





"""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 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	:						Optional[int]					=					logging.get_logger(__name__)

_snake_case	:						List[str]					=					"""Hello world! cécé herlolip"""






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							A						=    FairseqRobertaModel.from_pretrained(_lowerCAmelCase			)
							roberta.eval()  # disable dropout
							A						=    roberta.model.encoder.sentence_encoder
							A						=    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:
														A						=    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
							print("Our BERT config:"   ,    _lowerCAmelCase			)

							A						=    RobertaForSequenceClassification(_lowerCAmelCase			) if classification_head else RobertaForMaskedLM(_lowerCAmelCase			)
							model.eval()

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

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

														# self attention
														A						=    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						=    roberta_layer.self_attn.q_proj.weight
														A						=    roberta_layer.self_attn.q_proj.bias
														A						=    roberta_layer.self_attn.k_proj.weight
														A						=    roberta_layer.self_attn.k_proj.bias
														A						=    roberta_layer.self_attn.v_proj.weight
														A						=    roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

							A						=    model(_lowerCAmelCase			)[0]
							if classification_head:
														A						=    roberta.model.classification_heads["mnli"](roberta.extract_features(_lowerCAmelCase			)			)
							else:
														A						=    roberta.model(_lowerCAmelCase			)[0]
							print(our_output.shape   ,    their_output.shape			)
							A						=    torch.max(torch.abs(our_output - their_output			)			).item()
							print(F"max_absolute_diff = {max_absolute_diff}"			)  # ~ 1e-7
							A						=    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__":
		_snake_case	:						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.'
		)
		_snake_case	:						List[Any]					=					parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)







def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 9 / 5) + 32 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(float(_lowerCAmelCase) + 273.15 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 9 / 5) + 491.67 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) - 32) * 5 / 9 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(((float(_lowerCAmelCase) - 32) * 5 / 9) + 273.15 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(float(_lowerCAmelCase) + 459.67 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(float(_lowerCAmelCase) - 273.15 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(((float(_lowerCAmelCase) - 273.15) * 9 / 5) + 32 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 9 / 5) ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) - 491.67) * 5 / 9 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round(float(_lowerCAmelCase) - 459.67 ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 5 / 9) ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 1.25 + 273.15) ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 2.25 + 32) ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 1.25) ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase = 2):
					return round((float(_lowerCAmelCase) * 2.25 + 32 + 459.67) ,						_lowerCAmelCase)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
