




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	__lowerCAmelCase			(			__magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						BartTokenizer
							UpperCamelCase__        =						BartTokenizerFast
							UpperCamelCase__        =						True
							UpperCamelCase__        =						filter_roberta_detectors
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								super().setUp()
								a		       =							[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								a		       =							dict(zip(__magic_name__	,   range(len(__magic_name__       )       )       )       )
								a		       =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								a		       =							{"""unk_token""": """<unk>"""}

								a		       =							os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES["""vocab_file"""]       )
								a		       =							os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES["""merges_file"""]       )
								with open(self.vocab_file	,   """w"""	,   encoding="""utf-8"""       ) as fp:
									fp.write(json.dumps(__magic_name__       ) + """\n"""       )
								with open(self.merges_file	,   """w"""	,   encoding="""utf-8"""       ) as fp:
									fp.write("""\n""".join(__magic_name__       )       )
							def    lowerCamelCase__							(		self		:List[Any]	,   **__magic_name__		:List[str]       ):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map       )
								return self.tokenizer_class.from_pretrained(self.tmpdirname	,   **__magic_name__       )
							def    lowerCamelCase__							(		self		:Tuple	,   **__magic_name__		:List[Any]       ):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map       )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,   **__magic_name__       )
							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:Any       ):







								'''simple docstring'''







								return "lower newer", "lower newer"
							@cached_property
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								return BartTokenizer.from_pretrained("""facebook/bart-large"""       )
							@cached_property
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								return BartTokenizerFast.from_pretrained("""facebook/bart-large"""       )
							@require_torch
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								a		       =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								a		       =							[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a		       =							tokenizer(__magic_name__	,   max_length=len(__magic_name__       )	,   padding=__magic_name__	,   return_tensors="""pt"""       )
									self.assertIsInstance(__magic_name__	,   __magic_name__       )

									self.assertEqual((2, 9)	,   batch.input_ids.shape       )
									self.assertEqual((2, 9)	,   batch.attention_mask.shape       )
									a		       =							batch.input_ids.tolist()[0]
									self.assertListEqual(__magic_name__	,   __magic_name__       )
									# Test that special tokens are reset
							@require_torch
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a		       =							tokenizer(__magic_name__	,   padding=__magic_name__	,   return_tensors="""pt"""       )
									# check if input_ids are returned and no labels
									self.assertIn("""input_ids"""	,   __magic_name__       )
									self.assertIn("""attention_mask"""	,   __magic_name__       )
									self.assertNotIn("""labels"""	,   __magic_name__       )
									self.assertNotIn("""decoder_attention_mask"""	,   __magic_name__       )
							@require_torch
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							[
								    """Summary of the text.""",
								    """Another summary.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a		       =							tokenizer(text_target=__magic_name__	,   max_length=32	,   padding="""max_length"""	,   return_tensors="""pt"""       )
									self.assertEqual(32	,   targets["""input_ids"""].shape[1]       )
							@require_torch
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a		       =							tokenizer(
									    ["""I am a small frog""" * 1024, """I am a small frog"""]	,   padding=__magic_name__	,   truncation=__magic_name__	,   return_tensors="""pt"""       )
									self.assertIsInstance(__magic_name__	,   __magic_name__       )
									self.assertEqual(batch.input_ids.shape	,   (2, 1024)       )
							@require_torch
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							["""A long paragraph for summarization."""]
								a		       =							[
								    """Summary of the text.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a		       =							tokenizer(__magic_name__	,   return_tensors="""pt"""       )
									a		       =							tokenizer(text_target=__magic_name__	,   return_tensors="""pt"""       )
									a		       =							inputs["""input_ids"""]
									a		       =							targets["""input_ids"""]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								pass





							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
										a		       =							self.rust_tokenizer_class.from_pretrained(__magic_name__	,   **__magic_name__       )
										a		       =							self.tokenizer_class.from_pretrained(__magic_name__	,   **__magic_name__       )
										a		       =							"""A, <mask> AllenNLP sentence."""
										a		       =							tokenizer_r.encode_plus(__magic_name__	,   add_special_tokens=__magic_name__	,   return_token_type_ids=__magic_name__       )
										a		       =							tokenizer_p.encode_plus(__magic_name__	,   add_special_tokens=__magic_name__	,   return_token_type_ids=__magic_name__       )

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r["""token_type_ids"""]       )	,   sum(tokens_p["""token_type_ids"""]       )       )

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r["""attention_mask"""]       ) / len(tokens_r["""attention_mask"""]       )	,   sum(tokens_p["""attention_mask"""]       ) / len(tokens_p["""attention_mask"""]       )	,   )

										a		       =							tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]       )
										a		       =							tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]       )

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p["""input_ids"""]	,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )
										self.assertSequenceEqual(tokens_r["""input_ids"""]	,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )

										self.assertSequenceEqual(
										    __magic_name__	,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )
										self.assertSequenceEqual(
										    __magic_name__	,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

