



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()
__snake_case						:Any						=					logging.get_logger(__name__)

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





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    FairseqRobertaModel.from_pretrained(_UpperCAmelCase    )
    roberta.eval()  # disable dropout
    __a							=    roberta.model.encoder.sentence_encoder
    __a							=    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							=    roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

    __a							=    XLMRobertaXLForSequenceClassification(_UpperCAmelCase    ) if classification_head else XLMRobertaXLForMaskedLM(_UpperCAmelCase    )
    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.layer_norm.weight
    __a							=    roberta_sent_encoder.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]

        __a							=    layer.attention
        __a							=    roberta_layer.self_attn_layer_norm.weight
        __a							=    roberta_layer.self_attn_layer_norm.bias

        # 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

        # this one is final layer norm
        __a							=    roberta_layer.final_layer_norm.weight
        __a							=    roberta_layer.final_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
        # 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(_UpperCAmelCase    ).unsqueeze(0    )  # batch of size 1

    __a							=    model(_UpperCAmelCase    )[0]
    if classification_head:
        __a							=    roberta.model.classification_heads['''mnli'''](roberta.extract_features(_UpperCAmelCase    )    )
    else:
        __a							=    roberta.model(_UpperCAmelCase    )[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(_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						: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						:Tuple						=					parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )










def 					__snake_case     (      _UpperCAmelCase = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


if __name__ == "__main__":
 print(f'{solution() = }')





