





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()
lowercase_		:				Any				=						logging.get_logger(__name__)

lowercase_		:				Any				=						'''Hello world! cécé herlolip'''

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   FairseqRobertaModel.from_pretrained(A__ )
				roberta.eval()  # disable dropout
				_snake_case :			Dict  						=   roberta.model.encoder.sentence_encoder
				_snake_case :			Optional[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_14	,			type_vocab_size=1	,			layer_norm_eps=1E-5	,			)
				if classification_head:
								_snake_case :			str  						=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'	,			A__ )

				_snake_case :			Optional[int]  						=   XLMRobertaXLForSequenceClassification(A__ ) if classification_head else XLMRobertaXLForMaskedLM(A__ )
				model.eval()

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

				_snake_case :			Optional[int]  						=   roberta_sent_encoder.layer_norm.weight
				_snake_case :			Dict  						=   roberta_sent_encoder.layer_norm.bias

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

								_snake_case :			Any  						=   layer.attention
								_snake_case :			Tuple  						=   roberta_layer.self_attn_layer_norm.weight
								_snake_case :			Optional[Any]  						=   roberta_layer.self_attn_layer_norm.bias

								# self attention
								_snake_case :			Dict  						=   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 :			int  						=   roberta_layer.self_attn.q_proj.weight
								_snake_case :			List[str]  						=   roberta_layer.self_attn.q_proj.bias
								_snake_case :			List[Any]  						=   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 :			Dict  						=   roberta_layer.self_attn.v_proj.bias

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

								# this one is final layer norm
								_snake_case :			Union[str, Any]  						=   roberta_layer.final_layer_norm.weight
								_snake_case :			List[str]  						=   roberta_layer.final_layer_norm.bias

								# intermediate
								_snake_case :			Optional[Any]  						=   layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								_snake_case :			Optional[int]  						=   roberta_layer.fca.weight
								_snake_case :			Dict  						=   roberta_layer.fca.bias

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

				if classification_head:
								_snake_case :			Tuple  						=   roberta.model.classification_heads['mnli'].dense.weight
								_snake_case :			Union[str, Any]  						=   roberta.model.classification_heads['mnli'].dense.bias
								_snake_case :			int  						=   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 :			Union[str, Any]  						=   roberta.model.encoder.lm_head.dense.weight
								_snake_case :			str  						=   roberta.model.encoder.lm_head.dense.bias
								_snake_case :			Any  						=   roberta.model.encoder.lm_head.layer_norm.weight
								_snake_case :			Any  						=   roberta.model.encoder.lm_head.layer_norm.bias
								_snake_case :			Tuple  						=   roberta.model.encoder.lm_head.weight
								_snake_case :			Tuple  						=   roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				_snake_case :			int  						=   roberta.encode(A__ ).unsqueeze(0 )  # batch of size 1

				_snake_case :			List[Any]  						=   model(A__ )[0]
				if classification_head:
								_snake_case :			int  						=   roberta.model.classification_heads['mnli'](roberta.extract_features(A__ ) )
				else:
								_snake_case :			List[Any]  						=   roberta.model(A__ )[0]
				print(our_output.shape	,			their_output.shape )
				_snake_case :			List[str]  						=   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(A__	,			A__	,			atol=1E-3 )
				print('Do both models output the same tensors?'	,			'🔥' if success else '💩' )
				if not success:
								raise Exception('Something went wRoNg' )

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


if __name__ == "__main__":
		lowercase_		:				List[Any]				=						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.'''
		)
		lowercase_		:				Optional[int]				=						parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)













import math

def       A__(		__lowerCAmelCase ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5	,			int(math.sqrt(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


if __name__ == "__main__":
		print(F'''{solution() = }''')






