





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()
__UpperCAmelCase       :   List[str]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   List[Any]				       =							"Hello, World!"
__UpperCAmelCase       :   Any				       =							"en_XX"







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[int]:
  __snake_case:						Optional[Any]   =						Path("""data_bin""")
  __snake_case:						Tuple   =						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:						int   =						xmod.model.encoder.sentence_encoder
  __snake_case:						Optional[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:
    __snake_case:						Optional[Any]   =						xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

  __snake_case:						Optional[Any]   =						XmodForSequenceClassification(SCREAMING_SNAKE_CASE__) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__)
  model.eval()

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

  __snake_case:						Optional[int]   =						xmod_sent_encoder.layernorm_embedding.weight
  __snake_case:						List[Any]   =						xmod_sent_encoder.layernorm_embedding.bias

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

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

    # self-attention output
    __snake_case:						Optional[Any]   =						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:						Dict   =						xmod_layer.self_attn.out_proj.weight
    __snake_case:						List[Any]   =						xmod_layer.self_attn.out_proj.bias
    __snake_case:						Optional[Any]   =						xmod_layer.self_attn_layer_norm.weight
    __snake_case:						int   =						xmod_layer.self_attn_layer_norm.bias

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

    # output
    __snake_case:						Tuple   =						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:						Optional[Any]   =						xmod_layer.fca.weight
    __snake_case:						Any   =						xmod_layer.fca.bias
    __snake_case:						str   =						xmod_layer.final_layer_norm.weight
    __snake_case:						Optional[int]   =						xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      __snake_case:						List[str]   =						xmod_layer.adapter_layer_norm.weight
      __snake_case:						Tuple   =						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:						Tuple   =						bert_output.adapter_modules[lang_code]
      __snake_case:						Optional[Any]   =						xmod_layer.adapter_modules[lang_code]
      __snake_case:						Optional[int]   =						from_adapter.fca.weight
      __snake_case:						int   =						from_adapter.fca.bias
      __snake_case:						List[str]   =						from_adapter.fca.weight
      __snake_case:						Optional[Any]   =						from_adapter.fca.bias

    # end of layer

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

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

  # Let's check that we get the same results.
  __snake_case:						List[Any]   =						xmod.encode(SCREAMING_SNAKE_CASE__).unsqueeze(0)  # batch of size 1
  model.roberta.set_default_language(SCREAMING_SNAKE_CASE__)

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













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """ibert"""




       def __init__(  self :    Dict							,				A :    Union[str, Any]=30_522							,				A :    List[Any]=768							,				A :    List[Any]=12							,				A :    Optional[int]=12							,				A :    Optional[Any]=3_072							,				A :    int="gelu"							,				A :    str=0.1							,				A :    List[Any]=0.1							,				A :    Optional[Any]=512							,				A :    int=2							,				A :    Union[str, Any]=0.02							,				A :    List[str]=1E-12							,				A :    Optional[int]=1							,				A :    Optional[int]=0							,				A :    List[str]=2							,				A :    str="absolute"							,				A :    Any=False							,				A :    Optional[Any]="none"							,				**A :    Any							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)

         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						hidden_size
         __snake_case:						List[str]   =						num_hidden_layers
         __snake_case:						Tuple   =						num_attention_heads
         __snake_case:						List[str]   =						hidden_act
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_dropout_prob
         __snake_case:						List[str]   =						attention_probs_dropout_prob
         __snake_case:						Any   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[str]   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						Optional[int]   =						position_embedding_type
         __snake_case:						str   =						quant_mode
         __snake_case:						Optional[int]   =						force_dequant







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         if self.task == "multiple-choice":
           __snake_case:						List[Any]   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:						List[str]   =						{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]		)






