node] += component_size[v_node]
                 self.set_component(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Any											=					[]
           UpperCamelCase__     : List[Any]											=					0

           UpperCamelCase__     : list[Any]											=					[-1] * self.m_num_of_nodes

           # A list of components (initialized to all of the nodes)
           for node in range(self.m_num_of_nodes	):
                 self.m_component.update({node: node}	)
                 component_size.append(1	)

           UpperCamelCase__     : List[Any]											=					self.m_num_of_nodes

           while num_of_components > 1:
                 for edge in self.m_edges:
                       UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : str											=					edge

                       UpperCamelCase__     : List[str]											=					self.m_component[u]
                       UpperCamelCase__     : Optional[Any]											=					self.m_component[v]

                       if u_component != v_component:

                             for component in (u_component, v_component):
                                   if (
                                       minimum_weight_edge[component] == -1
                                       or minimum_weight_edge[component][2] > w
                                   ):
                                         UpperCamelCase__     : int											=					[u, v, w]

                 for edge in minimum_weight_edge:
                       if isinstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                             UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : List[str]											=					edge

                             UpperCamelCase__     : Any											=					self.m_component[u]
                             UpperCamelCase__     : Dict											=					self.m_component[v]

                             if u_component != v_component:
                                   mst_weight += w
                                   self.union(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                                   print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''	)
                                   num_of_components -= 1

                 UpperCamelCase__     : Any											=					[-1] * self.m_num_of_nodes
           print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''	)


def 				SCREAMING_SNAKE_CASE_   (      ):
      pass


if __name__ == "__main__":
    import doctest

    doctest.testmod()




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast




@require_vision
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : int											=					tempfile.mkdtemp()

           UpperCamelCase__     : str											=					BlipImageProcessor()
           UpperCamelCase__     : List[Any]											=					GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''	)

           UpperCamelCase__     : List[Any]											=					BlipaProcessor(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           processor.save_pretrained(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           return AutoProcessor.from_pretrained(self.tmpdirname    ,    **__SCREAMING_SNAKE_CASE	).tokenizer






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           return AutoProcessor.from_pretrained(self.tmpdirname    ,    **__SCREAMING_SNAKE_CASE	).image_processor






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					[np.random.randint(2_5_5    ,    size=(3, 3_0, 4_0_0)    ,    dtype=np.uinta	)]

           UpperCamelCase__     : Dict											=					[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE    ,    0    ,    -1	)	) for x in image_inputs]

           return image_inputs






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					BlipaProcessor(tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           UpperCamelCase__     : Any											=					self.get_tokenizer(bos_token='''(BOS)'''    ,    eos_token='''(EOS)'''	)
           UpperCamelCase__     : str											=					self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE    ,    padding_value=1.0	)

           UpperCamelCase__     : Optional[Any]											=					BlipaProcessor.from_pretrained(
               self.tmpdirname    ,    bos_token='''(BOS)'''    ,    eos_token='''(EOS)'''    ,    do_normalize=__SCREAMING_SNAKE_CASE    ,    padding_value=1.0	)

           self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()	)
           self.assertIsInstance(processor.tokenizer    ,    __SCREAMING_SNAKE_CASE	)

           self.assertEqual(processor.image_processor.to_json_string()    ,    image_processor_add_kwargs.to_json_string()	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_tokenizer()

           UpperCamelCase__     : int											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Union[str, Any]											=					self.prepare_image_inputs()

           UpperCamelCase__     : Optional[Any]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : List[Any]											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.get_image_processor()
           UpperCamelCase__     : Any											=					self.get_tokenizer()

           UpperCamelCase__     : Tuple											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					'''lower newer'''

           UpperCamelCase__     : Tuple											=					processor(text=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					tokenizer(__SCREAMING_SNAKE_CASE    ,    return_token_type_ids=__SCREAMING_SNAKE_CASE	)

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.get_image_processor()
           UpperCamelCase__     : Optional[int]											=					self.get_tokenizer()

           UpperCamelCase__     : Dict											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Tuple											=					'''lower newer'''
           UpperCamelCase__     : Any											=					self.prepare_image_inputs()

           UpperCamelCase__     : Any											=					processor(text=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''pixel_values''', '''input_ids''', '''attention_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[int]											=					self.get_tokenizer()

           UpperCamelCase__     : Tuple											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           UpperCamelCase__     : Tuple											=					processor.batch_decode(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Dict											=					self.get_tokenizer()

           UpperCamelCase__     : int											=					BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,    image_processor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					'''lower newer'''
           UpperCamelCase__     : List[str]											=					self.prepare_image_inputs()

           UpperCamelCase__     : List[Any]											=					processor(text=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()	)    ,    ['''pixel_values''', '''input_ids''', '''attention_mask''']	)


