






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

lowerCAmelCase__    						=			'Hello, World!'
lowerCAmelCase__    						=			'en_XX'





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    Path('data_bin'       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    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       )

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    xmod.model.encoder.sentence_encoder
				_SCREAMING_SNAKE_CASE   :		List[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:
								_SCREAMING_SNAKE_CASE   :		int       		=    xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    XmodForSequenceClassification(lowerCamelCase       ) if classification_head else XmodForMaskedLM(lowerCamelCase       )
				model.eval()

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

				_SCREAMING_SNAKE_CASE   :		Any       		=    xmod_sent_encoder.layernorm_embedding.weight
				_SCREAMING_SNAKE_CASE   :		Any       		=    xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								_SCREAMING_SNAKE_CASE   :		Optional[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.'       )

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

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

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

								# output
								_SCREAMING_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.'       )
								_SCREAMING_SNAKE_CASE   :		int       		=    xmod_layer.fca.weight
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xmod_layer.fca.bias
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    xmod_layer.final_layer_norm.weight
								_SCREAMING_SNAKE_CASE   :		Dict       		=    xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												_SCREAMING_SNAKE_CASE   :		Any       		=    xmod_layer.adapter_layer_norm.weight
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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():
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    bert_output.adapter_modules[lang_code]
												_SCREAMING_SNAKE_CASE   :		Any       		=    xmod_layer.adapter_modules[lang_code]
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    from_adapter.fca.weight
												_SCREAMING_SNAKE_CASE   :		Any       		=    from_adapter.fca.bias
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    from_adapter.fca.weight
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    from_adapter.fca.bias

								# end of layer

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

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

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

				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCamelCase       )[0]
				if classification_head:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    xmod.model.classification_heads['mnli'](xmod.extract_features(lowerCamelCase       )       )
				else:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xmod.model(lowerCamelCase,						lang_id=[SAMPLE_LANGUAGE]       )[0]
				print(our_output.shape,						their_output.shape       )
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.max(torch.abs(our_output - their_output       )       ).item()
				print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
				_SCREAMING_SNAKE_CASE   :		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
			)









"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    data
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    previous
										_SCREAMING_SNAKE_CASE   :		Any       		=    next_node







						def __str__(    self							)		->							str:
										return F"""{self.data}"""







						def 			A      (    self							)		->							int:
										return self.data







						def 			A      (    self							)		->							Dict:
										return self.next







						def 			A      (    self							)		->							Union[str, Any]:
										return self.previous







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    head







						def __iter__(    self							)		->							Any:
										return self







						def 			A      (    self							)		->							Dict:
										if not self.current:
														raise StopIteration
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.current.get_data()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.current.get_next()
														return value







class _lowerCAmelCase						:







						def __init__(    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    None  # First node in list
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    None  # Last node in list







						def __str__(    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.head
										_SCREAMING_SNAKE_CASE   :		int       		=    []
										while current is not None:
														nodes.append(current.get_data()							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current.get_next()
										return " ".join(str(lowerCAmelCase_							) for node in nodes							)







						def __contains__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.head
										while current:
														if current.get_data() == value:
																		return True
														_SCREAMING_SNAKE_CASE   :		int       		=    current.get_next()
										return False







						def __iter__(    self							)		->							str:
										return LinkedListIterator(self.head							)







						def 			A      (    self							)		->							Dict:
										if self.head:
														return self.head.get_data()
										return None







						def 			A      (    self							)		->							List[Any]:
										if self.tail:
														return self.tail.get_data()
										return None







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node
										else:
														self.insert_before_node(self.head   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    Node(lowerCAmelCase_							)
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.set_tail(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous() is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node.next

										if node.get_next() is None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1
										_SCREAMING_SNAKE_CASE   :		str       		=    Node(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head
										while node:
														if current_position == position:
																		self.insert_before_node(lowerCAmelCase_   ,				lowerCAmelCase_							)
																		return
														current_position += 1
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    node.next
										self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Node:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.head
										while node:
														if node.get_data() == item:
																		return node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_next()
										raise Exception('Node not found'							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										if (node := self.get_node(lowerCAmelCase_							)) is not None:
														if node == self.head:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head.get_next()

														if node == self.tail:
																		_SCREAMING_SNAKE_CASE   :		int       		=    self.tail.get_previous()

														self.remove_node_pointers(lowerCAmelCase_							)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							None:
										if node.get_next():
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous():
														_SCREAMING_SNAKE_CASE   :		str       		=    node.next

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None







						def 			A      (    self							)		->							List[str]:
										return self.head is None







def 				lowercase__		(					):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

