
'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Tuple:
       lowercase							:  Any 			=   [
           """encoder.version""",
           """decoder.version""",
           """model.encoder.version""",
           """model.decoder.version""",
           """_float_tensor""",
           """decoder.output_projection.weight""",
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Dict:
       lowercase			,    lowercase							:  Optional[int] 			=   emb.weight.shape
       lowercase							:  int 			=   nn.Linear(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			bias=SCREAMING_SNAKE_CASE__       )
       lowercase							:  Any 			=   emb.weight.data
       return lin_layer







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__="uclanlp/plbart-base"      ,			SCREAMING_SNAKE_CASE__=False      ,			SCREAMING_SNAKE_CASE__=False       )				->					Optional[int]:
       lowercase							:  List[str] 			=   torch.load(SCREAMING_SNAKE_CASE__      ,			map_location="""cpu"""       )["""model"""]
       remove_ignore_keys_(SCREAMING_SNAKE_CASE__       )
       lowercase							:  Tuple 			=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

       lowercase							:  Optional[Any] 			=   PLBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__      ,			vocab_size=SCREAMING_SNAKE_CASE__       )

       lowercase							:  Tuple 			=   state_dict["""decoder.embed_tokens.weight"""]
       if not classification:
              lowercase							:  str 			=   PLBartForConditionalGeneration(SCREAMING_SNAKE_CASE__       )
              model.model.load_state_dict(SCREAMING_SNAKE_CASE__       )
              if finetuned:
                     lowercase							:  Dict 			=   make_linear_from_emb(model.model.shared       )

       else:
              lowercase							:  Tuple 			=   {}
              for key, value in state_dict.copy().items():
                     if key.startswith("""classification_heads.sentence_classification_head"""       ):
                            lowercase							:  Tuple 			=   value
                            state_dict.pop(SCREAMING_SNAKE_CASE__       )
              lowercase							:  Optional[int] 			=   PLBartForSequenceClassification(SCREAMING_SNAKE_CASE__       )
              model.model.load_state_dict(SCREAMING_SNAKE_CASE__       )
              model.classification_head.load_state_dict(SCREAMING_SNAKE_CASE__       )

       return model


if __name__ == "__main__":
  lowercase						:      Optional[Any]							=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""fairseq_path""", type=str, help="""model.pt on local filesystem.""")
  parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument(
      """--hf_config""",
      default="""uclanlp/plbart-base""",
      type=str,
      help="""Which huggingface architecture to use: plbart-base""",
  )
  parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
  parser.add_argument(
      """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
  )
  lowercase						:      int							=							parser.parse_args()
  lowercase						:      Any							=							convert_fairseq_plbart_checkpoint_from_disk(
      args.fairseq_path,
      hf_config_path=args.hf_config,
      finetuned=args.finetuned,
      classification=args.classification,
  )
  model.save_pretrained(args.pytorch_dump_folder_path)


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 __snake_case	(   unittest.TestCase  ):
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""   )
           model.to(snake_case   )

           # encoder-decoder outputs
           lowercase							:  List[str] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  List[str] 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  str 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Tuple 			=   output[0]
           lowercase							:  int 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           lowercase							:  Union[str, Any] 			=   torch.tensor(
               [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]   ).to(snake_case   )
           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # encoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.encoder(snake_case   )[0]
           lowercase							:  Any 			=   torch.tensor(
               [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]   ).to(snake_case   )
           lowercase							:  Optional[Any] 			=   torch.Size((1, 4, 1024)   )
           self.assertEqual(encoder_outputs.shape	,snake_case   )
           self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # decoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.decoder(
               snake_case	,encoder_hidden_states=snake_case	,)
           lowercase							:  Optional[int] 			=   decoder_outputs[1].view(1	,model.config.ngram	,14	,-1   )
           lowercase							:  Optional[int] 			=   model.lm_head(snake_case   )
           lowercase							:  List[Any] 			=   predicting_streams_logits[:, 0]
           self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )

           lowercase							:  Optional[int] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  Dict 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  Any 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Optional[int] 			=   output[0]
           lowercase							:  Dict 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           # compare the actual values for a slice.
           lowercase							:  int 			=   torch.tensor(
               [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]   ).to(snake_case   )

           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )
           lowercase							:  Optional[int] 			=   512

           lowercase							:  List[Any] 			=   XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )

           lowercase							:  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."""
           )
           lowercase							:  List[str] 			=   (
               """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							:  str 			=   (
               """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							:  Tuple 			=   tokenizer(
               [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	,padding=snake_case	,max_length=255	,return_tensors="""pt"""   ).input_ids
           lowercase							:  Union[str, Any] 			=   input_ids.to(snake_case   )

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

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