
import os
import sys
import unittest


_UpperCAmelCase      :				List[Any]          = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase      :				str          = os.path.join(git_repo_path, "src", "transformers")

_UpperCAmelCase      :				Union[str, Any]          = "\n{0} = None\n"

_UpperCAmelCase      :				List[Any]          = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


_UpperCAmelCase      :				Optional[int]          = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"


class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
				self.assertIsNone(A_ )

				UpperCamelCase						=  find_backend('    if not is_tokenizers_available():' )
				self.assertEqual(A_       ,   'tokenizers' )

				UpperCamelCase						=  find_backend('    if not is_tensorflow_text_available():' )
				self.assertEqual(A_       ,   'tensorflow_text' )

				UpperCamelCase						=  find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
				self.assertEqual(A_       ,   'sentencepiece_and_tokenizers' )

				UpperCamelCase						=  find_backend(
				    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
				self.assertEqual(A_       ,   'sentencepiece_and_tensorflow_text' )

				UpperCamelCase						=  find_backend(
				    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
				self.assertEqual(A_       ,   'sentencepiece_and_tokenizers_and_vision' )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'       ,   A_ )
				self.assertIn('tensorflow_text'       ,   A_ )
				self.assertIn('sentencepiece_and_tokenizers'       ,   A_ )

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('BertModel'       ,   objects['torch'] )
				self.assertIn('TFBertModel'       ,   objects['tf'] )
				self.assertIn('FlaxBertModel'       ,   objects['flax'] )
				self.assertIn('BertModel'       ,   objects['torch'] )
				self.assertIn('TFBertTokenizer'       ,   objects['tensorflow_text'] )
				self.assertIn('convert_slow_tokenizer'       ,   objects['sentencepiece_and_tokenizers'] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  create_dummy_object('CONSTANT'       ,   '\'torch\'' )
				self.assertEqual(A_       ,   '\nCONSTANT = None\n' )

				UpperCamelCase						=  create_dummy_object('function'       ,   '\'torch\'' )
				self.assertEqual(
				    A_       ,   '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

				UpperCamelCase						=  '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
				UpperCamelCase						=  create_dummy_object('FakeClass'       ,   '\'torch\'' )
				self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
				UpperCamelCase						=  create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
				self.assertEqual(dummy_files['torch']       ,   A_ )


from string import ascii_uppercase

_UpperCAmelCase      :				Dict          = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase      :				Tuple          = dict(enumerate(ascii_uppercase))


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  len(lowercase				)
		UpperCamelCase						=  0
		while True:
				if x == i:
						UpperCamelCase						=  0
				if len(lowercase				) == len(lowercase				):
						break
				key += key[i]
				i += 1
		return key


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in message:
				if letter == " ":
						cipher_text += " "
				else:
						UpperCamelCase						=  (dicta[letter] - dicta[key_new[i]]) % 26
						i += 1
						cipher_text += dicta[x]
		return cipher_text


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in cipher_text:
				if letter == " ":
						or_txt += " "
				else:
						UpperCamelCase						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
						i += 1
						or_txt += dicta[x]
		return or_txt


def    A (      )     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  'THE GERMAN ATTACK'
		UpperCamelCase						=  'SECRET'
		UpperCamelCase						=  generate_key(lowercase					, lowercase				)
		UpperCamelCase						=  cipher_text(lowercase					, lowercase				)
		print(f'''Encrypted Text = {s}'''				)
		print(f'''Original Text = {original_text(lowercase					, lowercase				)}'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
