






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        a__      (	tf.keras.layers.Layer		):






		def __init__(      self							, lowercase__							, lowercase__							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = "longest"							, lowercase__ = True							, lowercase__ = 512							, lowercase__ = None							, lowercase__ = True							, lowercase__ = True							, lowercase__ = True							, **lowercase__							, )				->				Union[str, Any]:
				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 					_lowerCamelCase					(      cls							, lowercase__							, **lowercase__						)				->				List[Any]:  # noqa: F821
				__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 lowercase__						: 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 					_lowerCamelCase					(      cls							, lowercase__							, *lowercase__							, **lowercase__						)				->				int:
				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 					_lowerCamelCase					(      self							, lowercase__						)				->				Any:
				if self.do_lower_case:
						__A    =							case_fold_utfa(_lowerCamelCase						)
				__A    =							self.tf_tokenizer.tokenize(_lowerCamelCase						)
				return tokens.merge_dims(1							, -1						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=None							, lowercase__=None							, lowercase__=None							, lowercase__=None							, lowercase__=None							, lowercase__=None							, lowercase__=None							, )				->				Optional[int]:
				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 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				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,
				}









"""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      ):







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'			)
      model.to(_lowerCamelCase			)

      # encoder-decoder outputs
      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			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			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			torch.Size((1, 1_4, 2_5_0_0_1_2)			)
      self.assertEqual(output_predited_logis.shape				,						_lowerCamelCase			)
      lowercase				       =			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
      lowercase				       =			model.prophetnet.encoder(_lowerCamelCase			)[0]
      lowercase				       =			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			)
      lowercase				       =			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
      lowercase				       =			model.prophetnet.decoder(
          _lowerCamelCase				,						encoder_hidden_states=_lowerCamelCase				,						)
      lowercase				       =			decoder_outputs[1].view(1				,						model.config.ngram				,						1_4				,						-1			)
      lowercase				       =			model.lm_head(_lowerCamelCase			)
      lowercase				       =			predicting_streams_logits[:, 0]
      self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]				,						_lowerCamelCase				,						atol=1e-4			)			)







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)

      lowercase				       =			torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]			).to(_lowerCamelCase			)
      lowercase				       =			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			)
      lowercase				       =			model(
          input_ids=_lowerCamelCase				,						attention_mask=_lowerCamelCase				,						encoder_outputs=_lowerCamelCase				,						decoder_input_ids=_lowerCamelCase			)
      lowercase				       =			output[0]
      lowercase				       =			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.
      lowercase				       =			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			):
      lowercase				       =			XLMProphetNetForConditionalGeneration.from_pretrained(
          'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)
      model.to(_lowerCamelCase			)
      lowercase				       =			5_1_2

      lowercase				       =			XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'			)

      lowercase				       =			(
          '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.'
      )
      lowercase				       =			(
          '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.'
      )
      lowercase				       =			(
          '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。'
      )

      lowercase				       =			tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]				,						padding=_lowerCamelCase				,						max_length=2_5_5				,						return_tensors='pt'			).input_ids
      lowercase				       =			input_ids.to(_lowerCamelCase			)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1_0				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[tokenizer.decode(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids]
      lowercase				       =			'Microsoft to end Windows 7 free support after January 14, 2020'
      lowercase				       =			'Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'
      lowercase				       =			'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				,						)

      lowercase				       =			model.generate(
          _lowerCamelCase				,						num_beams=1				,						length_penalty=1.0				,						no_repeat_ngram_size=3				,						early_stopping=_lowerCamelCase			)
      lowercase				       =			[
          tokenizer.convert_ids_to_tokens(_lowerCamelCase				,						skip_special_tokens=_lowerCamelCase			) for g in summary_ids_beama
      ]
      lowercase				       =			'▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'.split(' '			)
      lowercase				       =			'▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'.split(
          ' '			)
      lowercase				       =			'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				,						)


