



"""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     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@slow
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Optional[Any]:
											snake_case_			:int						=      XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
											model.to(_UpperCAmelCase						)

											# encoder-decoder outputs
											snake_case_			:Optional[Any]						=      torch.tensor([[17, 96_208, 103_471, 2]]						).to(_UpperCAmelCase						)
											snake_case_			:Optional[int]						=      torch.tensor(
											    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(_UpperCAmelCase						)
											snake_case_			:List[str]						=      model(
											    input_ids=_UpperCAmelCase						,					attention_mask=_UpperCAmelCase						,					encoder_outputs=_UpperCAmelCase						,					decoder_input_ids=_UpperCAmelCase						)
											snake_case_			:Optional[Any]						=      output[0]
											snake_case_			:int						=      torch.Size((1, 14, 250_012)						)
											self.assertEqual(output_predited_logis.shape						,					_UpperCAmelCase						)
											snake_case_			:List[str]						=      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(_UpperCAmelCase						)
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]						,					_UpperCAmelCase						,					atol=1E-4						)						)

											# encoder outputs
											snake_case_			:int						=      model.prophetnet.encoder(_UpperCAmelCase						)[0]
											snake_case_			: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(_UpperCAmelCase						)
											snake_case_			:int						=      torch.Size((1, 4, 1_024)						)
											self.assertEqual(encoder_outputs.shape						,					_UpperCAmelCase						)
											self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]						,					_UpperCAmelCase						,					atol=1E-4						)						)

											# decoder outputs
											snake_case_			:Optional[Any]						=      model.prophetnet.decoder(
											    _UpperCAmelCase						,					encoder_hidden_states=_UpperCAmelCase						,					)
											snake_case_			:Union[str, Any]						=      decoder_outputs[1].view(1						,					model.config.ngram						,					14						,					-1						)
											snake_case_			:Any						=      model.lm_head(_UpperCAmelCase						)
											snake_case_			:List[Any]						=      predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]						,					_UpperCAmelCase						,					atol=1E-4						)						)






					@slow
					def        lowerCAmelCase_    (	self:							Any						)       ->     Union[str, Any]:
											snake_case_			:str						=      XLMProphetNetForConditionalGeneration.from_pretrained(
											    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
											model.to(_UpperCAmelCase						)

											snake_case_			:int						=      torch.tensor([[17, 96_208, 103_471, 2]]						).to(_UpperCAmelCase						)
											snake_case_			:List[str]						=      torch.tensor(
											    [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(_UpperCAmelCase						)
											snake_case_			:int						=      model(
											    input_ids=_UpperCAmelCase						,					attention_mask=_UpperCAmelCase						,					encoder_outputs=_UpperCAmelCase						,					decoder_input_ids=_UpperCAmelCase						)
											snake_case_			:Optional[Any]						=      output[0]
											snake_case_			:Dict						=      torch.Size((1, 14, 250_012)						)
											self.assertEqual(output_predited_logis.shape						,					_UpperCAmelCase						)
											# compare the actual values for a slice.
											snake_case_			:Any						=      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(_UpperCAmelCase						)

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






					@slow
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											snake_case_			:Optional[int]						=      XLMProphetNetForConditionalGeneration.from_pretrained(
											    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
											model.to(_UpperCAmelCase						)
											snake_case_			:Any						=      512

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

											snake_case_			:Tuple						=      (
											    "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_			:int						=      (
											    "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_			:List[Any]						=      (
											    "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_			:Dict						=      tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]						,					padding=_UpperCAmelCase						,					max_length=255						,					return_tensors="""pt"""						).input_ids
											snake_case_			:List[str]						=      input_ids.to(_UpperCAmelCase						)

											snake_case_			:int						=      model.generate(
											    _UpperCAmelCase						,					num_beams=10						,					length_penalty=1.0						,					no_repeat_ngram_size=3						,					early_stopping=_UpperCAmelCase						)
											snake_case_			:int						=      [tokenizer.decode(_UpperCAmelCase						,					skip_special_tokens=_UpperCAmelCase						) for g in summary_ids]
											snake_case_			:Optional[int]						=      "Microsoft to end Windows 7 free support after January 14, 2020"
											snake_case_			:Any						=      "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
											snake_case_			:Union[str, Any]						=      "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]						,					_UpperCAmelCase						,					)

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





"""simple docstring"""
from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
						import pyximport

						pyximport.install(setup_args={"include_dirs": np.get_include()})
						from . import algos_graphormer  # noqa E402
def        A_  (      _lowercase, _lowercase = 512     ):







						'''simple docstring'''


						snake_case_			:int						=      x.shape[1] if len(x.shape     ) > 1 else 1
						snake_case_			:List[Any]						=      1 + np.arange(0, feature_num * offset, _lowercase, dtype=np.intaa     )
						snake_case_			:int						=      x + feature_offset
						return x
def        A_  (      _lowercase, _lowercase=True     ):







