


import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE		:List[str]											=       namedtuple('''ExampleNamedTuple''', '''a b c''')


class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    Dict					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   torch.randn(5      ,			2					)
     snake_case_         =   torch.device("cuda"					) if torch.cuda.is_available() else torch.device("cpu"					)

     snake_case_         =   send_to_device(lowercase_      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta.cpu()      ,			lowercase_					)					)

     snake_case_         =   send_to_device((tensor, [tensor, tensor], 1)      ,			lowercase_					)
     self.assertIsInstance(lowercase_      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta[0].cpu()      ,			lowercase_					)					)
     self.assertIsInstance(resulta[1]      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta[1][0].cpu()      ,			lowercase_					)					)
     self.assertTrue(torch.equal(resulta[1][1].cpu()      ,			lowercase_					)					)
     self.assertEqual(resulta[2]      ,			1					)

     snake_case_         =   send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1}      ,			lowercase_					)
     self.assertIsInstance(lowercase_      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta["a"].cpu()      ,			lowercase_					)					)
     self.assertIsInstance(resulta["b"]      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta["b"][0].cpu()      ,			lowercase_					)					)
     self.assertTrue(torch.equal(resulta["b"][1].cpu()      ,			lowercase_					)					)
     self.assertEqual(resulta["c"]      ,			1					)

     snake_case_         =   send_to_device(ExampleNamedTuple(a=lowercase_      ,			b=[tensor, tensor]      ,			c=1					)      ,			lowercase_					)
     self.assertIsInstance(lowercase_      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta.a.cpu()      ,			lowercase_					)					)
     self.assertIsInstance(resulta.b      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta.b[0].cpu()      ,			lowercase_					)					)
     self.assertTrue(torch.equal(resulta.b[1].cpu()      ,			lowercase_					)					)
     self.assertEqual(resulta.c      ,			1					)

     snake_case_         =   send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}					)      ,			lowercase_					)
     self.assertIsInstance(lowercase_      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta["a"].cpu()      ,			lowercase_					)					)
     self.assertIsInstance(resulta["b"]      ,			lowercase_					)
     self.assertTrue(torch.equal(resulta["b"][0].cpu()      ,			lowercase_					)					)
     self.assertTrue(torch.equal(resulta["b"][1].cpu()      ,			lowercase_					)					)
     self.assertEqual(resulta["c"]      ,			1					)



  def    lowerCAmelCase__					(       self    :    str					)				->	List[str]:




     """simple docstring"""
     with self.assertRaises(lowercase_					) as cm:
        snake_case_         =   recursively_apply(torch.tensor      ,			(torch.tensor(1					), 1)      ,			error_on_other_type=lowercase_					)
     self.assertEqual(
         str(cm.exception					)      ,			"Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."      ,			)



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	List[str]:




     """simple docstring"""
     snake_case_         =   torch.tensor([1, 2, 3, 4, 5]					)
     self.assertEqual(listify(lowercase_					)      ,			[1, 2, 3, 4, 5]					)

     snake_case_         =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]					)
     self.assertEqual(listify(lowercase_					)      ,			[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]					)

     snake_case_         =   torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]					)
     self.assertEqual(
         listify(lowercase_					)      ,			[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[str]:




     """simple docstring"""
     with patch_environment(aa=1      ,			BB=2					):
        self.assertEqual(os.environ.get("AA"					)      ,			"1"					)
        self.assertEqual(os.environ.get("BB"					)      ,			"2"					)

     self.assertNotIn("AA"      ,			os.environ					)
     self.assertNotIn("BB"      ,			os.environ					)



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   RegressionModel()
     snake_case_         =   model.forward
     snake_case_         =   convert_outputs_to_fpaa(model.forward					)
     snake_case_         =   extract_model_from_parallel(lowercase_      ,			keep_fpaa_wrapper=lowercase_					)
     snake_case_         =   pickle.dumps(lowercase_					)



  @require_cuda
  def    lowerCAmelCase__					(       self    :    str					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   RegressionModel()
     snake_case_         =   model.forward
     snake_case_         =   torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
     snake_case_         =   convert_outputs_to_fpaa(model.forward					)
     snake_case_         =   extract_model_from_parallel(lowercase_      ,			keep_fpaa_wrapper=lowercase_					)
     snake_case_         =   pickle.dumps(lowercase_					)



  @require_cuda
  @require_torch_min_version(version="2.0"					)
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	str:




     """simple docstring"""
     snake_case_         =   RegressionModel()
     snake_case_         =   model.forward
     snake_case_         =   torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
     snake_case_         =   convert_outputs_to_fpaa(model.forward					)
     snake_case_         =   torch.compile(model.forward      ,			backend="inductor"					)
     snake_case_         =   torch.randn(4      ,			1_0					).cuda()
     snake_case_         =   model(lowercase_					)



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	List[str]:




     """simple docstring"""
     snake_case_         =   RegressionModel()
     # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
     snake_case_         =   torch.nn.parallel.DataParallel(lowercase_					)
     snake_case_         =   extract_model_from_parallel(lowercase_					)

     self.assertEqual(lowercase_      ,			lowercase_					)



  @require_torch_min_version(version="2.0"					)
  def    lowerCAmelCase__					(       self    :    int					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   RegressionModel()
     snake_case_         =   torch.compile(lowercase_					)

     # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
     snake_case_         =   torch.nn.parallel.DataParallel(lowercase_					)
     snake_case_         =   torch.compile(lowercase_					)
     snake_case_         =   extract_model_from_parallel(lowercase_					)

     self.assertEqual(compiled_model._orig_mod      ,			compiled_model_unwrapped._orig_mod					)



  def    lowerCAmelCase__					(       self    :    int					)				->	Optional[int]:




     """simple docstring"""
     self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]					)]					)      ,			torch.device("cpu"					)					)
     self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]					)}					)      ,			torch.device("cpu"					)					)
     self.assertIsNone(find_device([1, "a"]					)					)








import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:Dict											=       {'''UserAgent''': UserAgent().random}
SCREAMING_SNAKE_CASE		:Dict											=       '''https://www.mywaifulist.moe/random'''



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:str							)->None:
   '''simple docstring'''






   snake_case_         =   requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							)
   with open(lowerCAmelCase_						,  "wb"							) as file:
      file.write(image.content							)



def      _lowerCAmelCase  (							)->tuple[str, str, str]:
   '''simple docstring'''






   snake_case_         =   BeautifulSoup(requests.get(lowerCAmelCase_						,  headers=lowerCAmelCase_							).text						,  "html.parser"							)
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:title"}							).attrs["content"]
   snake_case_         =   soup.find("meta"						,  attrs={"property": "og:image"}							).attrs["content"]
   snake_case_         =   soup.find("p"						,  id="description"							).get_text()
   snake_case_      ,      snake_case_         =   os.path.splitext(os.path.basename(lowerCAmelCase_							)							)
   snake_case_         =   title.strip().replace(" "						,  "_"							)
   snake_case_         =   F'''{image_title}{image_extension}'''
   save_image(lowerCAmelCase_						,  lowerCAmelCase_							)
   return (title, description, image_title)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE						,		SCREAMING_SNAKE_CASE		:Optional[Any]											=       random_anime_character()
   print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')




