


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     ):

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Union[str, Any]				      =     XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
            model.to(lowerCamelCase_     )

            # encoder-decoder outputs
            a_     :  Optional[int]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  List[Any]				      =     torch.tensor(
                [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowerCamelCase_     )
            a_     :  Any				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  List[str]				      =     output[0]
            a_     :  Any				      =     torch.Size((1, 1_4, 2_5_0_0_1_2)     )
            self.assertEqual(output_predited_logis.shape   ,				lowerCamelCase_     )
            a_     :  int				      =     torch.tensor(
                [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]     ).to(lowerCamelCase_     )
            self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

            # encoder outputs
            a_     :  Dict				      =     model.prophetnet.encoder(lowerCamelCase_     )[0]
            a_     :  Optional[int]				      =     torch.tensor(
                [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]     ).to(lowerCamelCase_     )
            a_     :  Tuple				      =     torch.Size((1, 4, 1_0_2_4)     )
            self.assertEqual(encoder_outputs.shape   ,				lowerCamelCase_     )
            self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

            # decoder outputs
            a_     :  Optional[Any]				      =     model.prophetnet.decoder(
                lowerCamelCase_   ,				encoder_hidden_states=lowerCamelCase_   ,				)
            a_     :  Union[str, Any]				      =     decoder_outputs[1].view(1   ,				model.config.ngram   ,				1_4   ,				-1     )
            a_     :  int				      =     model.lm_head(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     predicting_streams_logits[:, 0]
            self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  List[Any]				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )

            a_     :  List[Any]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  Union[str, Any]				      =     torch.tensor(
                [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowerCamelCase_     )
            a_     :  List[Any]				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  str				      =     output[0]
            a_     :  str				      =     torch.Size((1, 1_4, 2_5_0_0_1_2)     )
            self.assertEqual(output_predited_logis.shape   ,				lowerCamelCase_     )
            # compare the actual values for a slice.
            a_     :  int				      =     torch.tensor(
                [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]     ).to(lowerCamelCase_     )

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

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  str				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )
            a_     :  Tuple				      =     5_1_2

            a_     :  Optional[Any]				      =     XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )

            a_     :  Dict				      =     (
                """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."""
            )
            a_     :  List[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."""
            )
            a_     :  Optional[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。"""
            )

            a_     :  Union[str, Any]				      =     tokenizer(
                [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,				padding=lowerCamelCase_   ,				max_length=2_5_5   ,				return_tensors="""pt"""     ).input_ids
            a_     :  str				      =     input_ids.to(lowerCamelCase_     )

            a_     :  int				      =     model.generate(
                lowerCamelCase_   ,				num_beams=1_0   ,				length_penalty=1.0   ,				no_repeat_ngram_size=3   ,				early_stopping=lowerCamelCase_     )
            a_     :  int				      =     [tokenizer.decode(lowerCamelCase_   ,				skip_special_tokens=lowerCamelCase_     ) for g in summary_ids]
            a_     :  str				      =     """Microsoft to end Windows 7 free support after January 14, 2020"""
            a_     :  Dict				      =     """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
            a_     :  Dict				      =     """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_   ,				)

            a_     :  int				      =     model.generate(
                lowerCamelCase_   ,				num_beams=1   ,				length_penalty=1.0   ,				no_repeat_ngram_size=3   ,				early_stopping=lowerCamelCase_     )
            a_     :  List[str]				      =     [
                tokenizer.convert_ids_to_tokens(lowerCamelCase_   ,				skip_special_tokens=lowerCamelCase_     ) for g in summary_ids_beama
            ]
            a_     :  int				      =     """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """     )
            a_     :  str				      =     """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
                """ """     )
            a_     :  Union[str, Any]				      =     """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_   ,				)








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     A_		:  Any										=  None


A_		:  Tuple										=  logging.get_logger(__name__)

A_		:  int										=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A_		:  List[Any]										=  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

A_		:  Optional[Any]										=  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
A_		:  List[str]										=  ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      VOCAB_FILES_NAMES
   _UpperCAmelCase  						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase  						=      PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase  						=      ['''input_ids''', '''attention_mask''']
   _UpperCAmelCase  						=      MBartaaTokenizer

   _UpperCAmelCase  						=      []
   _UpperCAmelCase  						=      []

