


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  lowerCamelCase (      self		:  int				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase :	Optional[int]						=XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'				)
             model.to(lowercase_				)

             # encoder-decoder outputs
             _lowerCamelCase :	str						=torch.tensor([[17, 9_6208, 10_3471, 2]]				).to(lowercase_				)
             _lowerCamelCase :	str						=torch.tensor(
                 [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]				).to(lowercase_				)
             _lowerCamelCase :	Tuple						=model(
                 input_ids=lowercase_    ,     attention_mask=lowercase_    ,     encoder_outputs=lowercase_    ,     decoder_input_ids=lowercase_				)
             _lowerCamelCase :	str						=output[0]
             _lowerCamelCase :	Union[str, Any]						=torch.Size((1, 14, 25_0012)				)
             self.assertEqual(output_predited_logis.shape    ,     lowercase_				)
             _lowerCamelCase :	Dict						=torch.tensor(
                 [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]				).to(lowercase_				)
             self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]    ,     lowercase_    ,     atol=1E-4				)				)

             # encoder outputs
             _lowerCamelCase :	str						=model.prophetnet.encoder(lowercase_				)[0]
             _lowerCamelCase :	Optional[Any]						=torch.tensor(
                 [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]				).to(lowercase_				)
             _lowerCamelCase :	List[str]						=torch.Size((1, 4, 1024)				)
             self.assertEqual(encoder_outputs.shape    ,     lowercase_				)
             self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]    ,     lowercase_    ,     atol=1E-4				)				)

             # decoder outputs
             _lowerCamelCase :	Tuple						=model.prophetnet.decoder(
                 lowercase_    ,     encoder_hidden_states=lowercase_    ,     )
             _lowerCamelCase :	Optional[Any]						=decoder_outputs[1].view(1    ,     model.config.ngram    ,     14    ,     -1				)
             _lowerCamelCase :	Tuple						=model.lm_head(lowercase_				)
             _lowerCamelCase :	Optional[int]						=predicting_streams_logits[:, 0]
             self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]    ,     lowercase_    ,     atol=1E-4				)				)






      @slow
      def  lowerCamelCase (      self		:  Any				)      ->		List[str]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=XLMProphetNetForConditionalGeneration.from_pretrained(
                 'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'				)
             model.to(lowercase_				)

             _lowerCamelCase :	Optional[int]						=torch.tensor([[17, 9_6208, 10_3471, 2]]				).to(lowercase_				)
             _lowerCamelCase :	Any						=torch.tensor(
                 [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]				).to(lowercase_				)
             _lowerCamelCase :	str						=model(
                 input_ids=lowercase_    ,     attention_mask=lowercase_    ,     encoder_outputs=lowercase_    ,     decoder_input_ids=lowercase_				)
             _lowerCamelCase :	Dict						=output[0]
             _lowerCamelCase :	List[Any]						=torch.Size((1, 14, 25_0012)				)
             self.assertEqual(output_predited_logis.shape    ,     lowercase_				)
             # compare the actual values for a slice.
             _lowerCamelCase :	Optional[Any]						=torch.tensor(
                 [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]				).to(lowercase_				)

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






      @slow
      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=XLMProphetNetForConditionalGeneration.from_pretrained(
                 'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'				)
             model.to(lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=512

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

             _lowerCamelCase :	List[Any]						=(
                 '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 :	Dict						=(
                 '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 :	List[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。'
             )

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

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

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






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase :	List[str]						=roberta_sent_encoder.embed_tokens.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.embed_positions.weight
       _lowerCamelCase :	Tuple						=torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
       _lowerCamelCase :	Optional[Any]						=roberta_sent_encoder.emb_layer_norm.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.emb_layer_norm.bias

       for i in range(config.num_hidden_layers  ):
              # Encoder: start of layer
              _lowerCamelCase :	BertLayer						=model.roberta.encoder.layer[i]
              _lowerCamelCase :	TransformerSentenceEncoderLayer						=roberta_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase :	BertSelfAttention						=layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              _lowerCamelCase :	Any						=roberta.model.classification_heads['mnli'].dense.weight
              _lowerCamelCase :	Optional[Any]						=roberta.model.classification_heads['mnli'].dense.bias
              _lowerCamelCase :	List[str]						=roberta.model.classification_heads['mnli'].out_proj.weight
              _lowerCamelCase :	Tuple						=roberta.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _lowerCamelCase :	Optional[Any]						=roberta.model.encoder.lm_head.dense.weight
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.dense.bias
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.layer_norm.weight
              _lowerCamelCase :	int						=roberta.model.encoder.lm_head.layer_norm.bias
              _lowerCamelCase :	Dict						=roberta.model.encoder.lm_head.weight
              _lowerCamelCase :	str						=roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=torch.allclose(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    atol=1e-3  )
       print('Do both models output the same tensors?'	,    '🔥' if success else '💩'  )
       if not success:
              raise Exception('Something went wRoNg'  )

       pathlib.Path(SCREAMING_SNAKE_CASE__  ).mkdir(parents=SCREAMING_SNAKE_CASE__	,    exist_ok=SCREAMING_SNAKE_CASE__  )
       print(F'''Saving model to {pytorch_dump_folder_path}'''  )
       model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--classification_head', action='store_true', help='Whether to convert a final classification head.'
 )
 lowerCamelCase		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


