





import subprocess
import sys

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







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   @require_torch
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						str      		=  "\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        "

      a__	:						Optional[Any]      		=  "\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
      a__	:						Tuple      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

      # baseline - just load from_pretrained with normal network
      a__	:						Optional[int]      		=  [sys.executable, "-c", "\n".join([load, run, mock]      )]

      # should succeed
      a__	:						Dict      		=  self.get_env()
      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Tuple      		=  "1"
      a__	:						List[Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						Optional[Any]      		=  "\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        "

      a__	:						Tuple      		=  "\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
      a__	:						List[Any]      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

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

      # should succeed
      a__	:						Any      		=  self.get_env()
      a__	:						int      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

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

      a__	:						int      		=  "\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
      a__	:						List[Any]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						str      		=  self.get_env()
      a__	:						List[str]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

      # next emulate no network
      a__	:						List[Any]      		=  [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
      a__	:						Union[str, Any]      		=  "1"
      a__	:						Tuple      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Optional[Any]      		=  "\nfrom transformers import pipeline\n        "
      a__	:						int      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

      a__	:						Dict      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
      a__	:						List[str]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  "1"
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, mock, run]      )]
      a__	:						Any      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=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 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Any      		=  "\nfrom transformers import AutoModel\n        "
      a__	:						Any      		=  "\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
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						Optional[Any]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=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
      a__	:						Dict      		=  "1"
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

