code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  lowerCAmelCase					 =	{
      '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  class    A					(     snake_case_				):
  							UpperCamelCase_     :							Any ='''mbart'''
  							UpperCamelCase_     :							Optional[int] =['''past_key_values''']
  							UpperCamelCase_     :							Tuple ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  							def __init__(self   ,       lowerCAmelCase=5_0_2_6_5   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=1_2   ,       lowerCAmelCase=4_0_9_6   ,       lowerCAmelCase=1_6   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=True   ,       lowerCAmelCase=True   ,       lowerCAmelCase="gelu"   ,       lowerCAmelCase=1_0_2_4   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=0.02   ,       lowerCAmelCase=0.0   ,       lowerCAmelCase=False   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=2   ,       **lowerCAmelCase   ,       ):
  									__lowercase=    vocab_size
  									__lowercase=    max_position_embeddings
  									__lowercase=    d_model
  									__lowercase=    encoder_ffn_dim
  									__lowercase=    encoder_layers
  									__lowercase=    encoder_attention_heads
  									__lowercase=    decoder_ffn_dim
  									__lowercase=    decoder_layers
  									__lowercase=    decoder_attention_heads
  									__lowercase=    dropout
  									__lowercase=    attention_dropout
  									__lowercase=    activation_dropout
  									__lowercase=    activation_function
  									__lowercase=    init_std
  									__lowercase=    encoder_layerdrop
  									__lowercase=    decoder_layerdrop
  									__lowercase=    classifier_dropout
  									__lowercase=    use_cache
  									__lowercase=    encoder_layers
  									__lowercase=    scale_embedding  # scale factor will be sqrt(d_model) if True
  									super().__init__(
  									    pad_token_id=lowerCAmelCase   ,       bos_token_id=lowerCAmelCase   ,       eos_token_id=lowerCAmelCase   ,       is_encoder_decoder=lowerCAmelCase   ,       forced_eos_token_id=lowerCAmelCase   ,       **lowerCAmelCase   ,       )
  
  
  
  
  
  class    A					(     snake_case_				):
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  
  											if self.use_past:
  													__lowercase=    {0: 'batch'}
  													__lowercase=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
  											else:
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  													__lowercase=    {0: 'batch', 1: 'decoder_sequence'}
  
  											if self.use_past:
  													self.fill_with_past_key_values_(lowerCAmelCase   ,       direction='inputs'      )
  									elif self.task == "causal-lm":
  											# TODO: figure this case out.
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											    ]      )
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									else:
  											__lowercase=    OrderedDict(
  											    [
  											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
  											        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
  											        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
  											    ]      )
  
  									return common_inputs
  
  
  							@property
  							def     _A (self      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super().outputs
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      ).outputs
  											if self.use_past:
  													__lowercase,							__lowercase=    self.num_layers
  													for i in range(lowerCAmelCase      ):
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  															__lowercase=    {0: 'batch', 2: 'past_sequence + sequence'}
  									return common_outputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									# Generate decoder inputs
  									__lowercase=    seq_length if not self.use_past else 1
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  									__lowercase=    dict(**lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											__lowercase=    common_inputs['decoder_input_ids'].shape[1]
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    encoder_seq_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  											__lowercase=    decoder_seq_length + 3
  											__lowercase=    (
  											    batch,
  											    num_decoder_attention_heads,
  											    decoder_past_length,
  											    self._config.hidden_size // num_decoder_attention_heads,
  											)
  
  											__lowercase=    torch.cat(
  											    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase      )]   ,       dim=1      )
  
  											__lowercase=    []
  											# If the number of encoder and decoder layers are present in the model configuration, both are considered
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase=    min(lowerCAmelCase   ,       lowerCAmelCase      )
  											__lowercase=    max(lowerCAmelCase   ,       lowerCAmelCase      ) - min_num_layers
  											__lowercase=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
  
  											for _ in range(lowerCAmelCase      ):
  													common_inputs["past_key_values"].append(
  													    (
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													        torch.zeros(lowerCAmelCase      ),
  													    )      )
  											# TODO: test this.
  											__lowercase=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
  											for _ in range(lowerCAmelCase   ,       lowerCAmelCase      ):
  													common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      ))      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  									if self.use_past:
  											if not is_torch_available():
  													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
  											else:
  													import torch
  											__lowercase,							__lowercase=    common_inputs['input_ids'].shape
  											# Not using the same length for past_key_values
  											__lowercase=    seqlen + 2
  											__lowercase,							__lowercase=    self.num_layers
  											__lowercase,							__lowercase=    self.num_attention_heads
  											__lowercase=    (
  											    batch,
  											    num_encoder_attention_heads,
  											    past_key_values_length,
  											    self._config.hidden_size // num_encoder_attention_heads,
  											)
  
  											__lowercase=    common_inputs['attention_mask'].dtype
  											__lowercase=    torch.cat(
  											    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase   ,       lowerCAmelCase   ,       dtype=lowerCAmelCase      )]   ,       dim=1      )
  											__lowercase=    [
  											    (torch.zeros(lowerCAmelCase      ), torch.zeros(lowerCAmelCase      )) for _ in range(lowerCAmelCase      )
  											]
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									# Copied from OnnxConfig.generate_dummy_inputs
  									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_batch   ,       num_token_to_add=0      )
  
  									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  									__lowercase=    tokenizer.num_special_tokens_to_add(lowerCAmelCase      )
  									__lowercase=    compute_effective_axis_dimension(
  									    lowerCAmelCase   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=lowerCAmelCase      )
  
  									# Generate dummy inputs according to compute batch and sequence
  									__lowercase=    [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
  									__lowercase=    dict(tokenizer(lowerCAmelCase   ,       return_tensors=lowerCAmelCase      )      )
  									return common_inputs
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = -1   ,       lowerCAmelCase = False   ,       lowerCAmelCase = None   ,       ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									elif self.task == "causal-lm":
  											__lowercase=    self._generate_dummy_inputs_for_causal_lm(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  									else:
  											__lowercase=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  											    lowerCAmelCase   ,       batch_size=lowerCAmelCase   ,       seq_length=lowerCAmelCase   ,       is_pair=lowerCAmelCase   ,       framework=lowerCAmelCase      )
  
  									return common_inputs
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									if self.task in ["default", "seq2seq-lm"]:
  											__lowercase=    super()._flatten_past_key_values_(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									else:
  											__lowercase=    super(lowerCAmelCase   ,       self      )._flatten_past_key_values_(
  											    lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
style_context: 
  
  
  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     _A (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(
  									    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]      ).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(
  									    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]      ).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     _A (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(
  									    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]      ).to(lowerCAmelCase      )
  
  									self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,       lowerCAmelCase   ,       atol=1E-4      )      )
  
  
  
  
  
  
  
  							@slow
  							def     _A (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   ,       )
  
  
  
  
  
  
  