






"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__SCREAMING_SNAKE_CASE      =      logging.getLogger(__name__)




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str			=     field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
					lowercase_						:							Optional[str]			=     field(
					    default=UpperCamelCase      ,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
					lowercase_						:							Optional[str]			=     field(
					    default=UpperCamelCase      ,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
					lowercase_						:							bool			=     field(default=UpperCamelCase      ,    metadata={"""help""": """Set this flag to use fast tokenization."""}	)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					lowercase_						:							Optional[str]			=     field(
					    default=UpperCamelCase      ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,    )


def 					UpperCAmelCase			(				)     ->   List[str]:





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )

						if len(sys.argv   ) == 2 and sys.argv[1].endswith('.json'   ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase      :Optional[int]				  = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												lowerCAmelCase      :Dict				  = parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir   )
						    and os.listdir(training_args.output_dir   )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    ' --overwrite_output_dir to overcome.'   )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,			datefmt='%m/%d/%Y %H:%M:%S'    ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,			)
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'    ,			training_args.local_rank    ,			training_args.device    ,			training_args.n_gpu    ,			bool(training_args.local_rank != -1   )    ,			training_args.fpaa    ,			)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank   ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('Training/evaluation parameters %s'    ,			a__   )

						# Prepare Question-Answering task
						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCAmelCase      :str				  = AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			)
						lowerCAmelCase      :Optional[int]				  = AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			use_fast=a__    ,			)
						lowerCAmelCase      :Union[str, Any]				  = AutoModelForQuestionAnswering.from_pretrained(
						    model_args.model_name_or_path    ,			from_tf=bool('.ckpt' in model_args.model_name_or_path   )    ,			config=a__    ,			cache_dir=model_args.cache_dir    ,			)

						# Get datasets
						lowerCAmelCase      :str				  = hasattr(model.config    ,			'lang2id'   )
						lowerCAmelCase      :Tuple				  = (
						    SquadDataset(
						        a__    ,			tokenizer=a__    ,			is_language_sensitive=a__    ,			cache_dir=model_args.cache_dir   )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase      :Any				  = (
						    SquadDataset(
						        a__    ,			tokenizer=a__    ,			mode='dev'    ,			is_language_sensitive=a__    ,			cache_dir=model_args.cache_dir    ,			)
						    if training_args.do_eval
						    else None
						)

						# Data collator
						lowerCAmelCase      :List[Any]				  = DataCollatorWithPadding(a__    ,			pad_to_multiple_of=8   ) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase      :Union[str, Any]				  = Trainer(
						    model=a__    ,			args=a__    ,			train_dataset=a__    ,			eval_dataset=a__    ,			data_collator=a__    ,			)

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir   )


def 					UpperCAmelCase			(				a__   )     ->   List[str]:





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()







"""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 	__UpperCamelCase    (    unittest.TestCase	):
					@slow
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					Union[str, Any]:
											lowerCAmelCase      :Any				  = XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )
											model.to(UpperCAmelCase      )

											# encoder-decoder outputs
											lowerCAmelCase      :Any				  = torch.tensor([[17, 9_6208, 10_3471, 2]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = model(
											    input_ids=UpperCAmelCase    ,   attention_mask=UpperCAmelCase    ,   encoder_outputs=UpperCAmelCase    ,   decoder_input_ids=UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = output[0]
											lowerCAmelCase      :Any				  = torch.Size((1, 14, 25_0012)      )
											self.assertEqual(output_predited_logis.shape    ,   UpperCAmelCase      )
											lowerCAmelCase      :Optional[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(UpperCAmelCase      )
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )

											# encoder outputs
											lowerCAmelCase      :int				  = model.prophetnet.encoder(UpperCAmelCase      )[0]
											lowerCAmelCase      :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      )
											lowerCAmelCase      :Optional[Any]				  = torch.Size((1, 4, 1024)      )
											self.assertEqual(encoder_outputs.shape    ,   UpperCAmelCase      )
											self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )

											# decoder outputs
											lowerCAmelCase      :Tuple				  = model.prophetnet.decoder(
											    UpperCAmelCase    ,   encoder_hidden_states=UpperCAmelCase    ,   )
											lowerCAmelCase      :Optional[Any]				  = decoder_outputs[1].view(1    ,   model.config.ngram    ,   14    ,   -1      )
											lowerCAmelCase      :Any				  = model.lm_head(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,   UpperCAmelCase    ,   atol=1e-4      )      )
					@slow
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
											model.to(UpperCAmelCase      )

											lowerCAmelCase      :Tuple				  = torch.tensor([[17, 9_6208, 10_3471, 2]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = torch.tensor(
											    [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCAmelCase      )
											lowerCAmelCase      :Tuple				  = model(
											    input_ids=UpperCAmelCase    ,   attention_mask=UpperCAmelCase    ,   encoder_outputs=UpperCAmelCase    ,   decoder_input_ids=UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = output[0]
											lowerCAmelCase      :List[str]				  = torch.Size((1, 14, 25_0012)      )
											self.assertEqual(output_predited_logis.shape    ,   UpperCAmelCase      )
											# compare the actual values for a slice.
											lowerCAmelCase      :List[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 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Optional[int]:
											lowerCAmelCase      :str				  = XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )
											model.to(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = 512

											lowerCAmelCase      :Union[str, Any]				  = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'      )

											lowerCAmelCase      :Optional[int]				  = (
											    '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      :Union[str, 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.'
											)
											lowerCAmelCase      :Optional[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。'
											)

											lowerCAmelCase      :List[Any]				  = tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]    ,   padding=UpperCAmelCase    ,   max_length=255    ,   return_tensors='pt'      ).input_ids
											lowerCAmelCase      :List[str]				  = input_ids.to(UpperCAmelCase      )

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

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