"""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()
_a					:					str=			logging.get_logger(__name__)

_a					:					str=			"Hello, World!"
_a					:					List[str]=			"en_XX"
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    bool  )							->      int:

	'''simple docstring'''





	__snake_case      :							Any						=			Path('data_bin'  )
	__snake_case      :							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_  )

	__snake_case      :							int						=			xmod.model.encoder.sentence_encoder
	__snake_case      :							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=5_14      ,							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      :							Optional[Any]						=			xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

	__snake_case      :							str						=			XmodForSequenceClassification(UpperCAmelCase_  ) if classification_head else XmodForMaskedLM(UpperCAmelCase_  )
	model.eval()

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

	__snake_case      :							Tuple						=			xmod_sent_encoder.layernorm_embedding.weight
	__snake_case      :							int						=			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      :							int						=			xmod_sent_encoder.layers[i]

		# self attention
		__snake_case      :							List[str]						=			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      :							int						=			xmod_layer.self_attn.q_proj.weight
		__snake_case      :							List[str]						=			xmod_layer.self_attn.q_proj.bias
		__snake_case      :							List[str]						=			xmod_layer.self_attn.k_proj.weight
		__snake_case      :							int						=			xmod_layer.self_attn.k_proj.bias
		__snake_case      :							List[Any]						=			xmod_layer.self_attn.v_proj.weight
		__snake_case      :							Tuple						=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		__snake_case      :							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.'  )
		__snake_case      :							Tuple						=			xmod_layer.self_attn.out_proj.weight
		__snake_case      :							List[Any]						=			xmod_layer.self_attn.out_proj.bias
		__snake_case      :							str						=			xmod_layer.self_attn_layer_norm.weight
		__snake_case      :							Optional[int]						=			xmod_layer.self_attn_layer_norm.bias

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

		# output
		__snake_case      :							List[str]						=			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      :							str						=			xmod_layer.fca.weight
		__snake_case      :							Dict						=			xmod_layer.fca.bias
		__snake_case      :							List[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      :							List[str]						=			xmod_layer.adapter_layer_norm.weight
			__snake_case      :							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():
			__snake_case      :							Any						=			bert_output.adapter_modules[lang_code]
			__snake_case      :							Tuple						=			xmod_layer.adapter_modules[lang_code]
			__snake_case      :							Any						=			from_adapter.fca.weight
			__snake_case      :							Dict						=			from_adapter.fca.bias
			__snake_case      :							Optional[Any]						=			from_adapter.fca.weight
			__snake_case      :							str						=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		__snake_case      :							List[str]						=			xmod_sent_encoder.layer_norm.weight
		__snake_case      :							List[str]						=			xmod_sent_encoder.layer_norm.bias

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

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

	__snake_case      :							str						=			model(UpperCAmelCase_  )[0]
	if classification_head:
		__snake_case      :							List[str]						=			xmod.model.classification_heads['mnli'](xmod.extract_features(UpperCAmelCase_  )  )
	else:
		__snake_case      :							Optional[Any]						=			xmod.model(UpperCAmelCase_      ,							lang_id=[SAMPLE_LANGUAGE]  )[0]
	print(our_output.shape      ,							their_output.shape  )
	__snake_case      :							Union[str, Any]						=			torch.max(torch.abs(our_output - their_output  )  ).item()
	print(F"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-7
	__snake_case      :							Optional[Any]						=			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__":
	_a					:					str=			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					:					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 __future__ import annotations

from math import pow, sqrt
def   __UpperCAmelCase    (   UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float  )							->      dict[str, float]:

	'''simple docstring'''





	if (resistance, reactance, impedance).count(0  ) != 1:
		raise ValueError('One and only one argument must be 0'  )
	if resistance == 0:
		return {"resistance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif reactance == 0:
		return {"reactance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif impedance == 0:
		return {"impedance": sqrt(pow(UpperCAmelCase_      ,							2  ) + pow(UpperCAmelCase_      ,							2  )  )}
	else:
		raise ValueError('Exactly one argument must be 0'  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





