import subprocess
import sys

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


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @require_torch
    def       UpperCamelCase__       (     self)	->     Tuple:
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        __UpperCamelCase					:Dict        =		'''
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					:Optional[int]        =		'''
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					:str        =		'''hf-internal-testing/tiny-random-bert'''
        BertConfig.from_pretrained(__lowercase)
        BertModel.from_pretrained(__lowercase)
        BertTokenizer.from_pretrained(__lowercase)
        pipeline(task='''fill-mask'''			,			model=__lowercase)

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

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







    @require_torch
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # python one-liner segments
        # this must be loaded before socket.socket is monkey-patched
        __UpperCamelCase					:List[str]        =		'''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

        __UpperCamelCase					:Dict        =		'''
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					:Tuple        =		'''
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					:Optional[int]        =		'''hf-internal-testing/tiny-random-bert'''
        BertConfig.from_pretrained(__lowercase)
        BertModel.from_pretrained(__lowercase)
        BertTokenizer.from_pretrained(__lowercase)
        pipeline(task='''fill-mask'''			,			model=__lowercase)

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

        # should succeed
        __UpperCamelCase					:Any        =		self.get_env()
        __UpperCamelCase					:Dict        =		subprocess.run(__lowercase			,			env=__lowercase			,			check=__lowercase			,			capture_output=__lowercase)
        self.assertEqual(result.returncode			,			0			,			result.stderr)
        self.assertIn('''success'''			,			result.stdout.decode())







    @require_torch
    def       UpperCamelCase__       (     self)	->     str:
        # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
        # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
        # while running an external program

        # python one-liner segments

        # this must be loaded before socket.socket is monkey-patched
        __UpperCamelCase					:Any        =		'''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

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

        __UpperCamelCase					:str        =		'''
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					:Any        =		[sys.executable, '''-c''', '''\n'''.join([load, run])]

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

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







    @require_torch
    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		'''
from transformers import pipeline
        '''
        __UpperCamelCase					:List[Any]        =		'''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

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

        # should succeed
        __UpperCamelCase					:int        =		self.get_env()
        __UpperCamelCase					:str        =		subprocess.run(__lowercase			,			env=__lowercase			,			check=__lowercase			,			capture_output=__lowercase)
        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					:Dict        =		'''1'''
        __UpperCamelCase					:Tuple        =		subprocess.run(__lowercase			,			env=__lowercase			,			check=__lowercase			,			capture_output=__lowercase)
        self.assertEqual(result.returncode			,			0			,			result.stderr)
        self.assertIn('''success'''			,			result.stdout.decode())






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (inductance, frequency, reactance).count(0     ) != 1:
        raise ValueError('''One and only one argument must be 0'''     )
    if inductance < 0:
        raise ValueError('''Inductance cannot be negative'''     )
    if frequency < 0:
        raise ValueError('''Frequency cannot be negative'''     )
    if reactance < 0:
        raise ValueError('''Inductive reactance cannot be negative'''     )
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





