






'''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							:							Tuple  							=		logging.get_logger(__name__)

__A							:							Optional[Any]  							=		"""Hello, World!"""
__A							:							Any  							=		"""en_XX"""






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     Path("data_bin")
 lowerCamelCase__  =     FairseqXmodModel.from_pretrained(
     model_name_or_path=str(Path(lowercase__).parent)				,     checkpoint_file=Path(lowercase__).name				,     _name="xmod_base"				,     arch="xmod_base"				,     task="multilingual_masked_lm"				,     data_name_or_path=str(lowercase__)				,     bpe="sentencepiece"				,     sentencepiece_model=str(Path(lowercase__).parent / "sentencepiece.bpe.model")				,     src_dict=str(data_dir / "dict.txt")				,     )
 xmod.eval()  # disable dropout
 print(lowercase__)

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

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

 lowerCamelCase__  =     XmodForSequenceClassification(lowercase__) if classification_head else XmodForMaskedLM(lowercase__)
 model.eval()

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

 lowerCamelCase__  =     xmod_sent_encoder.layernorm_embedding.weight
 lowerCamelCase__  =     xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  lowerCamelCase__  =     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.")

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

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

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

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

  # end of layer

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

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

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

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

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


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							:							Optional[Any]  							=		parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

