



"""simple docstring"""







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
			raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
A		=						logging.get_logger(__name__)

A		=						'Hello world! cécé herlolip'

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							bool ):
						"""simple docstring"""






						snake_case   :			int						    = FairseqRobertaModel.from_pretrained(lowerCamelCase_ )
						roberta.eval()  # disable dropout
						snake_case   :			str						    = roberta.model.encoder.sentence_encoder
						snake_case   :			Union[str, Any]						    = XLMRobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,		hidden_size=roberta.cfg.model.encoder_embed_dim     ,		num_hidden_layers=roberta.cfg.model.encoder_layers     ,		num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,		max_position_embeddings=5_1_4     ,		type_vocab_size=1     ,		layer_norm_eps=1e-5     ,		)
						if classification_head:
												snake_case   :			Dict						    = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

						print("Our RoBERTa config:"     ,		lowerCamelCase_ )

						snake_case   :			int						    = XLMRobertaXLForSequenceClassification(lowerCamelCase_ ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase_ )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						snake_case   :			Dict						    = roberta_sent_encoder.embed_tokens.weight
						snake_case   :			str						    = roberta_sent_encoder.embed_positions.weight
						snake_case   :			int						    = torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c RoBERTa doesn't use them.

						snake_case   :			Tuple						    = roberta_sent_encoder.layer_norm.weight
						snake_case   :			Any						    = roberta_sent_encoder.layer_norm.bias

						for i in range(config.num_hidden_layers ):
												# Encoder: start of layer
												snake_case   :			BertLayer						    = model.roberta.encoder.layer[i]
												snake_case   :			TransformerSentenceEncoderLayer						    = roberta_sent_encoder.layers[i]

												snake_case   :			RobertaAttention						    = layer.attention
												snake_case   :			int						    = roberta_layer.self_attn_layer_norm.weight
												snake_case   :			Tuple						    = roberta_layer.self_attn_layer_norm.bias

												# self attention
												snake_case   :			BertSelfAttention						    = layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size) )
												)

												snake_case   :			List[str]						    = roberta_layer.self_attn.q_proj.weight
												snake_case   :			Optional[Any]						    = roberta_layer.self_attn.q_proj.bias
												snake_case   :			Optional[int]						    = roberta_layer.self_attn.k_proj.weight
												snake_case   :			str						    = roberta_layer.self_attn.k_proj.bias
												snake_case   :			List[str]						    = roberta_layer.self_attn.v_proj.weight
												snake_case   :			Union[str, Any]						    = roberta_layer.self_attn.v_proj.bias

												# self-attention output
												snake_case   :			BertSelfOutput						    = layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												snake_case   :			List[Any]						    = roberta_layer.self_attn.out_proj.weight
												snake_case   :			Optional[int]						    = roberta_layer.self_attn.out_proj.bias

												# this one is final layer norm
												snake_case   :			List[Any]						    = roberta_layer.final_layer_norm.weight
												snake_case   :			Tuple						    = roberta_layer.final_layer_norm.bias

												# intermediate
												snake_case   :			BertIntermediate						    = layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												snake_case   :			int						    = roberta_layer.fca.weight
												snake_case   :			int						    = roberta_layer.fca.bias

												# output
												snake_case   :			BertOutput						    = layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												snake_case   :			Optional[int]						    = roberta_layer.fca.weight
												snake_case   :			Any						    = roberta_layer.fca.bias
												# end of layer

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

						# Let's check that we get the same results.
						snake_case   :			torch.Tensor						    = roberta.encode(lowerCamelCase_ ).unsqueeze(0 )  # batch of size 1

						snake_case   :			Union[str, Any]						    = model(lowerCamelCase_ )[0]
						if classification_head:
												snake_case   :			Optional[Any]						    = roberta.model.classification_heads["mnli"](roberta.extract_features(lowerCamelCase_ ) )
						else:
												snake_case   :			Any						    = roberta.model(lowerCamelCase_ )[0]
						print(our_output.shape     ,		their_output.shape )
						snake_case   :			Tuple						    = torch.max(torch.abs(our_output - their_output ) ).item()
						print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
						snake_case   :			List[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" )

						pathlib.Path(lowerCamelCase_ ).mkdir(parents=lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						print(f'''Saving model to {pytorch_dump_folder_path}''' )
						model.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--roberta_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		=						parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)








"""simple docstring"""







import math
import sys

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						if number != int(lowerCamelCase_ ):
												raise ValueError("the value of input must be a natural number" )
						if number < 0:
												raise ValueError("the value of input must not be a negative number" )
						if number == 0:
												return 1
						snake_case   :			List[str]						    = [-1] * (number + 1)
						snake_case   :			str						    = 0
						for i in range(1     ,		number + 1 ):
												snake_case   :			Tuple						    = sys.maxsize
												snake_case   :			Optional[Any]						    = int(math.sqrt(lowerCamelCase_ ) )
												for j in range(1     ,		root + 1 ):
																		snake_case   :			List[str]						    = 1 + answers[i - (j**2)]
																		snake_case   :			List[str]						    = min(lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Dict						    = answer
						return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



