"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
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 sapiens_transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from sapiens_transformers.models.bert.modeling_bert import (BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput)
from sapiens_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()
logger = logging.get_logger(__name__)
SAMPLE_TEXT = "Hello world! cécé herlolip"
def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool):
    roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
    roberta.eval()
    roberta_sent_encoder = roberta.model.encoder.sentence_encoder
    config = 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: config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
    print("Our BERT config:", config)
    model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
    model.eval()
    model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
    model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
    model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(model.roberta.embeddings.token_type_embeddings.weight)
    model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
    model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
    for i in range(config.num_hidden_layers):
        layer: BertLayer = model.roberta.encoder.layer[i]
        roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
        self_attn: 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)))
        self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
        self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
        self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
        self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
        self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
        self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
        self_output: BertSelfOutput = layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
        self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
        self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
        self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
        intermediate: BertIntermediate = layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
        intermediate.dense.weight = roberta_layer.fc1.weight
        intermediate.dense.bias = roberta_layer.fc1.bias
        bert_output: BertOutput = layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
        bert_output.dense.weight = roberta_layer.fc2.weight
        bert_output.dense.bias = roberta_layer.fc2.bias
        bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
        bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
    if classification_head:
        model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
        model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
        model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
        model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
    else:
        model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight
        model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias
        model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight
        model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias
        model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight
        model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias
    input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0)
    our_output = model(input_ids)[0]
    if classification_head: their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
    else: their_output = roberta.model(input_ids)[0]
    print(our_output.shape, their_output.shape)
    max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
    print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
    success = torch.allclose(our_output, their_output, atol=1e-3)
    print("Do both models output the same tensors?", "🔥" if success else "💩")
    if not success: raise Exception("Something went wRoNg")
    pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
    print(f"Saving model to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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.")
    args = parser.parse_args()
    convert_roberta_checkpoint_to_pytorch(args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
