

'''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()
SCREAMING_SNAKE_CASE							:				str     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Dict     	=			"Hello world! cécé herlolip"




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					str		,lowerCAmelCase__:					bool   )      -> Optional[Any]:
							SCREAMING_SNAKE_CASE_				=				FairseqRobertaModel.from_pretrained(lowerCAmelCase__   )
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE_				=				roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE_				=				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=514		,type_vocab_size=1		,layer_norm_eps=1E-5		,)
							if classification_head:
														SCREAMING_SNAKE_CASE_				=				roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

							print('Our RoBERTa config:'		,lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				XLMRobertaXLForSequenceClassification(lowerCAmelCase__   ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__   )
							model.eval()

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

							SCREAMING_SNAKE_CASE_				=				roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE_				=				roberta_sent_encoder.layer_norm.bias

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

														SCREAMING_SNAKE_CASE_				=				layer.attention
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE_				=				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)   )
														)

														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE_				=				layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE_				=				roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE_				=				layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE_				=				roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE_				=				layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE_				=				roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE_				=				roberta_layer.fca.bias
														# end of layer

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

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE_				=				roberta.encode(lowerCAmelCase__   ).unsqueeze(0   )  # batch of size 1

							SCREAMING_SNAKE_CASE_				=				model(lowerCAmelCase__   )[0]
							if classification_head:
														SCREAMING_SNAKE_CASE_				=				roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCAmelCase__   )   )
							else:
														SCREAMING_SNAKE_CASE_				=				roberta.model(lowerCAmelCase__   )[0]
							print(our_output.shape		,their_output.shape   )
							SCREAMING_SNAKE_CASE_				=				torch.max(torch.abs(our_output - their_output   )   ).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
							SCREAMING_SNAKE_CASE_				=				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__":
							SCREAMING_SNAKE_CASE							:				Union[str, 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."
							)
							SCREAMING_SNAKE_CASE							:				Tuple     	=			parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)








'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					bool = False   )      -> bool:
							if n == 2:
														return True
							if not n % 2 or n < 2:
														return False
							if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
														return False
							if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
														raise ValueError(
														    'Warning: upper bound of deterministic test is exceeded. '
														    'Pass allow_probable=True to allow probabilistic test. '
														    'A return value of True indicates a probable prime.'   )
							# array bounds provided by analysis
							SCREAMING_SNAKE_CASE_				=				[
							    2047,
							    137_3653,
							    2532_6001,
							    32_1503_1751,
							    2_1523_0289_8747,
							    3_4747_4966_0383,
							    341_5500_7172_8321,
							    1,
							    382_5123_0565_4641_3051,
							    1,
							    1,
							    3186_6585_7834_0311_5116_7461,
							    3_3170_4406_4679_8873_8596_1981,
							]

							SCREAMING_SNAKE_CASE_				=				[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
							for idx, _p in enumerate(lowerCAmelCase__		,1   ):
														if n < _p:
																					# then we have our last prime to check
																					SCREAMING_SNAKE_CASE_				=				primes[:idx]
																					break
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				n - 1, 0
							# break up n -1 into a power of 2 (s) and
							# remaining odd component
							# essentially, solve for d * 2 ** s == n - 1
							while d % 2 == 0:
														d //= 2
														s += 1
							for prime in plist:
														SCREAMING_SNAKE_CASE_				=				False
														for r in range(lowerCAmelCase__   ):
																					SCREAMING_SNAKE_CASE_				=				pow(lowerCAmelCase__		,d * 2**r		,lowerCAmelCase__   )
																					# see article for analysis explanation for m
																					if (r == 0 and m == 1) or ((m + 1) % n == 0):
																												SCREAMING_SNAKE_CASE_				=				True
																												# this loop will not determine compositeness
																												break
														if pr:
																					continue
														# if pr is False, then the above loop never evaluated to true,
														# and the n MUST be composite
														return False
							return True




def 							_UpperCamelCase				(   )      -> None:
							assert not miller_rabin(561   )
							assert miller_rabin(563   )
							# 2047

							assert not miller_rabin(83_8201   )
							assert miller_rabin(83_8207   )
							# 1_373_653

							assert not miller_rabin(1731_6001   )
							assert miller_rabin(1731_6017   )
							# 25_326_001

							assert not miller_rabin(30_7838_6641   )
							assert miller_rabin(30_7838_6653   )
							# 3_215_031_751

							assert not miller_rabin(1_7130_4557_4801   )
							assert miller_rabin(1_7130_4557_4819   )
							# 2_152_302_898_747

							assert not miller_rabin(2_7797_9972_8307   )
							assert miller_rabin(2_7797_9972_8327   )
							# 3_474_749_660_383

							assert not miller_rabin(113_8500_2390_9441   )
							assert miller_rabin(113_8500_2390_9527   )
							# 341_550_071_728_321

							assert not miller_rabin(127_5041_0188_4880_4351   )
							assert miller_rabin(127_5041_0188_4880_4391   )
							# 3_825_123_056_546_413_051

							assert not miller_rabin(796_6646_4458_5077_8779_1867   )
							assert miller_rabin(796_6646_4458_5077_8779_1951   )
							# 318_665_857_834_031_151_167_461

							assert not miller_rabin(5528_4067_7446_6478_9766_0333   )
							assert miller_rabin(5528_4067_7446_6478_9766_0359   )
							# 3_317_044_064_679_887_385_961_981
							# upper limit for probabilistic test


if __name__ == "__main__":
							test_miller_rabin()





