





'''simple docstring'''



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
 import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__   :						str    =			logging.get_logger(__name__)

__magic_name__   :						Optional[int]    =			{"""vocab_file""": """vocab.txt"""}

__magic_name__   :						Optional[int]    =			{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

__magic_name__   :						List[str]    =			{
    """openbmb/cpm-ant-10b""": 1_024,
}




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				collections.OrderedDict()
  with open(SCREAMING_SNAKE_CASE__					,	"r"					,	encoding="utf-8"       ) as reader:
    _snake_case     		=				reader.readlines()
  for index, token in enumerate(SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				token.rstrip("\n"       )
    _snake_case     		=				index
  return vocab


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<unk>"						,   lowerCamelCase=200	):
       _snake_case     		=				vocab
       _snake_case     		=				unk_token
       _snake_case     		=				max_input_chars_per_word






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(lowerCamelCase	)
       if len(lowerCamelCase	) > self.max_input_chars_per_word:
         return [self.unk_token]

       _snake_case     		=				0
       _snake_case     		=				[]
       while start < len(lowerCamelCase	):
         _snake_case     		=				len(lowerCamelCase	)
         _snake_case     		=				None
         while start < end:
           _snake_case     		=				"".join(chars[start:end]	)
           if substr in self.vocab:
             _snake_case     		=				substr
             break
           end -= 1
         if cur_substr is None:
           sub_tokens.append(self.unk_token	)
           start += 1
         else:
           sub_tokens.append(lowerCamelCase	)
           _snake_case     		=				end

       return sub_tokens







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						Dict      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Union[str, Any]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						List[str]      =   ['''input_ids''', '''attention_mask''']
     UpperCAmelCase__       :						Optional[Any]      =   False

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<d>"						,   lowerCamelCase="</d>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="</n>"						,   lowerCamelCase="</_>"						,   lowerCamelCase="left"						,   **lowerCamelCase						,   ):
       requires_backends(self						,   ["jieba"]	)
       super().__init__(
           bod_token=lowerCamelCase						,   eod_token=lowerCamelCase						,   bos_token=lowerCamelCase						,   eos_token=lowerCamelCase						,   pad_token=lowerCamelCase						,   unk_token=lowerCamelCase						,   line_token=lowerCamelCase						,   space_token=lowerCamelCase						,   padding_side=lowerCamelCase						,   **lowerCamelCase						,   )
       _snake_case     		=				bod_token
       _snake_case     		=				eod_token
       _snake_case     		=				load_vocab(lowerCamelCase	)
       _snake_case     		=				self.encoder[space_token]
       _snake_case     		=				self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       _snake_case     		=				collections.OrderedDict(sorted(self.encoder.items()						,   key=lambda lowerCamelCase	: x[1]	)	)
       _snake_case     		=				{v: k for k, v in self.encoder.items()}

       _snake_case     		=				WordpieceTokenizer(vocab=self.encoder						,   unk_token=self.unk_token	)

     @property
     def 	UpperCamelCase(					self	):
       return self.encoder[self.bod_token]

     @property
     def 	UpperCamelCase(					self	):
       return self.encoder[self.eod_token]

     @property
     def 	UpperCamelCase(					self	):
       return self.encoder["\n"]

     @property
     def 	UpperCamelCase(					self	):
       return len(self.encoder	)

     def 	UpperCamelCase(					self	):
       return dict(self.encoder						,   **self.added_tokens_encoder	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for x in jieba.cut(lowerCamelCase						,   cut_all=lowerCamelCase	):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase	)	)
       return output_tokens

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       _snake_case     		=				[i for i in token_ids if i >= 0]
       _snake_case     		=				[
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return token in self.encoder

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return "".join(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.encoder.get(lowerCamelCase						,   self.encoder.get(self.unk_token	)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.decoder.get(lowerCamelCase						,   self.unk_token	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if os.path.isdir(lowerCamelCase	):
         _snake_case     		=				os.path.join(
             lowerCamelCase						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
       else:
         _snake_case     		=				(filename_prefix + "-" if filename_prefix else "") + save_directory
       _snake_case     		=				0
       if " " in self.encoder:
         _snake_case     		=				self.encoder[" "]
         del self.encoder[" "]
       if "\n" in self.encoder:
         _snake_case     		=				self.encoder["\n"]
         del self.encoder["\n"]
       _snake_case     		=				collections.OrderedDict(sorted(self.encoder.items()						,   key=lambda lowerCamelCase	: x[1]	)	)
       with open(lowerCamelCase						,   "w"						,   encoding="utf-8"	) as writer:
         for token, token_index in self.encoder.items():
           if index != token_index:
             logger.warning(
                 F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                 " Please check that the vocabulary is not corrupted!"	)
             _snake_case     		=				token_index
           writer.write(token + "\n"	)
           index += 1
       return (vocab_file,)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase						,   token_ids_a=lowerCamelCase						,   already_has_special_tokens=lowerCamelCase	)

       if token_ids_a is not None:
         return [1] + ([0] * len(lowerCamelCase	)) + [1] + ([0] * len(lowerCamelCase	))
       return [1] + ([0] * len(lowerCamelCase	))









'''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()
__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			"""Hello, World!"""
__magic_name__   :						Union[str, Any]    =			"""en_XX"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				Path("data_bin"       )
  _snake_case     		=				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__       )

  _snake_case     		=				xmod.model.encoder.sentence_encoder
  _snake_case     		=				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:
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

  print("Our X-MOD config:"					,	SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				XmodForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.weight
  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _snake_case     		=				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."       )

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

    # self-attention output
    _snake_case     		=				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."       )
    _snake_case     		=				xmod_layer.self_attn.out_proj.weight
    _snake_case     		=				xmod_layer.self_attn.out_proj.bias
    _snake_case     		=				xmod_layer.self_attn_layer_norm.weight
    _snake_case     		=				xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

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

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

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

  _snake_case     		=				model(SCREAMING_SNAKE_CASE__       )[0]
  if classification_head:
    _snake_case     		=				xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE__       )       )
  else:
    _snake_case     		=				xmod.model(SCREAMING_SNAKE_CASE__					,	lang_id=[SAMPLE_LANGUAGE]       )[0]
  print(our_output.shape					,	their_output.shape       )
  _snake_case     		=				torch.max(torch.abs(our_output - their_output       )       ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
  _snake_case     		=				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__":
 __magic_name__   :						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."""
 )
 __magic_name__   :						List[Any]    =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


