'''simple docstring'''

import subprocess
import sys

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


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	'''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

         UpperCAmelCase__				:					List[Any] 					=	'''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

         UpperCAmelCase__				:					str 					=	'''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
        '''

         # Force fetching the files so that we can use the cache
         UpperCAmelCase__				:					List[str] 					=	'''hf-internal-testing/tiny-random-bert'''
         BertConfig.from_pretrained(lowerCamelCase_				)
         BertModel.from_pretrained(lowerCamelCase_				)
         BertTokenizer.from_pretrained(lowerCamelCase_				)
         pipeline(task='''fill-mask'''       ,model=lowerCamelCase_				)

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

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




      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	'''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

         UpperCAmelCase__				:					Tuple 					=	'''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

         UpperCAmelCase__				:					Optional[Any] 					=	'''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        '''

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

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

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




      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	'''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

         UpperCAmelCase__				:					Optional[Any] 					=	'''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        '''

         UpperCAmelCase__				:					Optional[int] 					=	'''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        '''

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

         # should succeed
         UpperCAmelCase__				:					List[Any] 					=	self.get_env()
         UpperCAmelCase__				:					Optional[int] 					=	subprocess.run(lowerCamelCase_       ,env=lowerCamelCase_       ,check=lowerCamelCase_       ,capture_output=lowerCamelCase_				)
         self.assertEqual(result.returncode       ,0       ,result.stderr				)
         self.assertIn('''success'''       ,result.stdout.decode()				)

         # next emulate no network
         UpperCAmelCase__				:					str 					=	[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
         UpperCAmelCase__				:					Any 					=	'''1'''
         UpperCAmelCase__				:					Optional[Any] 					=	subprocess.run(lowerCamelCase_       ,env=lowerCamelCase_       ,check=lowerCamelCase_       ,capture_output=lowerCamelCase_				)
         self.assertEqual(result.returncode       ,0       ,result.stderr				)
         self.assertIn('''success'''       ,result.stdout.decode()				)




      @require_torch
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	'''
from transformers import pipeline
        '''
         UpperCAmelCase__				:					Optional[Any] 					=	'''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

         UpperCAmelCase__				:					Union[str, Any] 					=	'''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        '''
         UpperCAmelCase__				:					Optional[int] 					=	self.get_env()
         UpperCAmelCase__				:					Any 					=	'''1'''
         UpperCAmelCase__				:					Tuple 					=	[sys.executable, '''-c''', '''\n'''.join([load, mock, run]				)]
         UpperCAmelCase__				:					Any 					=	subprocess.run(lowerCamelCase_       ,env=lowerCamelCase_       ,check=lowerCamelCase_       ,capture_output=lowerCamelCase_				)
         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 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	'''
from transformers import AutoModel
        '''
         UpperCAmelCase__				:					Optional[int] 					=	'''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        '''

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

         # should succeed
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_env()
         UpperCAmelCase__				:					int 					=	subprocess.run(lowerCamelCase_       ,env=lowerCamelCase_       ,check=lowerCamelCase_       ,capture_output=lowerCamelCase_				)
         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
         UpperCAmelCase__				:					Tuple 					=	'''1'''
         UpperCAmelCase__				:					Tuple 					=	subprocess.run(lowerCamelCase_       ,env=lowerCamelCase_       ,check=lowerCamelCase_       ,capture_output=lowerCamelCase_				)
         self.assertEqual(result.returncode       ,0       ,result.stderr				)
         self.assertIn('''success'''       ,result.stdout.decode()				)





'''simple docstring'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




