



"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


_lowerCAmelCase							:   int				=				logging.get_logger(__name__)

_lowerCAmelCase							:   List[str]				=				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase							:   str				=				{
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

_lowerCAmelCase							:   Tuple				=				{
    'google/realm-cc-news-pretrained-embedder': 5_12,
    'google/realm-cc-news-pretrained-encoder': 5_12,
    'google/realm-cc-news-pretrained-scorer': 5_12,
    'google/realm-cc-news-pretrained-openqa': 5_12,
    'google/realm-orqa-nq-openqa': 5_12,
    'google/realm-orqa-nq-reader': 5_12,
    'google/realm-orqa-wq-openqa': 5_12,
    'google/realm-orqa-wq-reader': 5_12,
}

_lowerCAmelCase							:   Tuple				=				{
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}






class       UpperCAmelCase_    (							_SCREAMING_SNAKE_CASE		):
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  List[str]						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Dict						=					PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Any						=					RealmTokenizer



      def __init__(	self	:      int	,      A	:      Optional[Any]=None	,      A	:      int=None	,      A	:      int=True	,      A	:      Dict="[UNK]"	,      A	:      List[str]="[SEP]"	,      A	:      Dict="[PAD]"	,      A	:      Dict="[CLS]"	,      A	:      Optional[Any]="[MASK]"	,      A	:      Optional[Any]=True	,      A	:      Any=None	,      **A	:      int	,      ):
           super().__init__(
               __snake_case	,      tokenizer_file=__snake_case	,      do_lower_case=__snake_case	,      unk_token=__snake_case	,      sep_token=__snake_case	,      pad_token=__snake_case	,      cls_token=__snake_case	,      mask_token=__snake_case	,      tokenize_chinese_chars=__snake_case	,      strip_accents=__snake_case	,      **__snake_case	,      )

           _UpperCAmelCase     :      Union[str, Any]														=       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
           if (
               normalizer_state.get("lowercase"	,      __snake_case		) != do_lower_case
               or normalizer_state.get("strip_accents"	,      __snake_case		) != strip_accents
               or normalizer_state.get("handle_chinese_chars"	,      __snake_case		) != tokenize_chinese_chars
           ):
                _UpperCAmelCase     :      str														=       getattr(__snake_case	,      normalizer_state.pop("type"		)		)
                _UpperCAmelCase     :      int														=       do_lower_case
                _UpperCAmelCase     :      List[Any]														=       strip_accents
                _UpperCAmelCase     :      List[Any]														=       tokenize_chinese_chars
                _UpperCAmelCase     :      List[str]														=       normalizer_class(**__snake_case		)

           _UpperCAmelCase     :      Optional[Any]														=       do_lower_case



      def 		snake_case_   (	self	:      List[str]	,      A	:      int	,      **A	:      Optional[int]		):
           _UpperCAmelCase     :      Tuple														=       PaddingStrategy.MAX_LENGTH

           _UpperCAmelCase     :      Tuple														=       text
           _UpperCAmelCase     :      Any														=       kwargs.pop("text_pair"	,      __snake_case		)
           _UpperCAmelCase     :      int														=       kwargs.pop("return_tensors"	,      __snake_case		)

           _UpperCAmelCase     :      int														=       {
               "input_ids": [],
               "attention_mask": [],
               "token_type_ids": [],
           }

           for idx, candidate_text in enumerate(__snake_case		):
                if batch_text_pair is not None:
                     _UpperCAmelCase     :      List[str]														=       batch_text_pair[idx]
                else:
                     _UpperCAmelCase     :      str														=       None

                _UpperCAmelCase     :      str														=       super().__call__(__snake_case	,      __snake_case	,      return_tensors=__snake_case	,      **__snake_case		)

                _UpperCAmelCase     :      Dict														=       encoded_candidates.get("input_ids"		)
                _UpperCAmelCase     :      Optional[Any]														=       encoded_candidates.get("attention_mask"		)
                _UpperCAmelCase     :      Optional[int]														=       encoded_candidates.get("token_type_ids"		)

                if encoded_input_ids is not None:
                     output_data["input_ids"].append(__snake_case		)
                if encoded_attention_mask is not None:
                     output_data["attention_mask"].append(__snake_case		)
                if encoded_token_type_ids is not None:
                     output_data["token_type_ids"].append(__snake_case		)

           _UpperCAmelCase     :      Tuple														=       {key: item for key, item in output_data.items() if len(__snake_case		) != 0}

           return BatchEncoding(__snake_case	,      tensor_type=__snake_case		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      Any=None		):
           _UpperCAmelCase     :      Dict														=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

           return output



      def 		snake_case_   (	self	:      List[str]	,      A	:      Optional[int]	,      A	:      Optional[int] = None		):
           _UpperCAmelCase     :      Any														=       [self.sep_token_id]
           _UpperCAmelCase     :      Union[str, Any]														=       [self.cls_token_id]
           if token_ids_a is None:
                return len(cls + token_ids_a + sep		) * [0]
           return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Tuple	,      A	:      List[str] = None		):
           _UpperCAmelCase     :      Tuple														=       self._tokenizer.model.save(__snake_case	,      name=__snake_case		)
           return tuple(__snake_case		)







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__      :     Any         =		logging.get_logger(__name__)

a__      :     Tuple         =		'Hello, World!'
a__      :     Any         =		'en_XX'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


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

     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:'''  ,						_UpperCAmelCase				)

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

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

     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__      :     List[str]         =		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__      :     Tuple         =		parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )



