

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    __UpperCAmelCase (	__a      :			str		,__a      :			str		,__a      :			bool	)			->     List[Any]:

					"""simple docstring"""

					_a	:    Tuple         =		FairseqRobertaModel.from_pretrained(__a	)
					roberta.eval()  # disable dropout
					_a	:    List[str]         =		roberta.model.encoder.sentence_encoder
					_a	:    List[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=514		,type_vocab_size=1		,layer_norm_eps=1E-5		,)
					if classification_head:
										_a	:    Optional[int]         =		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our RoBERTa config:'''		,__a	)

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

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

					_a	:    Union[str, Any]         =		roberta_sent_encoder.layer_norm.weight
					_a	:    Union[str, Any]         =		roberta_sent_encoder.layer_norm.bias

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

										_a	:    RobertaAttention         =		layer.attention
										_a	:    str         =		roberta_layer.self_attn_layer_norm.weight
										_a	:    Tuple         =		roberta_layer.self_attn_layer_norm.bias

										# self attention
										_a	:    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)	)
										)

										_a	:    Dict         =		roberta_layer.self_attn.q_proj.weight
										_a	:    Optional[int]         =		roberta_layer.self_attn.q_proj.bias
										_a	:    Optional[int]         =		roberta_layer.self_attn.k_proj.weight
										_a	:    List[str]         =		roberta_layer.self_attn.k_proj.bias
										_a	:    Optional[int]         =		roberta_layer.self_attn.v_proj.weight
										_a	:    List[Any]         =		roberta_layer.self_attn.v_proj.bias

										# self-attention output
										_a	:    BertSelfOutput         =		layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										_a	:    Any         =		roberta_layer.self_attn.out_proj.weight
										_a	:    Tuple         =		roberta_layer.self_attn.out_proj.bias

										# this one is final layer norm
										_a	:    Optional[Any]         =		roberta_layer.final_layer_norm.weight
										_a	:    Optional[Any]         =		roberta_layer.final_layer_norm.bias

										# intermediate
										_a	:    BertIntermediate         =		layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										_a	:    List[Any]         =		roberta_layer.fca.weight
										_a	:    List[str]         =		roberta_layer.fca.bias

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

					if classification_head:
										_a	:    str         =		roberta.model.classification_heads['''mnli'''].dense.weight
										_a	:    int         =		roberta.model.classification_heads['''mnli'''].dense.bias
										_a	:    Tuple         =		roberta.model.classification_heads['''mnli'''].out_proj.weight
										_a	:    Optional[Any]         =		roberta.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										_a	:    List[Any]         =		roberta.model.encoder.lm_head.dense.weight
										_a	:    List[Any]         =		roberta.model.encoder.lm_head.dense.bias
										_a	:    List[str]         =		roberta.model.encoder.lm_head.layer_norm.weight
										_a	:    str         =		roberta.model.encoder.lm_head.layer_norm.bias
										_a	:    List[Any]         =		roberta.model.encoder.lm_head.weight
										_a	:    Optional[int]         =		roberta.model.encoder.lm_head.bias

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

					_a	:    str         =		model(__a	)[0]
					if classification_head:
										_a	:    Union[str, Any]         =		roberta.model.classification_heads['''mnli'''](roberta.extract_features(__a	)	)
					else:
										_a	:    int         =		roberta.model(__a	)[0]
					print(our_output.shape		,their_output.shape	)
					_a	:    int         =		torch.max(torch.abs(our_output - their_output	)	).item()
					print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
					_a	:    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__":
			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
			)






from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



