

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()
UpperCamelCase__	:   str								=     logging.get_logger(__name__)

UpperCamelCase__	:   int								=     "Hello world! cécé herlolip"


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		str			, _SCREAMING_SNAKE_CASE       :		str			, _SCREAMING_SNAKE_CASE       :		bool							):





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_						=    RobertaForSequenceClassification(_SCREAMING_SNAKE_CASE							) if classification_head else RobertaForMaskedLM(_SCREAMING_SNAKE_CASE							)
    model.eval()

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

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

        # self attention
        SCREAMING_SNAKE_CASE_						=    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)							)
        )

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

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

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

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

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

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

    SCREAMING_SNAKE_CASE_						=    model(_SCREAMING_SNAKE_CASE							)[0]
    if classification_head:
        SCREAMING_SNAKE_CASE_						=    roberta.model.classification_heads['mnli'](roberta.extract_features(_SCREAMING_SNAKE_CASE							)							)
    else:
        SCREAMING_SNAKE_CASE_						=    roberta.model(_SCREAMING_SNAKE_CASE							)[0]
    print(our_output.shape			, their_output.shape							)
    SCREAMING_SNAKE_CASE_						=    torch.max(torch.abs(our_output - their_output							)							).item()
    print(f"""max_absolute_diff = {max_absolute_diff}"""							)  # ~ 1e-7
    SCREAMING_SNAKE_CASE_						=    torch.allclose(_SCREAMING_SNAKE_CASE			, _SCREAMING_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(_SCREAMING_SNAKE_CASE							).mkdir(parents=_SCREAMING_SNAKE_CASE			, exist_ok=_SCREAMING_SNAKE_CASE							)
    print(f"""Saving model to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       UpperCamelCase__	:   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."
       )
       UpperCamelCase__	:   List[str]								=     parser.parse_args()
       convert_roberta_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    task_template_dict.get('task'							)
    if task_name is None:
        logger.warning(f"""Couldn't find template for task '{task_name}'. Available templates: {list(_SCREAMING_SNAKE_CASE							)}"""							)
        return None
    SCREAMING_SNAKE_CASE_						=    NAMEaTEMPLATE.get(_SCREAMING_SNAKE_CASE							)
    return template.from_dict(_SCREAMING_SNAKE_CASE							)


