




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     snake_case__							(_UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Tuple							 =					RobertaTokenizer
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					RobertaTokenizerFast
				SCREAMING_SNAKE_CASE_       :							int							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					{"""cls_token""": """<s>"""}





				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a    =			[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							a    =			dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase							)							)							)							)
							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(__lowerCamelCase							) + "\n"							)
							with open(self.merges_file					,      "w"					,      encoding="utf-8"							) as fp:
										fp.write("\n".join(__lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Any					,      **__lowerCamelCase				:     str							)					->     Any:
							kwargs.update(self.special_tokens_map							)
							return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      **__lowerCamelCase				:     List[Any]							)					->     List[Any]:
							kwargs.update(self.special_tokens_map							)
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			"lower newer"
							a    =			"lower newer"
							return input_text, output_text





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Optional[int]:
							a    =			self.tokenizer_class(self.vocab_file					,      self.merges_file					,      **self.special_tokens_map							)
							a    =			"lower newer"
							a    =			["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
							a    =			tokenizer.tokenize(__lowerCamelCase							)  # , add_prefix_space=True)
							self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokens + [tokenizer.unk_token]
							a    =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							)					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Tuple:
							a    =			self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("Hello world!"					,      add_special_tokens=__lowerCamelCase							)					,      [0, 3_14_14, 2_32, 3_28, 2]							)
							self.assertListEqual(
							    tokenizer.encode("Hello world! cécé herlolip 418"					,      add_special_tokens=__lowerCamelCase							)					,      [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]					,      )





				@slow
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     List[Any]:
							a    =			self.tokenizer_class.from_pretrained("roberta-base"							)

							a    =			tokenizer.encode("sequence builders"					,      add_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.encode("multi-sequence build"					,      add_special_tokens=__lowerCamelCase							)

							a    =			tokenizer.encode(
							    "sequence builders"					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.encode(
							    "sequence builders"					,      "multi-sequence build"					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)

							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a    =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase					,      __lowerCamelCase							)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Union[str, Any]:
							a    =			self.get_tokenizer()

							a    =			"Encode this sequence."
							a    =			tokenizer.byte_encoder[" ".encode("utf-8"							)[0]]

							# Testing encoder arguments
							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
							self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)

							tokenizer.add_special_tokens({"bos_token": "<s>"}							)
							a    =			tokenizer.encode(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)

							# Testing spaces after special tokens
							a    =			"<mask>"
							tokenizer.add_special_tokens(
							    {"mask_token": AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							)}							)  # mask token has a left space
							a    =			tokenizer.convert_tokens_to_ids(__lowerCamelCase							)

							a    =			"Encode <mask> sequence"
							a    =			"Encode <mask>sequence"

							a    =			tokenizer.encode(__lowerCamelCase							)
							a    =			encoded.index(__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertEqual(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			tokenizer.encode(__lowerCamelCase							)
							a    =			encoded.index(__lowerCamelCase							)
							a    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
							self.assertNotEqual(__lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							pass





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Any:
							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(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase							)
													a    =			"A, <mask> AllenNLP sentence."
													a    =			tokenizer_r.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase							)
													a    =			tokenizer_p.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase							)

													# 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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
													self.assertSequenceEqual(tokens_r["input_ids"]					,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

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





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[int]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]					,      repeat=2							):
										a    =			self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)

										a    =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
										a    =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

										self.assertEqual(pre_tokenizer_state["add_prefix_space"]					,      __lowerCamelCase							)

										self.assertEqual(post_processor_state["add_prefix_space"]					,      __lowerCamelCase							)
										self.assertEqual(post_processor_state["trim_offsets"]					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a    =			"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													a    =			f"""{text_of_1_token} {text_of_1_token}"""

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							) + 1, len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (len(__lowerCamelCase							), len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			f""" {text}"""

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (1, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							) + 1, 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )

													a    =			self.rust_tokenizer_class.from_pretrained(
													    __lowerCamelCase					,      use_fast=__lowerCamelCase					,      add_prefix_space=__lowerCamelCase					,      trim_offsets=__lowerCamelCase							)
													a    =			tokenizer_r(__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase							)
													self.assertEqual(encoding.offset_mapping[0]					,      (0, 1 + len(__lowerCamelCase							))							)
													self.assertEqual(
													    encoding.offset_mapping[1]					,      (1 + len(__lowerCamelCase							), 1 + len(__lowerCamelCase							) + 1 + len(__lowerCamelCase							))					,      )












def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			[chr(i + 65   ) for i in range(26   )]
			# Remove duplicate characters from key
			a    =			remove_duplicates(key.upper()   )
			a    =			len(A   )
			# First fill cipher with key characters
			a    =			{alphabet[i]: char for i, char in enumerate(A   )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(A   ),  26   ):
						a    =			alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									a    =			alphabet[i - offset]
						a    =			char
			return cipher_alphabet




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			return "".join(cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			a    =			{v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			input("Enter message to encode or decode: "   ).strip()
			a    =			input("Enter keyword: "   ).strip()
			a    =			input("Encipher or decipher? E/D:"   ).strip()[0].lower()
			try:
						a    =			{"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"   )
			a    =			create_cipher_map(A   )
			print(func(A,  A   )   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






