'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		int 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))





from __future__ import annotations




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						key



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Any 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Optional[Any] 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""encrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""decrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




