
import numpy as np



def      __UpperCAmelCase    (  snake_case_							:		Dict				,					snake_case_							:		int="utf-8"				,					snake_case_							:		Dict="surrogatepass"				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     bin(int.from_bytes(text.encode(__lowerCAmelCase				,					__lowerCAmelCase				)				,					"big"				)				)[2:]
	return bits.zfill(8 * ((len(__lowerCAmelCase				) + 7) // 8)				)



def      __UpperCAmelCase    (  snake_case_							:		Optional[int]				,					snake_case_							:		str="utf-8"				,					snake_case_							:		Optional[Any]="surrogatepass"				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[Any] 						=     int(__lowerCAmelCase				,					2				)
	return n.to_bytes((n.bit_length() + 7) // 8				,					"big"				).decode(__lowerCAmelCase				,					__lowerCAmelCase				) or "\0"



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		str				):



	'''simple docstring'''



	if size_par + len(__lowerCAmelCase				) <= 2**size_par - (len(__lowerCAmelCase				) - 1):
		raise ValueError("size of parity don\'t match with size of data"				)

	UpperCAmelCase:					Any 						=     []
	UpperCAmelCase:					Union[str, Any] 						=     []
	UpperCAmelCase:					Tuple 						=     [bin(__lowerCAmelCase				)[2:] for x in range(1				,					size_par + len(__lowerCAmelCase				) + 1				)]

	# sorted information data for the size of the output data
	UpperCAmelCase:					List[str] 						=     []
	# data position template + parity
	UpperCAmelCase:					List[Any] 						=     []
	# parity bit counter
	UpperCAmelCase:					Optional[Any] 						=     0
	# counter position of data bits
	UpperCAmelCase:					Optional[int] 						=     0

	for x in range(1				,					size_par + len(__lowerCAmelCase				) + 1				):
		# Performs a template of bit positions - who should be given,
		# and who should be parity
		if qtd_bp < size_par:
			if (np.log(__lowerCAmelCase				) / np.log(2				)).is_integer():
				data_out_gab.append("P"				)
				UpperCAmelCase:					str 						=     qtd_bp + 1
			else:
				data_out_gab.append("D"				)
		else:
			data_out_gab.append("D"				)

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_ord.append(data[cont_data]				)
			cont_data += 1
		else:
			data_ord.append(__lowerCAmelCase				)

    # Calculates parity
	UpperCAmelCase:					Optional[Any] 						=     0  # parity bit counter
	for bp in range(1				,					size_par + 1				):
		# Bit counter one for a given parity
		UpperCAmelCase:					List[Any] 						=     0
		# counter to control the loop reading
		UpperCAmelCase:					int 						=     0
		for x in data_ord:
			if x is not None:
				try:
					UpperCAmelCase:					List[Any] 						=     (bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					UpperCAmelCase:					List[str] 						=     "0"
				if aux == "1" and x == "1":
					cont_bo += 1
			cont_loop += 1
		parity.append(cont_bo % 2				)

		qtd_bp += 1

	# Mount the message
	UpperCAmelCase:					Dict 						=     0  # parity bit counter
	for x in range(0				,					size_par + len(__lowerCAmelCase				)				):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]				)				)
			cont_bp += 1
		else:
			data_out.append(data_ord[x]				)

	return data_out



def      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     []
	# Parity bit counter
	UpperCAmelCase:					Union[str, Any] 						=     0
	# Counter p data bit reading
	UpperCAmelCase:					Tuple 						=     0
	# list of parity received
	UpperCAmelCase:					Optional[int] 						=     []
	UpperCAmelCase:					Optional[Any] 						=     []

	for x in range(1				,					len(__lowerCAmelCase				) + 1				):
		# Performs a template of bit positions - who should be given,
		#  and who should be parity
		if qtd_bp < size_par and (np.log(__lowerCAmelCase				) / np.log(2				)).is_integer():
			data_out_gab.append("P"				)
			UpperCAmelCase:					List[str] 						=     qtd_bp + 1
		else:
			data_out_gab.append("D"				)

