"""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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
  a__:					List[str]      							=     Path('data_bin'       )
  a__:					Any      							=     FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(_SCREAMING_SNAKE_CASE       ).parent       )		,					checkpoint_file=Path(_SCREAMING_SNAKE_CASE       ).name		,					_name='xmod_base'		,					arch='xmod_base'		,					task='multilingual_masked_lm'		,					data_name_or_path=str(_SCREAMING_SNAKE_CASE       )		,					bpe='sentencepiece'		,					sentencepiece_model=str(Path(_SCREAMING_SNAKE_CASE       ).parent / 'sentencepiece.bpe.model'       )		,					src_dict=str(data_dir / 'dict.txt'       )		,					)
  xmod.eval()  # disable dropout
  print(_SCREAMING_SNAKE_CASE       )

  a__:					Any      							=     xmod.model.encoder.sentence_encoder
  a__:					Union[str, Any]      							=     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__:					List[str]      							=     xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

  a__:					Tuple      							=     XmodForSequenceClassification(_SCREAMING_SNAKE_CASE       ) if classification_head else XmodForMaskedLM(_SCREAMING_SNAKE_CASE       )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  a__:					Optional[int]      							=     xmod_sent_encoder.embed_tokens.weight
  a__:					Dict      							=     xmod_sent_encoder.embed_positions.weight
  a__:					List[Any]      							=     torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c xmod doesn't use them.

  a__:					Union[str, Any]      							=     xmod_sent_encoder.layernorm_embedding.weight
  a__:					Any      							=     xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    a__:					str      							=     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__:					Tuple      							=     xmod_layer.self_attn.q_proj.weight
    a__:					int      							=     xmod_layer.self_attn.q_proj.bias
    a__:					List[str]      							=     xmod_layer.self_attn.k_proj.weight
    a__:					Union[str, Any]      							=     xmod_layer.self_attn.k_proj.bias
    a__:					Dict      							=     xmod_layer.self_attn.v_proj.weight
    a__:					List[str]      							=     xmod_layer.self_attn.v_proj.bias

    # self-attention output
    a__:					List[str]      							=     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__:					List[Any]      							=     xmod_layer.self_attn.out_proj.weight
    a__:					Union[str, Any]      							=     xmod_layer.self_attn.out_proj.bias
    a__:					Any      							=     xmod_layer.self_attn_layer_norm.weight
    a__:					List[Any]      							=     xmod_layer.self_attn_layer_norm.bias

    # intermediate
    a__:					List[Any]      							=     layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError('Dimensions of intermediate weights do not match.'       )
    a__:					str      							=     xmod_layer.fca.weight
    a__:					List[Any]      							=     xmod_layer.fca.bias

    # output
    a__:					str      							=     layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError('Dimensions of feed-forward weights do not match.'       )
    a__:					Any      							=     xmod_layer.fca.weight
    a__:					Optional[int]      							=     xmod_layer.fca.bias
    a__:					Any      							=     xmod_layer.final_layer_norm.weight
    a__:					Tuple      							=     xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      a__:					Dict      							=     xmod_layer.adapter_layer_norm.weight
      a__:					int      							=     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__:					List[str]      							=     bert_output.adapter_modules[lang_code]
      a__:					int      							=     xmod_layer.adapter_modules[lang_code]
      a__:					Any      							=     from_adapter.fca.weight
      a__:					List[str]      							=     from_adapter.fca.bias
      a__:					Tuple      							=     from_adapter.fca.weight
      a__:					Optional[int]      							=     from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    a__:					Optional[int]      							=     xmod_sent_encoder.layer_norm.weight
    a__:					int      							=     xmod_sent_encoder.layer_norm.bias

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

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

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

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


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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 lowercase__ 	=     None


lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowercase__ 	=     {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowercase__ 	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		["""input_ids""", """attention_mask"""]
      a__				=		MBartTokenizer

      a__				=		[]
      a__				=		[]



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase="<s>"   ,      lowercase="</s>"   ,      lowercase="</s>"   ,      lowercase="<s>"   ,      lowercase="<unk>"   ,      lowercase="<pad>"   ,      lowercase="<mask>"   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None   ,      **lowercase   ,      )     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     AddedToken(lowercase   ,      lstrip=lowercase   ,      rstrip=lowercase) if isinstance(lowercase   ,      lowercase) else mask_token

        super().__init__(
            vocab_file=lowercase   ,      tokenizer_file=lowercase   ,      bos_token=lowercase   ,      eos_token=lowercase   ,      sep_token=lowercase   ,      cls_token=lowercase   ,      unk_token=lowercase   ,      pad_token=lowercase   ,      mask_token=lowercase   ,      src_lang=lowercase   ,      tgt_lang=lowercase   ,      additional_special_tokens=lowercase   ,      **lowercase   ,      )

        a__:					Tuple      							=     vocab_file
        a__:					Union[str, Any]      							=     False if not self.vocab_file else True

        a__:					Union[str, Any]      							=     FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens])

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
        a__:					int      							=     {
            lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        a__:					List[Any]      							=     src_lang if src_lang is not None else 'en_XX'
        a__:					Tuple      							=     self.convert_tokens_to_ids(self._src_lang)
        a__:					str      							=     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return self._src_lang



      @src_lang.setter
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					Tuple      							=     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					Any      							=     [self.sep_token_id]
        a__:					List[str]      							=     [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 + sep + token_ids_a + sep) * [0]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
        a__:					Union[str, Any]      							=     src_lang
        a__:					Any      							=     self(lowercase   ,      add_special_tokens=lowercase   ,      return_tensors=lowercase   ,      **lowercase)
        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					Any      							=     tgt_lang_id
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = "en_XX"   ,      lowercase = None   ,      lowercase = "ro_RO"   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        a__:					Any      							=     src_lang
        a__:					List[Any]      							=     tgt_lang
        return super().prepare_seqaseq_batch(lowercase   ,      lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        return self.set_src_lang_special_tokens(self.src_lang)



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





        '''simple docstring'''





        return self.set_tgt_lang_special_tokens(self.tgt_lang)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					int      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					List[str]      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Dict      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Any      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					int      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					Dict      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Any      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Optional[Any]      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					str      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

        if not os.path.isdir(lowercase):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
          return
        a__:					Any      							=     os.path.join(
            lowercase   ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase):
          copyfile(self.vocab_file   ,      lowercase)

        return (out_vocab_file,)


