


'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__  =     logging.get_logger(__name__)

lowerCAmelCase__  =     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__  =     {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

lowerCAmelCase__  =     {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}



@lru_cache()
def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	List[str]          =					(
           list(range(ord("""!"""     )    ,       ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )    ,       ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )    ,       ord("""ÿ"""     ) + 1     )     )
       )
       snake_case__					:	Optional[Any]          =					bs[:]
       snake_case__					:	Optional[Any]          =					0
       for b in range(2**8     ):
              if b not in bs:
                     bs.append(UpperCAmelCase     )
                     cs.append(2**8 + n     )
                     n += 1
       snake_case__					:	str          =					[chr(UpperCAmelCase     ) for n in cs]
       return dict(zip(UpperCAmelCase    ,       UpperCAmelCase     )     )



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	List[str]          =					set()
       snake_case__					:	Tuple          =					word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)     )
              snake_case__					:	List[Any]          =					char
       return pairs







class 					_A						(						UpperCamelCase							):



   '''simple docstring'''






   _lowercase    						= VOCAB_FILES_NAMES
   _lowercase    						= PRETRAINED_VOCAB_FILES_MAP
   _lowercase    						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowercase    						= ['input_ids', 'attention_mask']




   def __init__(	self	:					Optional[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					Union[str, Any]="replace"					, lowerCamelCase	:					Dict="<s>"					, lowerCamelCase	:					Tuple="</s>"					, lowerCamelCase	:					str="</s>"					, lowerCamelCase	:					Optional[int]="<s>"					, lowerCamelCase	:					Union[str, Any]="<unk>"					, lowerCamelCase	:					Any="<pad>"					, lowerCamelCase	:					Optional[int]="<mask>"					, lowerCamelCase	:					str=False					, **lowerCamelCase	:					Optional[Any]					, )->	List[str]:
          snake_case__					:	List[str]          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else bos_token
          snake_case__					:	int          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else eos_token
          snake_case__					:	Dict          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else sep_token
          snake_case__					:	Dict          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else cls_token
          snake_case__					:	Any          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else unk_token
          snake_case__					:	Union[str, Any]          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          snake_case__					:	Tuple          =					AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase						) if isinstance(lowerCamelCase					, lowerCamelCase						) else mask_token

          super().__init__(
              errors=lowerCamelCase					, bos_token=lowerCamelCase					, eos_token=lowerCamelCase					, unk_token=lowerCamelCase					, sep_token=lowerCamelCase					, cls_token=lowerCamelCase					, pad_token=lowerCamelCase					, mask_token=lowerCamelCase					, add_prefix_space=lowerCamelCase					, **lowerCamelCase					, )

          with open(lowerCamelCase					, encoding="""utf-8"""						) as vocab_handle:
                 snake_case__					:	Optional[int]          =					json.load(lowerCamelCase						)
          snake_case__					:	str          =					{v: k for k, v in self.encoder.items()}
          snake_case__					:	List[str]          =					errors  # how to handle errors in decoding
          snake_case__					:	int          =					bytes_to_unicode()
          snake_case__					:	Any          =					{v: k for k, v in self.byte_encoder.items()}
          with open(lowerCamelCase					, encoding="""utf-8"""						) as merges_handle:
                 snake_case__					:	int          =					merges_handle.read().split("""\n"""						)[1:-1]
          snake_case__					:	Any          =					[tuple(merge.split()						) for merge in bpe_merges]
          snake_case__					:	Union[str, Any]          =					dict(zip(lowerCamelCase					, range(len(lowerCamelCase						)						)						)						)
          snake_case__					:	int          =					{}
          snake_case__					:	List[Any]          =					add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          snake_case__					:	str          =					re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)




