


'''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()
a_					:    List[Any]          =    logging.get_logger(__name__)

a_					:    Dict          =    "Hello, World!"
a_					:    Optional[int]          =    "en_XX"






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :bool		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							Path('data_bin'		)
     _a			      =							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__		)

     _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=5_14				,						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:'				,						lowerCAmelCase__		)

     _a			      =							XmodForSequenceClassification(lowerCAmelCase__		) if classification_head else XmodForMaskedLM(lowerCAmelCase__		)
     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(lowerCAmelCase__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(lowerCAmelCase__		)

     _a			      =							model(lowerCAmelCase__		)[0]
     if classification_head:
          _a			      =							xmod.model.classification_heads['mnli'](xmod.extract_features(lowerCAmelCase__		)		)
     else:
          _a			      =							xmod.model(lowerCAmelCase__				,						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(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__":
 a_					:    Union[str, Any]          =    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."
 )
 a_					:    List[str]          =    parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )





'''simple docstring'''






import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a_					:    Optional[Any]          =    logging.getLogger(__name__)




@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None





@dataclass(frozen=_SCREAMING_SNAKE_CASE       )
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None


if is_torch_available():
 import torch
 from torch.utils.data import Dataset




 class  a     (     _SCREAMING_SNAKE_CASE       ):
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							Optional[int]:
       _a			      =							hans_processors[task]()

       _a			      =							os.path.join(
           __magic_name__      ,				'cached_{}_{}_{}_{}'.format(
               'dev' if evaluate else 'train'      ,				tokenizer.__class__.__name__      ,				str(__magic_name__						)      ,				__magic_name__      ,				)      ,				)
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       _a			      =							cached_features_file + '.lock'
       with FileLock(__magic_name__						):
            if os.path.exists(__magic_name__						) and not overwrite_cache:
                 logger.info(f'Loading features from cached file {cached_features_file}'						)
                 _a			      =							torch.load(__magic_name__						)
            else:
                 logger.info(f'Creating features from dataset file at {data_dir}'						)

                 _a			      =							(
                     processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
                 )

                 logger.info('Training examples: %s'      ,				len(__magic_name__						)						)
                 _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)
                 logger.info('Saving features into cached file %s'      ,				__magic_name__						)
                 torch.save(self.features      ,				__magic_name__						)
  def __len__( self						)					->							List[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
       return self.label_list


if is_tf_available():
 import tensorflow as tf




 class  a     :
  _lowerCAmelCase 					=	42
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 1_28      ,				__magic_name__=False      ,				__magic_name__ = False      ,				)					->							str:
       _a			      =							hans_processors[task]()
       _a			      =							processor.get_labels()
       if tokenizer.__class__ in (
           RobertaTokenizer,
           RobertaTokenizerFast,
           XLMRobertaTokenizer,
           BartTokenizer,
           BartTokenizerFast,
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            _a      ,   _a			      =							label_list[2], label_list[1]
       _a			      =							label_list

       _a			      =							processor.get_dev_examples(__magic_name__						) if evaluate else processor.get_train_examples(__magic_name__						)
       _a			      =							hans_convert_examples_to_features(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)

       def gen():
            for ex_index, ex in tqdm.tqdm(enumerate(self.features						)      ,				desc='convert examples to features'						):
                 if ex_index % 1_00_00 == 0:
                      logger.info('Writing example %d of %d' % (ex_index, len(__magic_name__						))						)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

       _a			      =							tf.data.Dataset.from_generator(
           __magic_name__      ,				(
               {
                   'example_id': tf.intaa,
                   'input_ids': tf.intaa,
                   'attention_mask': tf.intaa,
                   'token_type_ids': tf.intaa,
               },
               tf.intaa,
           )      ,				(
               {
                   'example_id': tf.TensorShape([]						),
                   'input_ids': tf.TensorShape([None, None]						),
                   'attention_mask': tf.TensorShape([None, None]						),
                   'token_type_ids': tf.TensorShape([None, None]						),
               },
               tf.TensorShape([]						),
           )      ,				)
  def 							__UpperCAmelCase		( self						)					->							Tuple:
       return self.dataset
  def __len__( self						)					->							Optional[Any]:
       return len(self.features						)
  def __getitem__( self      ,				__magic_name__						)					->							InputFeatures:
       return self.features[i]




  def 							__UpperCAmelCase		( self						)					->							List[Any]:
       return self.label_list




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Tuple:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_train_set.txt'						)						)      ,				'train'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[Any]:
      return self._create_examples(self._read_tsv(os.path.join(__magic_name__      ,				'heuristics_evaluation_set.txt'						)						)      ,				'dev'						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return ["contradiction", "entailment", "neutral"]




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							[]
      for i, line in enumerate(__magic_name__						):
           if i == 0:
                continue
           _a			      =							'%s-%s' % (set_type, line[0])
           _a			      =							line[5]
           _a			      =							line[6]
           _a			      =							line[7][2:] if line[7].startswith('ex'						) else line[7]
           _a			      =							line[0]
           examples.append(InputExample(guid=__magic_name__      ,				text_a=__magic_name__      ,				text_b=__magic_name__      ,				label=__magic_name__      ,				pairID=__magic_name__						)						)
      return examples






def 	_A				(lowerCAmelCase__  :List[InputExample]				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :PreTrainedTokenizer				,						)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{label: i for i, label in enumerate(lowerCAmelCase__		)}

     _a			      =							[]
     for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__		)				,						desc='convert examples to features'		):
          if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d' % (ex_index)		)

          _a			      =							tokenizer(
              example.text_a				,						example.text_b				,						add_special_tokens=lowerCAmelCase__				,						max_length=lowerCAmelCase__				,						padding='max_length'				,						truncation=lowerCAmelCase__				,						return_overflowing_tokens=lowerCAmelCase__				,						)

          _a			      =							label_map[example.label] if example.label in label_map else 0

          _a			      =							int(example.pairID		)

          features.append(InputFeatures(**lowerCAmelCase__				,						label=lowerCAmelCase__				,						pairID=lowerCAmelCase__		)		)

     for i, example in enumerate(examples[:5]		):
          logger.info('*** Example ***'		)
          logger.info(f'guid: {example}'		)
          logger.info(f'features: {features[i]}'		)

     return features


a_					:    Optional[int]          =    {
    "hans": 3,
}






a_					:    Optional[Any]          =    {
    "hans": HansProcessor,
}

