'''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()
_lowercase						: int						=						logging.get_logger(__name__)

_lowercase						: List[str]						=						"Hello, World!"
_lowercase						: List[str]						=						"en_XX"




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					bool )			->      str:
							lowercase_					:			Union[str, Any]										=       Path("""data_bin""" )
							lowercase_					:			List[str]										=       FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(UpperCAmelCase__ ).parent )      ,      checkpoint_file=Path(UpperCAmelCase__ ).name      ,      _name="""xmod_base"""      ,      arch="""xmod_base"""      ,      task="""multilingual_masked_lm"""      ,      data_name_or_path=str(UpperCAmelCase__ )      ,      bpe="""sentencepiece"""      ,      sentencepiece_model=str(Path(UpperCAmelCase__ ).parent / """sentencepiece.bpe.model""" )      ,      src_dict=str(data_dir / """dict.txt""" )      ,      )
							xmod.eval()  # disable dropout
							print(UpperCAmelCase__ )

							lowercase_					:			List[str]										=       xmod.model.encoder.sentence_encoder
							lowercase_					:			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:
														lowercase_					:			List[Any]										=       xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

							lowercase_					:			str										=       XmodForSequenceClassification(UpperCAmelCase__ ) if classification_head else XmodForMaskedLM(UpperCAmelCase__ )
							model.eval()

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

							lowercase_					:			str										=       xmod_sent_encoder.layernorm_embedding.weight
							lowercase_					:			List[str]										=       xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														lowercase_					:			int										=       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.""" )

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

														# self-attention output
														lowercase_					:			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.""" )
														lowercase_					:			List[Any]										=       xmod_layer.self_attn.out_proj.weight
														lowercase_					:			Union[str, Any]										=       xmod_layer.self_attn.out_proj.bias
														lowercase_					:			int										=       xmod_layer.self_attn_layer_norm.weight
														lowercase_					:			Optional[Any]										=       xmod_layer.self_attn_layer_norm.bias

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

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

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														lowercase_					:			List[Any]										=       xmod_sent_encoder.layer_norm.weight
														lowercase_					:			Tuple										=       xmod_sent_encoder.layer_norm.bias

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

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

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

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


if __name__ == "__main__":
						_lowercase						: Dict						=						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."
						)
						_lowercase						: List[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_torch_available


_lowercase						: Dict						=						{"configuration_mmbt": ["MMBTConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Dict						=						["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						_lowercase						: Optional[int]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





