


import heapq

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     __UpperCamelCase       				=[(0, start)]  # cost from start node,end node
     __UpperCamelCase       				=set()
     while heap:
          ((__UpperCamelCase)				, (__UpperCamelCase))       				=heapq.heappop(lowerCAmelCase_							)
          if u in visited:
               continue
          visited.add(lowerCAmelCase_							)
          if u == end:
               return cost
          for v, c in graph[u]:
               if v in visited:
                    continue
               __UpperCamelCase       				=cost + c
               heapq.heappush(lowerCAmelCase_     ,	(next_item, v)							)
     return -1


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

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

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

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

if __name__ == "__main__":
   import doctest

   doctest.testmod()






import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch





class   lowerCAmelCase    (       unittest.TestCase      ):



     @patch('sys.argv'					,   ['fakeprogrampath', 'env']							)
     def       a__	(       self							):
            # test transformers-cli env
            import transformers.commands.transformers_cli

            with CaptureStd() as cs:
                   transformers.commands.transformers_cli.main()
            self.assertIn('Python version'					,   cs.out							)
            self.assertIn('Platform'					,   cs.out							)
            self.assertIn('Using distributed or parallel set-up in script?'					,   cs.out							)



     @is_pt_tf_cross_test
     @patch(
         'sys.argv'					,   ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)  # cleans potential past runs
            transformers.commands.transformers_cli.main()

            # The original repo has no TF weights -- if they exist, they were created by the CLI
            self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'							)							)



     @require_torch
     @patch('sys.argv'					,   ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'							)							)





     @require_torch
     @patch(
         'sys.argv'					,   [
             'fakeprogrampath',
             'download',
             'hf-internal-testing/test_dynamic_model_with_tokenizer',
             '--trust-remote-code',
             '--cache-dir',
             '/tmp',
         ]					,   )
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'							)							)
            self.assertTrue(
                os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'							)							)