   @property
   def   __lowerCAmelCase						(	self	:					Dict						)->	List[str]:
          return len(self.encoder						)




   def   __lowerCAmelCase						(	self	:					Any						)->	List[Any]:
          return dict(self.encoder					, **self.added_tokens_encoder						)




   def   __lowerCAmelCase						(	self	:					List[str]					, lowerCamelCase	:					Union[str, Any]						)->	Dict:
          if token in self.cache:
                 return self.cache[token]
          snake_case__					:	str          =					tuple(lowerCamelCase						)
          snake_case__					:	List[str]          =					get_pairs(lowerCamelCase						)

          if not pairs:
                 return token

          while True:
                 snake_case__					:	Optional[int]          =					min(lowerCamelCase					, key=lambda lowerCamelCase						: self.bpe_ranks.get(lowerCamelCase					, float("""inf"""						)						)						)
                 if bigram not in self.bpe_ranks:
                        break
                 snake_case__       ,   snake_case__					:	List[str]          =					bigram
                 snake_case__					:	int          =					[]
                 snake_case__					:	Optional[int]          =					0
                 while i < len(lowerCamelCase						):
                        try:
                               snake_case__					:	Optional[Any]          =					word.index(lowerCamelCase					, lowerCamelCase						)
                        except ValueError:
                               new_word.extend(word[i:]						)
                               break
                        else:
                               new_word.extend(word[i:j]						)
                               snake_case__					:	Any          =					j

                        if word[i] == first and i < len(lowerCamelCase						) - 1 and word[i + 1] == second:
                               new_word.append(first + second						)
                               i += 2
                        else:
                               new_word.append(word[i]						)
                               i += 1
                 snake_case__					:	str          =					tuple(lowerCamelCase						)
                 snake_case__					:	Tuple          =					new_word
                 if len(lowerCamelCase						) == 1:
                        break
                 else:
                        snake_case__					:	List[str]          =					get_pairs(lowerCamelCase						)
          snake_case__					:	List[Any]          =					""" """.join(lowerCamelCase						)
          snake_case__					:	Any          =					word
          return word




   def   __lowerCAmelCase						(	self	:					Optional[int]					, lowerCamelCase	:					Optional[int]						)->	int:
          snake_case__					:	List[Any]          =					[]
          for token in re.findall(self.pat					, lowerCamelCase						):
                 snake_case__					:	Optional[Any]          =					"""""".join(
                     self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase						).split(""" """						)						)
          return bpe_tokens




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					int						)->	Any:
          return self.encoder.get(lowerCamelCase					, self.encoder.get(self.unk_token						)						)




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					str						)->	List[str]:
          return self.decoder.get(lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					str						)->	Tuple:
          snake_case__					:	Tuple          =					"""""".join(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""					, errors=self.errors						)
          return text




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					str					, lowerCamelCase	:					Optional[str] = None						)->	Tuple[str]:
          if not os.path.isdir(lowerCamelCase						):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
                 return
          snake_case__					:	Optional[int]          =					os.path.join(
              lowerCamelCase					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
          snake_case__					:	Union[str, Any]          =					os.path.join(
              lowerCamelCase					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

          with open(lowerCamelCase					, """w"""					, encoding="""utf-8"""						) as f:
                 f.write(json.dumps(self.encoder					, indent=2					, sort_keys=lowerCamelCase					, ensure_ascii=lowerCamelCase						) + """\n"""						)

          snake_case__					:	str          =					0
          with open(lowerCamelCase					, """w"""					, encoding="""utf-8"""						) as writer:
                 writer.write("""#version: 0.2\n"""						)
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					, key=lambda lowerCamelCase						: kv[1]						):
                        if index != token_index:
                               logger.warning(
                                   F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                   """ Please check that the tokenizer is not corrupted!"""						)
                               snake_case__					:	Optional[Any]          =					token_index
                        writer.write(""" """.join(lowerCamelCase						) + """\n"""						)
                        index += 1

