





"""simple docstring"""


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   lowercase_					(  lowercase_					,unittest.TestCase				):
       snake_case__					:				Optional[int]									=  BartTokenizer
       snake_case__					:				List[str]									=  BartTokenizerFast
       snake_case__					:				Tuple									=  True
       snake_case__					:				List[str]									=  filter_roberta_detectors

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

        snake_case__     				=					os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["vocab_file"]  )
        snake_case__     				=					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(snake_case__  ) + "\n"  )
        with open(self.merges_file ,       "w" ,       encoding="utf-8"  ) as fp:
         fp.write("\n".join(snake_case__  )  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname ,       **snake_case__  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,       **snake_case__  )

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        return "lower newer", "lower newer"

       @cached_property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return BartTokenizer.from_pretrained("facebook/bart-large"  )

       @cached_property
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        return BartTokenizerFast.from_pretrained("facebook/bart-large"  )

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

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

         self.assertEqual((2, 9) ,       batch.input_ids.shape  )
         self.assertEqual((2, 9) ,       batch.attention_mask.shape  )
         snake_case__     				=					batch.input_ids.tolist()[0]
         self.assertListEqual(snake_case__ ,       snake_case__  )
         # Test that special tokens are reset

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

       @require_torch
       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        snake_case__     				=					["A long paragraph for summarization."]
        snake_case__     				=					[
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         snake_case__     				=					tokenizer(snake_case__ ,       return_tensors="pt"  )
         snake_case__     				=					tokenizer(text_target=snake_case__ ,       return_tensors="pt"  )
         snake_case__     				=					inputs["input_ids"]
         snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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

          # 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"]  ) ,       )

          snake_case__     				=					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
          snake_case__     				=					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(
              snake_case__ ,       ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
          self.assertSequenceEqual(
              snake_case__ ,       ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )









from typing import Literal

lowerCamelCase					    =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						LETTERS if mode == 'decrypt' else key
    _lowercase     			=						key if mode == 'decrypt' else LETTERS
    _lowercase     			=						''
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            _lowercase     			=						chars_a.find(symbol.upper()   )
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'encrypt'   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'decrypt'   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'Hello World'
    _lowercase     			=						'QWERTYUIOPASDFGHJKLZXCVBNM'
    _lowercase     			=						'decrypt'  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        _lowercase     			=						encrypt_message(_lowercase,   _lowercase   )
    elif mode == "decrypt":
        _lowercase     			=						decrypt_message(_lowercase,   _lowercase   )
    print(f'''Using the key {key}, the {mode}ed message is: {translated}'''   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()
