





'''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__					:			Union[str, Any]          =     logging.get_logger(__name__)

a__					:			List[Any]          =     '''Hello, World!'''
a__					:			Dict          =     '''en_XX'''




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	snake_case__	       =   Path('data_bin'							)
	snake_case__	       =   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__	       =   xmod.model.encoder.sentence_encoder
	snake_case__	       =   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__	       =   xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

	snake_case__	       =   XmodForSequenceClassification(UpperCAmelCase_							) if classification_head else XmodForMaskedLM(UpperCAmelCase_							)
	model.eval()

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

	snake_case__	       =   xmod_sent_encoder.layernorm_embedding.weight
	snake_case__	       =   xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		snake_case__	       =   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__	       =   xmod_layer.self_attn.q_proj.weight
		snake_case__	       =   xmod_layer.self_attn.q_proj.bias
		snake_case__	       =   xmod_layer.self_attn.k_proj.weight
		snake_case__	       =   xmod_layer.self_attn.k_proj.bias
		snake_case__	       =   xmod_layer.self_attn.v_proj.weight
		snake_case__	       =   xmod_layer.self_attn.v_proj.bias

		# self-attention output
		snake_case__	       =   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__	       =   xmod_layer.self_attn.out_proj.weight
		snake_case__	       =   xmod_layer.self_attn.out_proj.bias
		snake_case__	       =   xmod_layer.self_attn_layer_norm.weight
		snake_case__	       =   xmod_layer.self_attn_layer_norm.bias

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

		# output
		snake_case__	       =   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__	       =   xmod_layer.fca.weight
		snake_case__	       =   xmod_layer.fca.bias
		snake_case__	       =   xmod_layer.final_layer_norm.weight
		snake_case__	       =   xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			snake_case__	       =   xmod_layer.adapter_layer_norm.weight
			snake_case__	       =   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__	       =   bert_output.adapter_modules[lang_code]
			snake_case__	       =   xmod_layer.adapter_modules[lang_code]
			snake_case__	       =   from_adapter.fca.weight
			snake_case__	       =   from_adapter.fca.bias
			snake_case__	       =   from_adapter.fca.weight
			snake_case__	       =   from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		snake_case__	       =   xmod_sent_encoder.layer_norm.weight
		snake_case__	       =   xmod_sent_encoder.layer_norm.bias

	if classification_head:
		snake_case__	       =   xmod.model.classification_heads['mnli'].dense.weight
		snake_case__	       =   xmod.model.classification_heads['mnli'].dense.bias
		snake_case__	       =   xmod.model.classification_heads['mnli'].out_proj.weight
		snake_case__	       =   xmod.model.classification_heads['mnli'].out_proj.bias
	else:
		# LM Head
		snake_case__	       =   xmod.model.encoder.lm_head.dense.weight
		snake_case__	       =   xmod.model.encoder.lm_head.dense.bias
		snake_case__	       =   xmod.model.encoder.lm_head.layer_norm.weight
		snake_case__	       =   xmod.model.encoder.lm_head.layer_norm.bias
		snake_case__	       =   xmod.model.encoder.lm_head.weight
		snake_case__	       =   xmod.model.encoder.lm_head.bias

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

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










'''simple docstring'''




from math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