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							Tuple=None				, __lowerCAmelCase      :							Optional[Any]=None				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							Union[str, Any]=None				, __lowerCAmelCase      :							Optional[int]="</s>"				, __lowerCAmelCase      :							List[Any]="</s>"				, __lowerCAmelCase      :							Optional[Any]="<s>"				, __lowerCAmelCase      :							Optional[Any]="<unk>"				, __lowerCAmelCase      :							Optional[Any]="<pad>"				, __lowerCAmelCase      :							List[Any]="<mask>"				, **__lowerCAmelCase      :							Dict				, )							->					Optional[int]:


    """simple docstring"""






    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else mask_token

    a							=		kwargs.get("additional_special_tokens"				, []				)
    kwargs["additional_special_tokens"] += [
        code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
    ]

    super().__init__(
        __lowerCAmelCase				, src_lang=__lowerCAmelCase				, tgt_lang=__lowerCAmelCase				, tokenizer_file=__lowerCAmelCase				, eos_token=__lowerCAmelCase				, sep_token=__lowerCAmelCase				, cls_token=__lowerCAmelCase				, unk_token=__lowerCAmelCase				, pad_token=__lowerCAmelCase				, mask_token=__lowerCAmelCase				, **__lowerCAmelCase				, )

    a							=		vocab_file
    a							=		False if not self.vocab_file else True

    a							=		{
        lang_code: self.convert_tokens_to_ids(__lowerCAmelCase				) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    a							=		src_lang if src_lang is not None else "en_XX"
    a							=		tgt_lang
    a							=		self.lang_code_to_id[self._src_lang]
    self.set_src_lang_special_tokens(self._src_lang				)

   @property
   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return self._src_lang

   @src_lang.setter
   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		new_src_lang
    self.set_src_lang_special_tokens(self._src_lang				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[int]				, __lowerCAmelCase      :							Optional[List[int]] = None				)							->					List[int]:


    """simple docstring"""






    if token_ids_a is None:
     return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							str = "en_XX"				, __lowerCAmelCase      :							Optional[List[str]] = None				, __lowerCAmelCase      :							str = "ro_RO"				, **__lowerCAmelCase      :							Dict				, )							->					BatchEncoding:


    """simple docstring"""






    a							=		src_lang
    a							=		tgt_lang
    return super().prepare_seqaseq_batch(__lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)

   def 		A				(   self      :							List[str]				)							->					List[str]:


    """simple docstring"""






    return self.set_src_lang_special_tokens(self.src_lang				)

   def 		A				(   self      :							str				)							->					int:


    """simple docstring"""






    return self.set_tgt_lang_special_tokens(self.tgt_lang				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

    a							=		self.convert_ids_to_tokens(self.prefix_tokens				)
    a							=		self.convert_ids_to_tokens(self.suffix_tokens				)

    a							=		processors.TemplateProcessing(
        single=prefix_tokens_str + ["$A"] + suffix_tokens_str				, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				, self.prefix_tokens + self.suffix_tokens				)				)				, )

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

    a							=		self.convert_ids_to_tokens(self.prefix_tokens				)
    a							=		self.convert_ids_to_tokens(self.suffix_tokens				)

    a							=		processors.TemplateProcessing(
        single=prefix_tokens_str + ["$A"] + suffix_tokens_str				, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				, self.prefix_tokens + self.suffix_tokens				)				)				, )

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str]				, __lowerCAmelCase      :							Optional[str]				, **__lowerCAmelCase      :							Any				)							->					Union[str, Any]:


    """simple docstring"""






    if src_lang is None or tgt_lang is None:
     raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
    a							=		src_lang
    a							=		self(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				, return_tensors=__lowerCAmelCase				, **__lowerCAmelCase				)
    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		tgt_lang_id
    return inputs




   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str] = None				)							->					Tuple[str]:


    """simple docstring"""






    if not self.can_save_slow_tokenizer:
     raise ValueError(
         "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
         "tokenizer."				)

    if not os.path.isdir(__lowerCAmelCase				):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
     return
    a							=		os.path.join(
        __lowerCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

    if os.path.abspath(self.vocab_file				) != os.path.abspath(__lowerCAmelCase				):
     copyfile(self.vocab_file				, __lowerCAmelCase				)

    return (out_vocab_file,)

