


'''simple docstring'''




import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 						UpperCAmelCase_ (    _lowercase						):





     @require_torch
     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    Dict:
        lowerCAmelCase	=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

        lowerCAmelCase	=     '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

        lowerCAmelCase	=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

        # Force fetching the files so that we can use the cache
        lowerCAmelCase	=     'hf-internal-testing/tiny-random-bert'
        BertConfig.from_pretrained(__SCREAMING_SNAKE_CASE       )
        BertModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
        BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )
        pipeline(task='fill-mask'       , model=__SCREAMING_SNAKE_CASE       )

        # baseline - just load from_pretrained with normal network
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, run, mock]       )]

        # should succeed
        lowerCAmelCase	=     self.get_env()
        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        lowerCAmelCase	=     '1'
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )





     @require_torch
     def 				__UpperCAmelCase (					self			: List[Any]       )							->    Any:
        lowerCAmelCase	=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

        lowerCAmelCase	=     '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

        lowerCAmelCase	=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

        # Force fetching the files so that we can use the cache
        lowerCAmelCase	=     'hf-internal-testing/tiny-random-bert'
        BertConfig.from_pretrained(__SCREAMING_SNAKE_CASE       )
        BertModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
        BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )
        pipeline(task='fill-mask'       , model=__SCREAMING_SNAKE_CASE       )

        # baseline - just load from_pretrained with normal network
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, run, mock]       )]

        # should succeed
        lowerCAmelCase	=     self.get_env()
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )





     @require_torch
     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    Union[str, Any]:
        lowerCAmelCase	=     '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

        lowerCAmelCase	=     '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

        lowerCAmelCase	=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

        # baseline - just load from_pretrained with normal network
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, run]       )]

        # should succeed
        lowerCAmelCase	=     self.get_env()
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )

        # next emulate no network
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, mock, run]       )]

        # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
        # env["TRANSFORMERS_OFFLINE"] = "0"
        # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
        # self.assertEqual(result.returncode, 1, result.stderr)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        lowerCAmelCase	=     '1'
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )





     @require_torch
     def 				__UpperCAmelCase (					self			: Dict       )							->    Any:
        lowerCAmelCase	=     '\nfrom transformers import pipeline\n        '
        lowerCAmelCase	=     '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

        lowerCAmelCase	=     '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
        lowerCAmelCase	=     self.get_env()
        lowerCAmelCase	=     '1'
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, mock, run]       )]
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 1       , result.stderr       )
        self.assertIn(
            'You cannot infer task automatically within `pipeline` when using offline mode'       , result.stderr.decode().replace('\n'       , ''       )       , )





     @require_torch
     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    List[str]:
        lowerCAmelCase	=     '\nfrom transformers import AutoModel\n        '
        lowerCAmelCase	=     '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

        # baseline - just load from_pretrained with normal network
        lowerCAmelCase	=     [sys.executable, '-c', '\n'.join([load, run]       )]

        # should succeed
        lowerCAmelCase	=     self.get_env()
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        lowerCAmelCase	=     '1'
        lowerCAmelCase	=     subprocess.run(__SCREAMING_SNAKE_CASE       , env=__SCREAMING_SNAKE_CASE       , check=__SCREAMING_SNAKE_CASE       , capture_output=__SCREAMING_SNAKE_CASE       )
        self.assertEqual(result.returncode       , 0       , result.stderr       )
        self.assertIn('success'       , result.stdout.decode()       )







"""simple docstring"""




from __future__ import annotations

a		:       str     						=       {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







class     lowercase:



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







         """simple docstring"""



         a__		=			graph
         # mapping node to its parent in resulting breadth first tree
         a__		=			{}
         a__		=			source_vertex



     def 			lowercase__  (							self   )		->     None:







         """simple docstring"""



         a__		=			{self.source_vertex}
         a__		=			None
         a__		=			[self.source_vertex]  # first in first out queue

         while queue:
             a__		=			queue.pop(0   )
             for adjacent_vertex in self.graph[vertex]:
                 if adjacent_vertex not in visited:
                     visited.add(__SCREAMING_SNAKE_CASE   )
                     a__		=			vertex
                     queue.append(__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         if target_vertex == self.source_vertex:
             return self.source_vertex

         a__		=			self.parent.get(__SCREAMING_SNAKE_CASE   )
         if target_vertex_parent is None:
             a__		=			(
                 f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
             )
             raise ValueError(__SCREAMING_SNAKE_CASE   )

         return self.shortest_path(__SCREAMING_SNAKE_CASE   ) + f'->{target_vertex}'


if __name__ == "__main__":
    a		:       Optional[Any]     						=       Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))


