"""simple docstring"""



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__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			Dict           =		RobertaTokenizer
 _snake_case   :			List[str]           =		RobertaTokenizerFast
 _snake_case   :			Any           =		True
 _snake_case   :			Dict           =		{"""cls_token""": """<s>"""}


 def 		a__ (				self		):
        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 		a__ (				self  ,						**lowerCamelCase		):
        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        kwargs.update(self.special_tokens_map		)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  "lower newer"
        __a     				=  "lower newer"
        return input_text, output_text


 def 		a__ (				self		):
        __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 		a__ (				self		):
        __a     				=  self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("Hello world!"  ,						add_special_tokens=lowerCamelCase		)  ,						[0, 31414, 232, 328, 2]		)
        self.assertListEqual(
            tokenizer.encode("Hello world! cécé herlolip 418"  ,						add_special_tokens=lowerCamelCase		)  ,						[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]  ,						)


 @slow
 def 		a__ (				self		):
        __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 		a__ (				self		):
        __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 		a__ (				self		):
        pass


 def 		a__ (				self		):
        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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
                      self.assertSequenceEqual(tokens_r["input_ids"]  ,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 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 		a__ (				self		):
        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 		a__ (				self		):
        # 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		))  ,						)

"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__:Any			=	{
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
SCREAMING_SNAKE_CASE__:Optional[int]			=	"""ETAOINSHRDLCUMWFGYPBVKJXQZ"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def 					_lowerCamelCase(					a							):
       __a     				=  {letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count



def 					_lowerCamelCase(					a							):
       return x[0]



def 					_lowerCamelCase(					a							):
       __a     				=  get_letter_count(a							)
       __a     				=  {
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(a							)

       __a     				=  {}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find ,  reverse=a							)
              __a     				=  "".join(freq_to_letter[freq]							)

       __a     				=  list(freq_to_letter_str.items()							)
       freq_pairs.sort(key=a ,  reverse=a							)

       __a     				=  [freq_pair[1] for freq_pair in freq_pairs]

       return "".join(a							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_frequency_order(a							)
       __a     				=  0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
   import doctest

   doctest.testmod()