          return vocab_file, merge_file




   def   __lowerCAmelCase						(	self	:					List[str]					, lowerCamelCase	:					List[int]					, lowerCamelCase	:					Optional[List[int]] = None						)->	List[int]:
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          snake_case__					:	Optional[Any]          =					[self.cls_token_id]
          snake_case__					:	Optional[int]          =					[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					List[int]					, lowerCamelCase	:					Optional[List[int]] = None					, lowerCamelCase	:					bool = False						)->	List[int]:
          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCamelCase					, token_ids_a=lowerCamelCase					, already_has_special_tokens=lowerCamelCase						)

          if token_ids_a is None:
                 return [1] + ([0] * len(lowerCamelCase						)) + [1]
          return [1] + ([0] * len(lowerCamelCase						)) + [1, 1] + ([0] * len(lowerCamelCase						)) + [1]




   def   __lowerCAmelCase						(	self	:					Union[str, Any]					, lowerCamelCase	:					List[int]					, lowerCamelCase	:					Optional[List[int]] = None						)->	List[int]:
          snake_case__					:	List[str]          =					[self.sep_token_id]
          snake_case__					:	List[str]          =					[self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep						) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




   def   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					int					, lowerCamelCase	:					int=False					, **lowerCamelCase	:					Any						)->	Dict:
          snake_case__					:	Dict          =					kwargs.pop("""add_prefix_space"""					, self.add_prefix_space						)
          if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase						) > 0 and not text[0].isspace()):
                 snake_case__					:	int          =					""" """ + text
          return (text, kwargs)








'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer







@require_torch
class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					Tuple						)->	Optional[int]:
          snake_case__					:	Any          =					XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(lowerCamelCase						)

          # encoder-decoder outputs
          snake_case__					:	Optional[int]          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          snake_case__					:	Optional[int]          =					torch.tensor(
              [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]						).to(lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # encoder outputs
          snake_case__					:	Dict          =					model.prophetnet.encoder(lowerCamelCase						)[0]
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]						).to(lowerCamelCase						)
          snake_case__					:	str          =					torch.Size((1, 4, 1_024)						)
          self.assertEqual(encoder_outputs.shape					, lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # decoder outputs
          snake_case__					:	int          =					model.prophetnet.decoder(
              lowerCamelCase					, encoder_hidden_states=lowerCamelCase					, )
          snake_case__					:	Any          =					decoder_outputs[1].view(1					, model.config.ngram					, 14					, -1						)
          snake_case__					:	Optional[Any]          =					model.lm_head(lowerCamelCase						)
          snake_case__					:	str          =					predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Tuple          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)

          snake_case__					:	str          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	List[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[str]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case__					:	List[str]          =					torch.tensor(
              [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]						).to(lowerCamelCase						)

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          snake_case__					:	Dict          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					512

          snake_case__					:	Any          =					XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

          snake_case__					:	Optional[int]          =					(
              """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
              """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
              """ system will not be able to receive security updates, which could make their computers vulnerable to"""
              """ cyber attacks."""
          )
          snake_case__					:	Union[str, Any]          =					(
              """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
              """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
              """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
              """ к кибератакам."""
          )
          snake_case__					:	int          =					(
              """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
          )

          snake_case__					:	int          =					tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					, padding=lowerCamelCase					, max_length=255					, return_tensors="""pt"""						).input_ids
          snake_case__					:	Optional[int]          =					input_ids.to(lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					model.generate(
              lowerCamelCase					, num_beams=10					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					[tokenizer.decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids]
          snake_case__					:	Optional[Any]          =					"""Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case__					:	Dict          =					"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case__					:	Optional[int]          =					"""微软打算终止对Windows 7操作系统的免费支持"""
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]					, lowerCamelCase					, )

          snake_case__					:	Optional[int]          =					model.generate(
              lowerCamelCase					, num_beams=1					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Tuple          =					[
              tokenizer.convert_ids_to_tokens(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids_beama
          ]
          snake_case__					:	Any          =					"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
          snake_case__					:	Union[str, Any]          =					"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """						)
          snake_case__					:	Tuple          =					"""微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """						)
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]					, lowerCamelCase					, )