						'''simple docstring'''


						requires_backends(_lowercase, ["""cython"""]     )

						if keep_features and "edge_attr" in item.keys():  # edge_attr
												snake_case_			:Tuple						=      np.asarray(item["""edge_attr"""], dtype=np.intaa     )
						else:
												snake_case_			:List[str]						=      np.ones((len(item["""edge_index"""][0]     ), 1), dtype=np.intaa     )  # same embedding for all

						if keep_features and "node_feat" in item.keys():  # input_nodes
												snake_case_			:Dict						=      np.asarray(item["""node_feat"""], dtype=np.intaa     )
						else:
												snake_case_			:Tuple						=      np.ones((item["""num_nodes"""], 1), dtype=np.intaa     )  # same embedding for all

						snake_case_			:Any						=      np.asarray(item["""edge_index"""], dtype=np.intaa     )

						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1
						snake_case_			:str						=      item["""num_nodes"""]

						if len(edge_attr.shape     ) == 1:
												snake_case_			:Any						=      edge_attr[:, None]
						snake_case_			:Dict						=      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]], dtype=np.intaa     )
						snake_case_			:str						=      convert_to_single_emb(_lowercase     ) + 1

						# node adj matrix [num_nodes, num_nodes] bool
						snake_case_			:List[str]						=      np.zeros([num_nodes, num_nodes], dtype=_lowercase     )
						snake_case_			:int						=      True

						snake_case_,	snake_case_			:Tuple						=      algos_graphormer.floyd_warshall(_lowercase     )
						snake_case_			:List[str]						=      np.amax(_lowercase     )

						snake_case_			:Dict						=      algos_graphormer.gen_edge_input(_lowercase, _lowercase, _lowercase     )
						snake_case_			:Tuple						=      np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single     )  # with graph token

						# combine
						snake_case_			:str						=      input_nodes + 1  # we shift all indices by one for padding
						snake_case_			:str						=      attn_bias
						snake_case_			:Optional[Any]						=      attn_edge_type
						snake_case_			:Any						=      shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
						snake_case_			:int						=      np.sum(_lowercase, axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
						snake_case_			:List[str]						=      item["""in_degree"""]  # for undirected graph
						snake_case_			:List[str]						=      input_edges + 1  # we shift all indices by one for padding
						if "labels" not in item:
												snake_case_			:Optional[Any]						=      item["""y"""]

						return item

class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							Optional[int]=20						,					snake_case:							List[Any]=False						)       ->     Union[str, Any]:
											if not is_cython_available():
																	raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""						)

											snake_case_			:List[str]						=      spatial_pos_max
											snake_case_			:int						=      on_the_fly_processing






					def __call__(	self:							str						,					snake_case:							List[dict]						)       ->     Dict[str, Any]:
											if self.on_the_fly_processing:
																	snake_case_			:int						=      [preprocess_item(snake_case						) for i in features]

											if not isinstance(features[0]						,					snake_case						):
																	snake_case_			:Tuple						=      [vars(snake_case						) for f in features]
											snake_case_			:Any						=      {}

											snake_case_			:Dict						=      max(len(i["""input_nodes"""]						) for i in features						)
											snake_case_			:List[Any]						=      len(features[0]["""input_nodes"""][0]						)
											snake_case_			:Dict						=      len(features[0]["""attn_edge_type"""][0][0]						)
											snake_case_			:List[Any]						=      max(len(i["""input_edges"""][0][0]						) for i in features						)
											snake_case_			:Any						=      len(features[0]["""input_edges"""][0][0][0]						)
											snake_case_			:List[str]						=      len(snake_case						)

											snake_case_			:Union[str, Any]						=      torch.zeros(snake_case						,					max_node_num + 1						,					max_node_num + 1						,					dtype=torch.float						)
											snake_case_			:int						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Optional[Any]						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Tuple						=      torch.zeros(snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Dict						=      torch.zeros(snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)
											snake_case_			:Union[str, Any]						=      torch.zeros(
											    snake_case						,					snake_case						,					snake_case						,					snake_case						,					snake_case						,					dtype=torch.long						)

											for ix, f in enumerate(snake_case						):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																							snake_case_			:List[str]						=      torch.tensor(f[k]						)

																	if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]						) > 0:
																							snake_case_			:Union[str, Any]						=      float("""-inf"""						)

																	snake_case_			:Optional[Any]						=      f["""attn_bias"""]
																	snake_case_			:int						=      f[
																	    """attn_edge_type"""
																	]
																	snake_case_			:Dict						=      f["""spatial_pos"""]
																	snake_case_			:Tuple						=      f["""in_degree"""]
																	snake_case_			:List[Any]						=      f["""input_nodes"""]
																	snake_case_			:List[Any]						=      f["""input_edges"""]

											snake_case_			:int						=      batch["""in_degree"""]

											snake_case_			:Any						=      features[0]["""labels"""]
											if len(snake_case						) == 1:  # one task
																	if isinstance(sample[0]						,					snake_case						):  # regression
																							snake_case_			:Dict						=      torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
																	else:  # binary classification
																							snake_case_			:str						=      torch.from_numpy(np.concatenate([i["""labels"""] for i in features]						)						)
											else:  # multi task classification, left to float to keep the NaNs
																	snake_case_			:Optional[int]						=      torch.from_numpy(np.stack([i["""labels"""] for i in features]						,					axis=0						)						)

											return batch
