


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



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    _lowercase       :							Dict      		=		FairseqRobertaModel.from_pretrained(snake_case__ )
    roberta.eval()  # disable dropout
    _lowercase       :							Union[str, Any]      		=		roberta.model.encoder.sentence_encoder
    _lowercase       :							Optional[int]      		=		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:
        _lowercase       :							Optional[int]      		=		roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

    print('Our RoBERTa config:'							, snake_case__ )

    _lowercase       :							Any      		=		XLMRobertaXLForSequenceClassification(snake_case__ ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__ )
    model.eval()

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

    _lowercase       :							Any      		=		roberta_sent_encoder.layer_norm.weight
    _lowercase       :							List[Any]      		=		roberta_sent_encoder.layer_norm.bias

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

        _lowercase       :							RobertaAttention      		=		layer.attention
        _lowercase       :							Any      		=		roberta_layer.self_attn_layer_norm.weight
        _lowercase       :							List[str]      		=		roberta_layer.self_attn_layer_norm.bias

        # self attention
        _lowercase       :							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) )
        )

        _lowercase       :							int      		=		roberta_layer.self_attn.q_proj.weight
        _lowercase       :							int      		=		roberta_layer.self_attn.q_proj.bias
        _lowercase       :							Dict      		=		roberta_layer.self_attn.k_proj.weight
        _lowercase       :							Union[str, Any]      		=		roberta_layer.self_attn.k_proj.bias
        _lowercase       :							Any      		=		roberta_layer.self_attn.v_proj.weight
        _lowercase       :							int      		=		roberta_layer.self_attn.v_proj.bias

        # self-attention output
        _lowercase       :							BertSelfOutput      		=		layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        _lowercase       :							Dict      		=		roberta_layer.self_attn.out_proj.weight
        _lowercase       :							Dict      		=		roberta_layer.self_attn.out_proj.bias

        # this one is final layer norm
        _lowercase       :							Union[str, Any]      		=		roberta_layer.final_layer_norm.weight
        _lowercase       :							Optional[int]      		=		roberta_layer.final_layer_norm.bias

        # intermediate
        _lowercase       :							BertIntermediate      		=		layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        _lowercase       :							Tuple      		=		roberta_layer.fca.weight
        _lowercase       :							Optional[Any]      		=		roberta_layer.fca.bias

        # output
        _lowercase       :							BertOutput      		=		layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        _lowercase       :							Optional[int]      		=		roberta_layer.fca.weight
        _lowercase       :							Tuple      		=		roberta_layer.fca.bias
        # end of layer

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

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      Tuple												= 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       :      Optional[Any]												= parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )








"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


