
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()
__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			"""Hello, World!"""
__lowerCamelCase				=			"""en_XX"""


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					bool       ):
       snake_case			:  int     							= Path("data_bin"       )
       snake_case			:  List[str]     							= FairseqXmodModel.from_pretrained(
           model_name_or_path=str(Path(__lowerCamelCase       ).parent       )							,   checkpoint_file=Path(__lowerCamelCase       ).name							,   _name="xmod_base"							,   arch="xmod_base"							,   task="multilingual_masked_lm"							,   data_name_or_path=str(__lowerCamelCase       )							,   bpe="sentencepiece"							,   sentencepiece_model=str(Path(__lowerCamelCase       ).parent / "sentencepiece.bpe.model"       )							,   src_dict=str(data_dir / "dict.txt"       )							,   )
       xmod.eval()  # disable dropout
       print(__lowerCamelCase       )

       snake_case			:  List[Any]     							= xmod.model.encoder.sentence_encoder
       snake_case			:  Optional[int]     							= 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			:  Any     							= xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

       snake_case			:  Dict     							= XmodForSequenceClassification(__lowerCamelCase       ) if classification_head else XmodForMaskedLM(__lowerCamelCase       )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       snake_case			:  Optional[int]     							= xmod_sent_encoder.embed_tokens.weight
       snake_case			:  Dict     							= 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			:  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			:  List[str]     							= model.roberta.encoder.layer[i]
              snake_case			:  Tuple     							= xmod_sent_encoder.layers[i]

              # self attention
              snake_case			:  List[Any]     							= 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			:  List[str]     							= xmod_layer.self_attn.q_proj.weight
              snake_case			:  List[str]     							= xmod_layer.self_attn.q_proj.bias
              snake_case			:  Union[str, Any]     							= xmod_layer.self_attn.k_proj.weight
              snake_case			:  Optional[int]     							= xmod_layer.self_attn.k_proj.bias
              snake_case			:  Any     							= xmod_layer.self_attn.v_proj.weight
              snake_case			:  List[Any]     							= xmod_layer.self_attn.v_proj.bias

              # self-attention output
              snake_case			:  int     							= 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			:  Any     							= xmod_layer.self_attn.out_proj.weight
              snake_case			:  Tuple     							= xmod_layer.self_attn.out_proj.bias
              snake_case			:  Any     							= xmod_layer.self_attn_layer_norm.weight
              snake_case			:  Dict     							= xmod_layer.self_attn_layer_norm.bias

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

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

              # end of layer

       if xmod_sent_encoder.layer_norm is not None:
              snake_case			:  Union[str, Any]     							= xmod_sent_encoder.layer_norm.weight
              snake_case			:  Tuple     							= xmod_sent_encoder.layer_norm.bias

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

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

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

       Path(__lowerCamelCase       ).mkdir(parents=__lowerCamelCase							,   exist_ok=__lowerCamelCase       )
       print(f"""Saving model to {pytorch_dump_folder_path}"""       )
       model.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			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."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )







__lowerCamelCase				=			{
    0: """0""",
    1: """1""",
    2: """2""",
    3: """3""",
    4: """4""",
    5: """5""",
    6: """6""",
    7: """7""",
    8: """8""",
    9: """9""",
    10: """a""",
    11: """b""",
    12: """c""",
    13: """d""",
    14: """e""",
    15: """f""",
}


def      UpperCamelCase   (       __lowerCamelCase	:					float       ):
       assert type(__lowerCamelCase       ) in (int, float) and decimal == int(__lowerCamelCase       )
       snake_case			:  str     							= int(__lowerCamelCase       )
       snake_case			:  str     							= ""
       snake_case			:  str     							= False
       if decimal < 0:
              snake_case			:  Optional[Any]     							= True
              decimal *= -1
       while decimal > 0:
              snake_case							,   snake_case			:  Union[str, Any]     							= divmod(__lowerCamelCase							,   16       )
              snake_case			:  Tuple     							= values[remainder] + hexadecimal
       snake_case			:  Tuple     							= "0x" + hexadecimal
       if negative:
              snake_case			:  Any     							= "-" + hexadecimal
       return hexadecimal


if __name__ == "__main__":
   import doctest

   doctest.testmod()





