





from collections import OrderedDict
from typing import Mapping

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


__A             =       logging.get_logger(__name__)

__A             =       {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







	'''simple docstring'''
	lowercase_	=    "xlm-roberta-xl"

	def __init__(self       :     Tuple ,				UpperCAmelCase_       :     Union[str, Any]=250_880 ,				UpperCAmelCase_       :     Optional[int]=2_560 ,				UpperCAmelCase_       :     Union[str, Any]=36 ,				UpperCAmelCase_       :     str=32 ,				UpperCAmelCase_       :     Dict=10_240 ,				UpperCAmelCase_       :     Optional[int]="gelu" ,				UpperCAmelCase_       :     Tuple=0.1 ,				UpperCAmelCase_       :     List[Any]=0.1 ,				UpperCAmelCase_       :     List[Any]=514 ,				UpperCAmelCase_       :     List[Any]=1 ,				UpperCAmelCase_       :     Dict=0.02 ,				UpperCAmelCase_       :     Dict=1E-0_5 ,				UpperCAmelCase_       :     int=1 ,				UpperCAmelCase_       :     Dict=0 ,				UpperCAmelCase_       :     Optional[Any]=2 ,				UpperCAmelCase_       :     Union[str, Any]="absolute" ,				UpperCAmelCase_       :     str=True ,				UpperCAmelCase_       :     str=None ,				**UpperCAmelCase_       :     List[str] ,				) ->int:



			'''simple docstring'''



			super().__init__(pad_token_id=UpperCAmelCase_ ,				bos_token_id=UpperCAmelCase_ ,				eos_token_id=UpperCAmelCase_ ,				**UpperCAmelCase_)
			lowerCamelCase__:   List[str]    						=vocab_size
			lowerCamelCase__:   List[Any]    						=hidden_size
			lowerCamelCase__:   Any    						=num_hidden_layers
			lowerCamelCase__:   Optional[Any]    						=num_attention_heads
			lowerCamelCase__:   Dict    						=hidden_act
			lowerCamelCase__:   str    						=intermediate_size
			lowerCamelCase__:   List[Any]    						=hidden_dropout_prob
			lowerCamelCase__:   List[str]    						=attention_probs_dropout_prob
			lowerCamelCase__:   Union[str, Any]    						=max_position_embeddings
			lowerCamelCase__:   Optional[int]    						=type_vocab_size
			lowerCamelCase__:   Tuple    						=initializer_range
			lowerCamelCase__:   Tuple    						=layer_norm_eps
			lowerCamelCase__:   Dict    						=position_embedding_type
			lowerCamelCase__:   Optional[int]    						=use_cache
			lowerCamelCase__:   List[str]    						=classifier_dropout



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







	'''simple docstring'''

	@property
	def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Mapping[str, Mapping[int, str]]:



			'''simple docstring'''



			if self.task == "multiple-choice":
					lowerCamelCase__:   Union[str, Any]    						={0: "batch", 1: "choice", 2: "sequence"}
			else:
					lowerCamelCase__:   Optional[int]    						={0: "batch", 1: "sequence"}
			return OrderedDict(
			    [
			        ("input_ids", dynamic_axis),
			        ("attention_mask", dynamic_axis),
			    ])








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             =       logging.get_logger(__name__)

__A             =       "Hello, World!"
__A             =       "en_XX"





def   lowerCAmelCase_				( __a			,					__a			,					__a	)  ->	List[Any]:







  """simple docstring"""





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

  lowerCamelCase__:   Optional[int]    						=xmod.model.encoder.sentence_encoder
  lowerCamelCase__:   Tuple    						=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__:   Optional[Any]    						=xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  lowerCamelCase__:   Tuple    						=XmodForSequenceClassification(__a	) if classification_head else XmodForMaskedLM(__a	)
  model.eval()

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

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

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

    # self attention
    lowerCamelCase__:   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."	)

    lowerCamelCase__:   List[str]    						=xmod_layer.self_attn.q_proj.weight
    lowerCamelCase__:   Any    						=xmod_layer.self_attn.q_proj.bias
    lowerCamelCase__:   Any    						=xmod_layer.self_attn.k_proj.weight
    lowerCamelCase__:   Tuple    						=xmod_layer.self_attn.k_proj.bias
    lowerCamelCase__:   Optional[int]    						=xmod_layer.self_attn.v_proj.weight
    lowerCamelCase__:   List[str]    						=xmod_layer.self_attn.v_proj.bias

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

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

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

    # end of layer

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

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

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

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

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


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

