





"""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()
UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									="""Hello, World!"""
UpperCamelCase_									="""en_XX"""





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   Path('''data_bin'''       )
						_UpperCamelCase      : Optional[Any]         =   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       )

						_UpperCamelCase      : Any         =   xmod.model.encoder.sentence_encoder
						_UpperCamelCase      : Dict         =   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:
												_UpperCamelCase      : Optional[int]         =   xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

						print('''Our X-MOD config:'''	,						_lowercase       )

						_UpperCamelCase      : int         =   XmodForSequenceClassification(_lowercase       ) if classification_head else XmodForMaskedLM(_lowercase       )
						model.eval()

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

						_UpperCamelCase      : List[Any]         =   xmod_sent_encoder.layernorm_embedding.weight
						_UpperCamelCase      : List[Any]         =   xmod_sent_encoder.layernorm_embedding.bias

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

												# self attention
												_UpperCamelCase      : 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.'''       )

												_UpperCamelCase      : Union[str, Any]         =   xmod_layer.self_attn.q_proj.weight
												_UpperCamelCase      : List[str]         =   xmod_layer.self_attn.q_proj.bias
												_UpperCamelCase      : Any         =   xmod_layer.self_attn.k_proj.weight
												_UpperCamelCase      : Optional[int]         =   xmod_layer.self_attn.k_proj.bias
												_UpperCamelCase      : Union[str, Any]         =   xmod_layer.self_attn.v_proj.weight
												_UpperCamelCase      : Dict         =   xmod_layer.self_attn.v_proj.bias

												# self-attention output
												_UpperCamelCase      : Dict         =   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.'''       )
												_UpperCamelCase      : Tuple         =   xmod_layer.self_attn.out_proj.weight
												_UpperCamelCase      : Dict         =   xmod_layer.self_attn.out_proj.bias
												_UpperCamelCase      : Optional[Any]         =   xmod_layer.self_attn_layer_norm.weight
												_UpperCamelCase      : Union[str, Any]         =   xmod_layer.self_attn_layer_norm.bias

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

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

												# end of layer

						if xmod_sent_encoder.layer_norm is not None:
												_UpperCamelCase      : Optional[Any]         =   xmod_sent_encoder.layer_norm.weight
												_UpperCamelCase      : str         =   xmod_sent_encoder.layer_norm.bias

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

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

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










"""simple docstring"""




import logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