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_output.append(data[cont_data]				)
		else:
			parity_received.append(data[cont_data]				)
		cont_data += 1

	# -----------calculates the parity with the data
	UpperCAmelCase:					Optional[Any] 						=     []
	UpperCAmelCase:					List[Any] 						=     []
	UpperCAmelCase:					Optional[int] 						=     [bin(__lowerCAmelCase				)[2:] for x in range(1				,					size_par + len(__lowerCAmelCase				) + 1				)]

	#  sorted information data for the size of the output data
	UpperCAmelCase:					str 						=     []
	# Data position feedback + parity
	UpperCAmelCase:					List[str] 						=     []
	# Parity bit counter
	UpperCAmelCase:					Any 						=     0
	# Counter p data bit reading
	UpperCAmelCase:					Union[str, Any] 						=     0

	for x in range(1				,					size_par + len(__lowerCAmelCase				) + 1				):
		# Performs a template position of bits - who should be given,
		# and who should be parity
		if qtd_bp < size_par and (np.log(__lowerCAmelCase				) / np.log(2				)).is_integer():
			data_out_gab.append("P"				)
			UpperCAmelCase:					List[Any] 						=     qtd_bp + 1
		else:
			data_out_gab.append("D"				)

		# Sorts the data to the new output size
		if data_out_gab[-1] == "D":
			data_ord.append(data_output[cont_data]				)
			cont_data += 1
		else:
			data_ord.append(__lowerCAmelCase				)

    # Calculates parity
	UpperCAmelCase:					Tuple 						=     0  # parity bit counter
	for bp in range(1				,					size_par + 1				):
		# Bit counter one for a certain parity
		UpperCAmelCase:					Optional[int] 						=     0
		# Counter to control loop reading
		UpperCAmelCase:					Optional[int] 						=     0
		for x in data_ord:
			if x is not None:
				try:
					UpperCAmelCase:					Union[str, Any] 						=     (bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					UpperCAmelCase:					Optional[Any] 						=     "0"
				if aux == "1" and x == "1":
					cont_bo += 1
			cont_loop += 1
		parity.append(str(cont_bo % 2				)				)

		qtd_bp += 1

	# Mount the message
	UpperCAmelCase:					Any 						=     0  # Parity bit counter
	for x in range(0				,					size_par + len(__lowerCAmelCase				)				):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]				)				)
			cont_bp += 1
		else:
			data_out.append(data_ord[x]				)

	UpperCAmelCase:					Optional[int] 						=     parity_received == parity
	return data_output, ack








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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


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

_a       =    '''Hello world! cécé herlolip'''


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     FairseqRobertaModel.from_pretrained(__lowerCAmelCase      )
						roberta.eval()  # disable dropout
						_UpperCAmelCase							   =     roberta.model.encoder.sentence_encoder
						_UpperCAmelCase							   =     RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,						hidden_size=roberta.args.encoder_embed_dim					,						num_hidden_layers=roberta.args.encoder_layers					,						num_attention_heads=roberta.args.encoder_attention_heads					,						intermediate_size=roberta.args.encoder_ffn_embed_dim					,						max_position_embeddings=514					,						type_vocab_size=1					,						layer_norm_eps=1E-5					,						)
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     RobertaForSequenceClassification(__lowerCAmelCase      ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase      )
						model.eval()

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

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

												# self attention
												_UpperCAmelCase							   =     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)      )
												)

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

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

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

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

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

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

						_UpperCAmelCase							   =     model(__lowerCAmelCase      )[0]
						if classification_head:
												_UpperCAmelCase							   =     roberta.model.classification_heads['mnli'](roberta.extract_features(__lowerCAmelCase      )      )
						else:
												_UpperCAmelCase							   =     roberta.model(__lowerCAmelCase      )[0]
						print(our_output.shape					,						their_output.shape      )
						_UpperCAmelCase							   =     torch.max(torch.abs(our_output - their_output      )      ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
						_UpperCAmelCase							   =     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_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
