



'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
  raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
  raise Exception("""requires fairseq < v2""")

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

lowerCamelCase	 =       """Hello, World!"""
lowerCamelCase	 =       """en_XX"""



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



       """simple docstring"""



       __lowercase       	=Path('data_bin'					)
       __lowercase       	=FairseqXmodModel.from_pretrained(
           model_name_or_path=str(Path(_lowerCAmelCase					).parent					)  ,   checkpoint_file=Path(_lowerCAmelCase					).name  ,   _name='xmod_base'  ,   arch='xmod_base'  ,   task='multilingual_masked_lm'  ,   data_name_or_path=str(_lowerCAmelCase					)  ,   bpe='sentencepiece'  ,   sentencepiece_model=str(Path(_lowerCAmelCase					).parent / 'sentencepiece.bpe.model'					)  ,   src_dict=str(data_dir / 'dict.txt'					)  ,   )
       xmod.eval()  # disable dropout
       print(_lowerCAmelCase					)

       __lowercase       	=xmod.model.encoder.sentence_encoder
       __lowercase       	=XmodConfig(
           vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,   hidden_size=xmod.cfg.model.encoder_embed_dim  ,   num_hidden_layers=xmod.cfg.model.encoder_layers  ,   num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,   max_position_embeddings=514  ,   type_vocab_size=1  ,   layer_norm_eps=1e-5  ,   pre_norm=xmod.cfg.model.encoder_normalize_before  ,   adapter_reduction_factor=getattr(xmod.cfg.model  ,   'bottleneck'  ,   2					)  ,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,   ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,   languages=xmod.cfg.model.languages  ,   )
       if classification_head:
              __lowercase       	=xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

       print('Our X-MOD config:'  ,   _lowerCAmelCase					)

       __lowercase       	=XmodForSequenceClassification(_lowerCAmelCase					) if classification_head else XmodForMaskedLM(_lowerCAmelCase					)
       model.eval()

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

       __lowercase       	=xmod_sent_encoder.layernorm_embedding.weight
       __lowercase       	=xmod_sent_encoder.layernorm_embedding.bias

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

              # self attention
              __lowercase       	=layer.attention.self
              if not (
                  xmod_layer.self_attn.k_proj.weight.data.shape
                  == xmod_layer.self_attn.q_proj.weight.data.shape
                  == xmod_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)					)
              ):
                     raise AssertionError('Dimensions of self-attention weights do not match.'					)

              __lowercase       	=xmod_layer.self_attn.q_proj.weight
              __lowercase       	=xmod_layer.self_attn.q_proj.bias
              __lowercase       	=xmod_layer.self_attn.k_proj.weight
              __lowercase       	=xmod_layer.self_attn.k_proj.bias
              __lowercase       	=xmod_layer.self_attn.v_proj.weight
              __lowercase       	=xmod_layer.self_attn.v_proj.bias

              # self-attention output
              __lowercase       	=layer.attention.output
              if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                     raise AssertionError('Dimensions of self-attention output weights do not match.'					)
              __lowercase       	=xmod_layer.self_attn.out_proj.weight
              __lowercase       	=xmod_layer.self_attn.out_proj.bias
              __lowercase       	=xmod_layer.self_attn_layer_norm.weight
              __lowercase       	=xmod_layer.self_attn_layer_norm.bias

              # intermediate
              __lowercase       	=layer.intermediate
              if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError('Dimensions of intermediate weights do not match.'					)
              __lowercase       	=xmod_layer.fca.weight
              __lowercase       	=xmod_layer.fca.bias

              # output
              __lowercase       	=layer.output
              if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError('Dimensions of feed-forward weights do not match.'					)
              __lowercase       	=xmod_layer.fca.weight
              __lowercase       	=xmod_layer.fca.bias
              __lowercase       	=xmod_layer.final_layer_norm.weight
              __lowercase       	=xmod_layer.final_layer_norm.bias
              if bert_output.adapter_layer_norm is not None:
                     __lowercase       	=xmod_layer.adapter_layer_norm.weight
                     __lowercase       	=xmod_layer.adapter_layer_norm.bias

              if sorted(bert_output.adapter_modules.keys()					) != sorted(xmod_layer.adapter_modules.keys()					):
                     raise AssertionError('Lists of language adapters do not match.'					)
              for lang_code, adapter in xmod_layer.adapter_modules.items():
                     __lowercase       	=bert_output.adapter_modules[lang_code]
                     __lowercase       	=xmod_layer.adapter_modules[lang_code]
                     __lowercase       	=from_adapter.fca.weight
                     __lowercase       	=from_adapter.fca.bias
                     __lowercase       	=from_adapter.fca.weight
                     __lowercase       	=from_adapter.fca.bias

              # end of layer

       if xmod_sent_encoder.layer_norm is not None:
              __lowercase       	=xmod_sent_encoder.layer_norm.weight
              __lowercase       	=xmod_sent_encoder.layer_norm.bias

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

       # Let's check that we get the same results.
       __lowercase       	=xmod.encode(_lowerCAmelCase					).unsqueeze(0					)  # batch of size 1
       model.roberta.set_default_language(_lowerCAmelCase					)

       __lowercase       	=model(_lowerCAmelCase					)[0]
       if classification_head:
              __lowercase       	=xmod.model.classification_heads['mnli'](xmod.extract_features(_lowerCAmelCase					)					)
       else:
              __lowercase       	=xmod.model(_lowerCAmelCase  ,   lang_id=[SAMPLE_LANGUAGE]					)[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(_lowerCAmelCase  ,   _lowerCAmelCase  ,   atol=1e-3					)
       print('Do both models output the same tensors?'  ,   '🔥' if success else '💩'					)
       if not success:
              raise Exception('Something went wRoNg'					)

       Path(_lowerCAmelCase					).mkdir(parents=_lowerCAmelCase  ,   exist_ok=_lowerCAmelCase					)
       print(f"""Saving model to {pytorch_dump_folder_path}"""					)
       model.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
  lowerCamelCase	 =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xmod_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_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )








'''simple docstring'''



import string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=''
							for i in sequence:
														__lowercase       	=ord(_lowerCAmelCase					)
														if 65 <= extract <= 90:
																					output += chr(155 - extract					)
														elif 97 <= extract <= 122:
																					output += chr(219 - extract					)
														else:
																					output += i
							return output



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=string.ascii_letters
							__lowercase       	=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(_lowerCAmelCase					)] if c in letters else c for c in sequence					)



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

							print('Running performance benchmarks...'					)
							__lowercase       	='from string import printable ; from __main__ import atbash, atbash_slow'
							print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)
							print(f""">      atbash(): {timeit('atbash(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"{example} encrypted in atbash: {atbash(example)}")
		benchmark()



