






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()
__snake_case     :     List[str]       =  logging.get_logger(__name__)

__snake_case     :     Any       =  """Hello world! cécé herlolip"""







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       bool		)	-> Dict:

						"""simple docstring"""



						lowerCAmelCase__         =    FairseqRobertaModel.from_pretrained(UpperCamelCase_		)
						roberta.eval()  # disable dropout
						lowerCAmelCase__         =    roberta.model.encoder.sentence_encoder
						lowerCAmelCase__         =    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:
												lowerCAmelCase__         =    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    RobertaForSequenceClassification(UpperCamelCase_		) if classification_head else RobertaForMaskedLM(UpperCamelCase_		)
						model.eval()

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

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

												# self attention
												lowerCAmelCase__         =    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)		)
												)

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

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

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

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

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

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

						lowerCAmelCase__         =    model(UpperCamelCase_		)[0]
						if classification_head:
												lowerCAmelCase__         =    roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCamelCase_		)		)
						else:
												lowerCAmelCase__         =    roberta.model(UpperCamelCase_		)[0]
						print(our_output.shape     ,							their_output.shape		)
						lowerCAmelCase__         =    torch.max(torch.abs(our_output - their_output		)		).item()
						print(F"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
						lowerCAmelCase__         =    torch.allclose(UpperCamelCase_     ,							UpperCamelCase_     ,							atol=1e-3		)
						print('Do both models output the same tensors?'     ,							'🔥' if success else '💩'		)
						if not success:
												raise Exception('Something went wRoNg'		)

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


if __name__ == "__main__":
					__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."""
					)
					__snake_case     :     Optional[int]       =  parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)













from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

__snake_case     :     str       =  TypeVar("""T""", bound=bool)


class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    value
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return self._value







						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		lowerCAmelCase__    ,							lowerCAmelCase__         =    roota, roota

												lowerCAmelCase__         =    roota
												lowerCAmelCase__         =    roota.right
												lowerCAmelCase__         =    roota.left
												lowerCAmelCase__         =    SkewNode.merge(_UpperCamelCase  ,    _UpperCamelCase )

												return result




class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase = () ):






												"""simple docstring"""
												lowerCAmelCase__         =    None
												if data:
																		for item in data:
																								self.insert(_UpperCamelCase )






						def __bool__(						self ):






												"""simple docstring"""
												return self._root is not None






						def __iter__(						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												while self:
																		result.append(self.pop() )

												# Pushing items back to the heap not to clear it.
												for item in result:
																		self.insert(_UpperCamelCase )

												return iter(_UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    SkewNode.merge(self._root  ,    SkewNode(_UpperCamelCase ) )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.top()
												lowerCAmelCase__         =    (
												    SkewNode.merge(self._root.left  ,    self._root.right ) if self._root else None
												)

												return result






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.' )
												return self._root.value







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    None


if __name__ == "__main__":
					import doctest

					doctest.testmod()





