






"""simple docstring"""



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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
    raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
_UpperCamelCase		:  Union[str, Any]						=		logging.get_logger(__name__)

_UpperCamelCase		:  int						=		'Hello world! cécé herlolip'
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  bool    ):





  '''simple docstring'''





  lowercase							    =       FairseqRobertaModel.from_pretrained(__snake_case    )
  roberta.eval()  # disable dropout
  lowercase							    =       roberta.model.encoder.sentence_encoder
  lowercase							    =       XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,					hidden_size=roberta.cfg.model.encoder_embed_dim		,					num_hidden_layers=roberta.cfg.model.encoder_layers		,					num_attention_heads=roberta.cfg.model.encoder_attention_heads		,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim		,					max_position_embeddings=5_14		,					type_vocab_size=1		,					layer_norm_eps=1e-5		,					)
  if classification_head:
    lowercase							    =       roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'		,					__snake_case    )

  lowercase							    =       XLMRobertaXLForSequenceClassification(__snake_case    ) if classification_head else XLMRobertaXLForMaskedLM(__snake_case    )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  lowercase							    =       roberta_sent_encoder.embed_tokens.weight
  lowercase							    =       roberta_sent_encoder.embed_positions.weight
  lowercase							    =       torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.

  lowercase							    =       roberta_sent_encoder.layer_norm.weight
  lowercase							    =       roberta_sent_encoder.layer_norm.bias

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

    lowercase							    =       layer.attention
    lowercase							    =       roberta_layer.self_attn_layer_norm.weight
    lowercase							    =       roberta_layer.self_attn_layer_norm.bias

    # self attention
    lowercase							    =       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)    )
    )

    lowercase							    =       roberta_layer.self_attn.q_proj.weight
    lowercase							    =       roberta_layer.self_attn.q_proj.bias
    lowercase							    =       roberta_layer.self_attn.k_proj.weight
    lowercase							    =       roberta_layer.self_attn.k_proj.bias
    lowercase							    =       roberta_layer.self_attn.v_proj.weight
    lowercase							    =       roberta_layer.self_attn.v_proj.bias

    # self-attention output
    lowercase							    =       layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    lowercase							    =       roberta_layer.self_attn.out_proj.weight
    lowercase							    =       roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    lowercase							    =       roberta_layer.final_layer_norm.weight
    lowercase							    =       roberta_layer.final_layer_norm.bias

    # intermediate
    lowercase							    =       layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    lowercase							    =       roberta_layer.fca.weight
    lowercase							    =       roberta_layer.fca.bias

    # output
    lowercase							    =       layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    lowercase							    =       roberta_layer.fca.weight
    lowercase							    =       roberta_layer.fca.bias
    # end of layer

  if classification_head:
    lowercase							    =       roberta.model.classification_heads['mnli'].dense.weight
    lowercase							    =       roberta.model.classification_heads['mnli'].dense.bias
    lowercase							    =       roberta.model.classification_heads['mnli'].out_proj.weight
    lowercase							    =       roberta.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    lowercase							    =       roberta.model.encoder.lm_head.dense.weight
    lowercase							    =       roberta.model.encoder.lm_head.dense.bias
    lowercase							    =       roberta.model.encoder.lm_head.layer_norm.weight
    lowercase							    =       roberta.model.encoder.lm_head.layer_norm.bias
    lowercase							    =       roberta.model.encoder.lm_head.weight
    lowercase							    =       roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  lowercase							    =       roberta.encode(__snake_case    ).unsqueeze(0    )  # batch of size 1

  lowercase							    =       model(__snake_case    )[0]
  if classification_head:
    lowercase							    =       roberta.model.classification_heads['mnli'](roberta.extract_features(__snake_case    )    )
  else:
    lowercase							    =       roberta.model(__snake_case    )[0]
  print(our_output.shape		,					their_output.shape    )
  lowercase							    =       torch.max(torch.abs(our_output - their_output    )    ).item()
  print(f'max_absolute_diff = {max_absolute_diff}'    )  # ~ 1e-7
  lowercase							    =       torch.allclose(__snake_case		,					__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(__snake_case    ).mkdir(parents=__snake_case		,					exist_ok=__snake_case    )
  print(f'Saving model to {pytorch_dump_folder_path}'    )
  model.save_pretrained(__snake_case    )


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		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.'
    )
    _UpperCamelCase		:  str						=		parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in table:
    res += inp[i - 1]
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  return data[1:] + data[0]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       ''
  for i in range(len(__snake_case    )    ):
    if a[i] == b[i]:
      res += "0"
    else:
      res += "1"
  return res
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       int('0b' + data[0] + data[-1]		,					2    )
  lowercase							    =       int('0b' + data[1:3]		,					2    )
  return bin(s[row][col]    )[2:]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Dict		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       message[:4]
  lowercase							    =       message[4:]
  lowercase							    =       apply_table(__snake_case		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  lowercase							    =       apply_sbox(__snake_case		,					temp[:4]    )  # noqa: E741
  lowercase							    =       apply_sbox(__snake_case		,					temp[4:]    )
  lowercase							    =       '0' * (2 - len(__snake_case    )) + l  # noqa: E741
  lowercase							    =       '0' * (2 - len(__snake_case    )) + r
  lowercase							    =       apply_table(l + r		,					__snake_case    )
  lowercase							    =       xor(__snake_case		,					__snake_case    )
  return temp + right


if __name__ == "__main__":
    _UpperCamelCase		:  Dict						=		input('Enter 10 bit key: ')
    _UpperCamelCase		:  int						=		input('Enter 8 bit message: ')

    _UpperCamelCase		:  Optional[int]						=		[6, 3, 7, 4, 8, 5, 1_0, 9]
    _UpperCamelCase		:  Optional[Any]						=		[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
    _UpperCamelCase		:  str						=		[2, 4, 3, 1]
    _UpperCamelCase		:  List[Any]						=		[2, 6, 3, 1, 4, 8, 5, 7]
    _UpperCamelCase		:  List[str]						=		[4, 1, 3, 5, 7, 2, 8, 6]
    _UpperCamelCase		:  int						=		[4, 1, 2, 3, 2, 3, 4, 1]
    _UpperCamelCase		:  Optional[Any]						=		[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    _UpperCamelCase		:  Optional[int]						=		[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    _UpperCamelCase		:  str						=		apply_table(key, paa_table)
    _UpperCamelCase		:  Union[str, Any]						=		temp[:5]
    _UpperCamelCase		:  Dict						=		temp[5:]
    _UpperCamelCase		:  List[Any]						=		left_shift(left)
    _UpperCamelCase		:  str						=		left_shift(right)
    _UpperCamelCase		:  int						=		apply_table(left + right, pa_table)
    _UpperCamelCase		:  Optional[int]						=		left_shift(left)
    _UpperCamelCase		:  Any						=		left_shift(right)
    _UpperCamelCase		:  str						=		left_shift(left)
    _UpperCamelCase		:  Tuple						=		left_shift(right)
    _UpperCamelCase		:  Union[str, Any]						=		apply_table(left + right, pa_table)

    # encryption
    _UpperCamelCase		:  Tuple						=		apply_table(message, IP)
    _UpperCamelCase		:  Any						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  List[str]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    _UpperCamelCase		:  List[Any]						=		apply_table(CT, IP)
    _UpperCamelCase		:  Optional[Any]						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  int						=		temp[4:] + temp[:4]
    _UpperCamelCase		:  str						=		function(expansion, sa, sa, keya, temp)
    _UpperCamelCase		:  List[Any]						=		apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)


