



'''simple docstring'''





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,
)


lowercase_			=  namedtuple("ExampleNamedTuple", "a b c")






class __A      (  unittest.TestCase						):



 '''simple docstring'''







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


      """simple docstring"""



      _a     =  torch.randn(5			,       2	)
      _a     =  torch.device('''cuda'''	) if torch.cuda.is_available() else torch.device('''cpu'''	)

      _a     =  send_to_device(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE	)
      self.assertTrue(torch.equal(resulta.cpu()			,       __SCREAMING_SNAKE_CASE	)	)

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

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

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

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



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      with self.assertRaises(__SCREAMING_SNAKE_CASE	) as cm:
           _a     =  recursively_apply(torch.tensor			,       (torch.tensor(1	), 1)			,       error_on_other_type=__SCREAMING_SNAKE_CASE	)
      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  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  torch.tensor([1, 2, 3, 4, 5]	)
      self.assertEqual(listify(__SCREAMING_SNAKE_CASE	)			,       [1, 2, 3, 4, 5]	)

      _a     =  torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)
      self.assertEqual(listify(__SCREAMING_SNAKE_CASE	)			,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)

      _a     =  torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]	)
      self.assertEqual(
          listify(__SCREAMING_SNAKE_CASE	)			,       [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]	)



 def  a__  (self	)   ->					Optional[Any]:


      """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  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  RegressionModel()
      _a     =  model.forward
      _a     =  convert_outputs_to_fpaa(model.forward	)
      _a     =  extract_model_from_parallel(__SCREAMING_SNAKE_CASE			,       keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE	)
      _a     =  pickle.dumps(__SCREAMING_SNAKE_CASE	)



 @require_cuda
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  RegressionModel()
      _a     =  model.forward
      _a     =  torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
      _a     =  convert_outputs_to_fpaa(model.forward	)
      _a     =  extract_model_from_parallel(__SCREAMING_SNAKE_CASE			,       keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE	)
      _a     =  pickle.dumps(__SCREAMING_SNAKE_CASE	)



 @require_cuda
 @require_torch_min_version(version='''2.0'''	)
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  RegressionModel()
      _a     =  model.forward
      _a     =  torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
      _a     =  convert_outputs_to_fpaa(model.forward	)
      _a     =  torch.compile(model.forward			,       backend='''inductor'''	)
      _a     =  torch.randn(4			,       10	).cuda()
      _a     =  model(__SCREAMING_SNAKE_CASE	)



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



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

      self.assertEqual(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE	)



 @require_torch_min_version(version='''2.0'''	)
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  RegressionModel()
      _a     =  torch.compile(__SCREAMING_SNAKE_CASE	)

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

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



 def  a__  (self	)   ->					Optional[Any]:


      """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''']	)	)








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



