



'''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()
lowercase_									=    logging.get_logger(__name__)

lowercase_									=    "Hello, World!"
lowercase_									=    "en_XX"




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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

     _a           =    xmod.model.encoder.sentence_encoder
     _a           =    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:
          _a           =    xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     _a           =    XmodForSequenceClassification(__A) if classification_head else XmodForMaskedLM(__A)
     model.eval()

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

     _a           =    xmod_sent_encoder.layernorm_embedding.weight
     _a           =    xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          _a           =    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.''')

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

          # self-attention output
          _a           =    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.''')
          _a           =    xmod_layer.self_attn.out_proj.weight
          _a           =    xmod_layer.self_attn.out_proj.bias
          _a           =    xmod_layer.self_attn_layer_norm.weight
          _a           =    xmod_layer.self_attn_layer_norm.bias

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

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

          # end of layer

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

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

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

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

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


if __name__ == "__main__":
  lowercase_									=    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."
  )
  lowercase_									=    parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )








'''simple docstring'''





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			CTRLTokenizer
 __lowerCamelCase       :			Union[str, Any]		=			False
 __lowerCamelCase       :			Any		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a           =    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _a           =    dict(zip(A					,  range(len(A			)			)			)			)
      _a           =    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _a           =    {'''unk_token''': '''<unk>'''}

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write(json.dumps(A			) + '''\n'''			)
      with open(self.merges_file					,  '''w'''					,  encoding='''utf-8'''			) as fp:
           fp.write('''\n'''.join(A			)			)



 def    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return CTRLTokenizer.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt react readapt apt'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    CTRLTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''adapt react readapt apt'''
      _a           =    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]

      _a           =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



