





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def  __UpperCamelCase   (     a)			->Optional[int]:
					if "model" in orig_key:
										lowerCamelCase__      =							orig_key.replace("model.",  "")
					if "norm1" in orig_key:
										lowerCamelCase__      =							orig_key.replace("norm1",  "attention.output.LayerNorm")
					if "norm2" in orig_key:
										lowerCamelCase__      =							orig_key.replace("norm2",  "output.LayerNorm")
					if "norm" in orig_key:
										lowerCamelCase__      =							orig_key.replace("norm",  "LayerNorm")
					if "transformer" in orig_key:
										lowerCamelCase__      =							orig_key.split(".")[0].split("_")[-1]
										lowerCamelCase__      =							orig_key.replace(f"transformer_{layer_num}",  f"encoder.layer.{layer_num}")
					if "mha.attn" in orig_key:
										lowerCamelCase__      =							orig_key.replace("mha.attn",  "attention.self")
					if "mha" in orig_key:
										lowerCamelCase__      =							orig_key.replace("mha",  "attention")
					if "W_q" in orig_key:
										lowerCamelCase__      =							orig_key.replace("W_q",  "self.query")
					if "W_k" in orig_key:
										lowerCamelCase__      =							orig_key.replace("W_k",  "self.key")
					if "W_v" in orig_key:
										lowerCamelCase__      =							orig_key.replace("W_v",  "self.value")
					if "ff.0" in orig_key:
										lowerCamelCase__      =							orig_key.replace("ff.0",  "intermediate.dense")
					if "ff.2" in orig_key:
										lowerCamelCase__      =							orig_key.replace("ff.2",  "output.dense")
					if "ff" in orig_key:
										lowerCamelCase__      =							orig_key.replace("ff",  "output.dense")
					if "mlm_class" in orig_key:
										lowerCamelCase__      =							orig_key.replace("mlm.mlm_class",  "cls.predictions.decoder")
					if "mlm" in orig_key:
										lowerCamelCase__      =							orig_key.replace("mlm",  "cls.predictions.transform")
					if "backbone.backbone.encoders" in orig_key:
										lowerCamelCase__      =							orig_key.replace("backbone.backbone.encoders",  "encoder.layer")
					if "cls" not in orig_key:
										lowerCamelCase__      =							"mra." + orig_key

					return orig_key





def  __UpperCamelCase   (     a,  a)			->List[Any]:
					for key in orig_state_dict.copy().keys():
										lowerCamelCase__      =							orig_state_dict.pop(a)

										if ("pooler" in key) or ("sen_class" in key):
															continue
										else:
															lowerCamelCase__      =							val

					lowerCamelCase__      =							orig_state_dict["cls.predictions.decoder.bias"]
					lowerCamelCase__      =							torch.arange(a).expand((1, -1)) + 2

					return orig_state_dict





def  __UpperCamelCase   (     a,  a,  a)			->Optional[Any]:
					lowerCamelCase__      =							torch.load(a,  map_location="cpu")["model_state_dict"]
					lowerCamelCase__      =							MraConfig.from_json_file(a)
					lowerCamelCase__      =							MraForMaskedLM(a)

					lowerCamelCase__      =							convert_checkpoint_helper(config.max_position_embeddings,  a)

					print(model.load_state_dict(a))
					model.eval()
					model.save_pretrained(a)

					print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")


if __name__ == "__main__":
						A_        =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The json file for Mra model config.",
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						A_        =  parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








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()
A_        =  logging.get_logger(__name__)

A_        =  "Hello world! cécé herlolip"





def  __UpperCamelCase   (     a,  a,  a)			->Any:
					lowerCamelCase__      =							FairseqRobertaModel.from_pretrained(a)
					roberta.eval()  # disable dropout
					lowerCamelCase__      =							roberta.model.encoder.sentence_encoder
					lowerCamelCase__      =							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:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

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

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

										# self attention
										lowerCamelCase__      =							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))
										)

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

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

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

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

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

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

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

					pathlib.Path(a).mkdir(parents=a,  exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


if __name__ == "__main__":
						A_        =  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."
						)
						A_        =  parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)

