


'''simple docstring'''



from __future__ import annotations



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       dict[str, str]							)  ->	str:
     __lowerCamelCase     :			Tuple              =	'', '', ''
     __lowerCamelCase     :			Any              =	[]

     for character in message_part:
          tmp.append(character_to_number[character]							)

     for each in tmp:
          one += each[0]
          two += each[1]
          three += each[2]

     return one + two + three



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       dict[str, str]							)  ->	tuple[str, str, str]:
     __lowerCamelCase     :			Any              =	'', ''
     __lowerCamelCase     :			Tuple              =	[]

     for character in message_part:
          this_part += character_to_number[character]

     for digit in this_part:
          tmp += digit
          if len(UpperCAmelCase_							) == len(UpperCAmelCase_							):
               result.append(UpperCAmelCase_							)
               __lowerCamelCase     :			Optional[Any]              =	''

     return result[0], result[1], result[2]



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str							)  ->	tuple[str, str, dict[str, str], dict[str, str]]:
     # Validate message and alphabet, set to upper and remove spaces
     __lowerCamelCase     :			List[str]              =	alphabet.replace(' '   ,					''							).upper()
     __lowerCamelCase     :			Union[str, Any]              =	message.replace(' '   ,					''							).upper()

     # Check length and characters
     if len(UpperCAmelCase_							) != 27:
          raise KeyError('Length of alphabet has to be 27.'							)
     for each in message:
          if each not in alphabet:
               raise ValueError('Each message character has to be included in alphabet!'							)

    # Generate dictionares
     __lowerCamelCase     :			int              =	(
         '111',
         '112',
         '113',
         '121',
         '122',
         '123',
         '131',
         '132',
         '133',
         '211',
         '212',
         '213',
         '221',
         '222',
         '223',
         '231',
         '232',
         '233',
         '311',
         '312',
         '313',
         '321',
         '322',
         '323',
         '331',
         '332',
         '333',
     )
     __lowerCamelCase     :			str              =	{}
     __lowerCamelCase     :			Dict              =	{}
     for letter, number in zip(UpperCAmelCase_   ,					UpperCAmelCase_							):
          __lowerCamelCase     :			List[Any]              =	number
          __lowerCamelCase     :			List[Any]              =	letter

     return message, alphabet, character_to_number, number_to_character



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,					UpperCAmelCase_					:       int = 5							)  ->	str:
     __lowerCamelCase     :			List[str]              =	__prepare(
         UpperCAmelCase_   ,					UpperCAmelCase_							)
     __lowerCamelCase     :			int              =	'', ''

     for i in range(0   ,					len(UpperCAmelCase_							) + 1   ,					UpperCAmelCase_							):
          encrypted_numeric += __encrypt_part(
              message[i : i + period]   ,					UpperCAmelCase_							)

     for i in range(0   ,					len(UpperCAmelCase_							)   ,					3							):
          encrypted += number_to_character[encrypted_numeric[i : i + 3]]

     return encrypted



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,					UpperCAmelCase_					:       int = 5							)  ->	str:
     __lowerCamelCase     :			List[Any]              =	__prepare(
         UpperCAmelCase_   ,					UpperCAmelCase_							)
     __lowerCamelCase     :			int              =	[]
     __lowerCamelCase     :			Optional[int]              =	''

     for i in range(0   ,					len(UpperCAmelCase_							) + 1   ,					UpperCAmelCase_							):
          __lowerCamelCase     :			List[Any]              =	__decrypt_part(message[i : i + period]   ,					UpperCAmelCase_							)

          for j in range(0   ,					len(UpperCAmelCase_							)							):
               decrypted_numeric.append(a[j] + b[j] + c[j]							)

     for each in decrypted_numeric:
          decrypted += number_to_character[each]

     return decrypted


if __name__ == "__main__":
      A__	:      Any				=						"""DEFEND THE EAST WALL OF THE CASTLE."""
      A__	:      Optional[Any]				=						encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
      A__	:      List[str]				=						decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
      print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')










'''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 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()
A__	:      List[str]				=						logging.get_logger(__name__)

A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
     __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
     roberta.eval()  # disable dropout
     __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
     __lowerCamelCase     :			Optional[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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
     if classification_head:
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'   ,					UpperCAmelCase_							)

     __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
     __lowerCamelCase     :			List[str]              =	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     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
     __lowerCamelCase     :			Tuple              =	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     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase     :			List[Any]              =	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     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
          __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias

          # intermediate
          __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Optional[Any]              =	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     :			Tuple              =	roberta_layer.fca.bias
          __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
          # end of layer

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

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

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

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


if __name__ == "__main__":
      A__	:      Optional[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."""
      )
      A__	:      Optional[Any]				=						parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )






