
"""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						)				->  Tuple:
				_lowerCAmelCase			=XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
				model.to(__UpperCAmelCase						)

				# encoder-decoder outputs
				_lowerCAmelCase			=torch.tensor([[17, 9_62_08, 10_34_71, 2]]						).to(__UpperCAmelCase						)
				_lowerCAmelCase			=torch.tensor(
				    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]						).to(__UpperCAmelCase						)
				_lowerCAmelCase			=model(
				    input_ids=__UpperCAmelCase  ,  attention_mask=__UpperCAmelCase  ,  encoder_outputs=__UpperCAmelCase  ,  decoder_input_ids=__UpperCAmelCase						)
				_lowerCAmelCase			=output[0]
				_lowerCAmelCase			=torch.Size((1, 14, 25_00_12)						)
				self.assertEqual(output_predited_logis.shape  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=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
				_lowerCAmelCase			=model.prophetnet.encoder(__UpperCAmelCase						)[0]
				_lowerCAmelCase			=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						)
				_lowerCAmelCase			=torch.Size((1, 4, 10_24)						)
				self.assertEqual(encoder_outputs.shape  ,  __UpperCAmelCase						)
				self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]  ,  __UpperCAmelCase  ,  atol=1e-4						)						)

				# decoder outputs
				_lowerCAmelCase			=model.prophetnet.decoder(
				    __UpperCAmelCase  ,  encoder_hidden_states=__UpperCAmelCase  ,  )
				_lowerCAmelCase			=decoder_outputs[1].view(1  ,  model.config.ngram  ,  14  ,  -1						)
				_lowerCAmelCase			=model.lm_head(__UpperCAmelCase						)
				_lowerCAmelCase			=predicting_streams_logits[:, 0]
				self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]  ,  __UpperCAmelCase  ,  atol=1e-4						)						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Dict:
				_lowerCAmelCase			=XLMProphetNetForConditionalGeneration.from_pretrained(
				    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
				model.to(__UpperCAmelCase						)

				_lowerCAmelCase			=torch.tensor([[17, 9_62_08, 10_34_71, 2]]						).to(__UpperCAmelCase						)
				_lowerCAmelCase			=torch.tensor(
				    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]						).to(__UpperCAmelCase						)
				_lowerCAmelCase			=model(
				    input_ids=__UpperCAmelCase  ,  attention_mask=__UpperCAmelCase  ,  encoder_outputs=__UpperCAmelCase  ,  decoder_input_ids=__UpperCAmelCase						)
				_lowerCAmelCase			=output[0]
				_lowerCAmelCase			=torch.Size((1, 14, 25_00_12)						)
				self.assertEqual(output_predited_logis.shape  ,  __UpperCAmelCase						)
				# compare the actual values for a slice.
				_lowerCAmelCase			=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						)				->  str:
				_lowerCAmelCase			=XLMProphetNetForConditionalGeneration.from_pretrained(
				    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
				model.to(__UpperCAmelCase						)
				_lowerCAmelCase			=5_12

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

				_lowerCAmelCase			=(
				    """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."""
				)
				_lowerCAmelCase			=(
				    """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."""
				)
				_lowerCAmelCase			=(
				    """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。"""
				)

				_lowerCAmelCase			=tokenizer(
				    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]  ,  padding=__UpperCAmelCase  ,  max_length=2_55  ,  return_tensors="""pt"""						).input_ids
				_lowerCAmelCase			=input_ids.to(__UpperCAmelCase						)

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

				_lowerCAmelCase			=model.generate(
				    __UpperCAmelCase  ,  num_beams=1  ,  length_penalty=1.0  ,  no_repeat_ngram_size=3  ,  early_stopping=__UpperCAmelCase						)
				_lowerCAmelCase			=[
				    tokenizer.convert_ids_to_tokens(__UpperCAmelCase  ,  skip_special_tokens=__UpperCAmelCase						) for g in summary_ids_beama
				]
				_lowerCAmelCase			="""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
				_lowerCAmelCase			="""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
				    """ """						)
				_lowerCAmelCase			="""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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A 		= logging.get_logger(__name__)

__A 		= {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					_lowerCAmelCase			={0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



