


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_   ,				)











"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class    snake_case						(				tf.keras.layers.Layer		):


   '''simple docstring'''

   def __init__(      self					:	List[str],			_lowerCamelCase					:	List,			_lowerCamelCase					:	bool,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	str = "longest",			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	int = 5_12,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     super().__init__()
     if use_fast_bert_tokenizer:
       __A		     =					FastBertTokenizer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case_nfd_strip_accents=_lowerCamelCase,			**_lowerCamelCase    )
     else:
       __A		     =					tf.lookup.StaticVocabularyTable(
           tf.lookup.KeyValueTensorInitializer(
               keys=_lowerCamelCase,			key_dtype=tf.string,			values=tf.range(tf.size(_lowerCamelCase,			out_type=tf.intaa    ),			dtype=tf.intaa    ),			value_dtype=tf.intaa,			),			num_oov_buckets=1,			)
       __A		     =					BertTokenizerLayer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case=_lowerCamelCase,			**_lowerCamelCase    )

     __A		     =					vocab_list
     __A		     =					do_lower_case
     __A		     =					cls_token_id or vocab_list.index('''[CLS]'''    )
     __A		     =					sep_token_id or vocab_list.index('''[SEP]'''    )
     __A		     =					pad_token_id or vocab_list.index('''[PAD]'''    )
     __A		     =					ShrinkLongestTrimmer(max_length - 3,			axis=1    )  # Allow room for special tokens
     __A		     =					max_length
     __A		     =					padding
     __A		     =					truncation
     __A		     =					pad_to_multiple_of
     __A		     =					return_token_type_ids
     __A		     =					return_attention_mask


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	int,			_lowerCamelCase					:	"PreTrainedTokenizerBase",			**_lowerCamelCase					:	List[Any]    ):  # noqa: F821

     '''simple docstring'''
     __A		     =					kwargs.pop('''do_lower_case''',			_lowerCamelCase    )
     __A		     =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     __A		     =					kwargs.pop('''cls_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     __A		     =					kwargs.pop('''sep_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     __A		     =					kwargs.pop('''pad_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     __A		     =					tokenizer.get_vocab()
     __A		     =					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],			key=lambda _lowerCamelCase    : x[1]    )
     __A		     =					[entry[0] for entry in vocab]
     return cls(
         vocab_list=_lowerCamelCase,			do_lower_case=_lowerCamelCase,			cls_token_id=_lowerCamelCase,			sep_token_id=_lowerCamelCase,			pad_token_id=_lowerCamelCase,			**_lowerCamelCase,			)


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Optional[Any],			_lowerCamelCase					:	Union[str, os.PathLike],			*_lowerCamelCase					:	Any,			**_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     try:
       __A		     =					BertTokenizer.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     except:  # noqa: E722
       from .tokenization_bert_fast import BertTokenizerFast

       __A		     =					BertTokenizerFast.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     return cls.from_tokenizer(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if self.do_lower_case:
       __A		     =					case_fold_utfa(_lowerCamelCase    )
     __A		     =					self.tf_tokenizer.tokenize(_lowerCamelCase    )
     return tokens.merge_dims(1,			-1    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Dict=None,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	Union[str, Any]=None,			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	List[Any]=None,			_lowerCamelCase					:	str=None,			_lowerCamelCase					:	Dict=None,			):

     '''simple docstring'''
     if padding is None:
       __A		     =					self.padding
     if padding not in ("longest", "max_length"):
       raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''    )
     if max_length is not None and text_pair is not None:
       # Because we have to instantiate a Trimmer to do it properly
       raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''    )
     if max_length is None:
       __A		     =					self.max_length
     if truncation is None:
       __A		     =					self.truncation
     if pad_to_multiple_of is None:
       __A		     =					self.pad_to_multiple_of
     if return_token_type_ids is None:
       __A		     =					self.return_token_type_ids
     if return_attention_mask is None:
       __A		     =					self.return_attention_mask
     if not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None and not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None:
       if text.shape.rank > 1:
         raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''    )
       if text_pair.shape.rank > 1:
         raise ValueError('''text_pair should not be multidimensional!'''    )
     if text.shape.rank == 2:
       __A     ,       __A		     =					text[:, 0], text[:, 1]
     __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
     if text_pair is None:  # Unpaired text
       if truncation:
         __A		     =					text[:, : max_length - 2]  # Allow room for special tokens
       __A     ,       __A		     =					combine_segments(
           (text,),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     else:  # Paired text
       __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
       if truncation:
         __A     ,       __A		     =					self.paired_trimmer.trim([text, text_pair]    )
       __A     ,       __A		     =					combine_segments(
           (text, text_pair),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     if padding == "longest":
       __A		     =					input_ids.bounding_shape(axis=1    )
       if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         __A		     =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length,			_lowerCamelCase    ))
     else:
       __A		     =					max_length

     __A     ,       __A		     =					pad_model_inputs(_lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
     __A		     =					{'''input_ids''': input_ids}
     if return_attention_mask:
       __A		     =					attention_mask
     if return_token_type_ids:
       __A     ,       __A		     =					pad_model_inputs(
           _lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
       __A		     =					token_type_ids
     return output






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }




