





from __future__ import annotations





def  __UpperCamelCase   (     a,  a)			->list[int]:
					assert len(a) == len(a)

					lowerCamelCase__      =							len(a)
					lowerCamelCase__      =							list(range(a))
					lowerCamelCase__      =							[-1] * n  # who the donor has donated to
					lowerCamelCase__      =							[-1] * n  # who the recipient has received from
					lowerCamelCase__      =							[0] * n

					while unmatched_donors:
										lowerCamelCase__      =							unmatched_donors[0]
										lowerCamelCase__      =							donor_pref[donor]
										lowerCamelCase__      =							donor_preference[num_donations[donor]]
										num_donations[donor] += 1
										lowerCamelCase__      =							recipient_pref[recipient]
										lowerCamelCase__      =							rec_record[recipient]

										if prev_donor != -1:
															if rec_preference.index(a) > rec_preference.index(a):
																				lowerCamelCase__      =							donor
																				lowerCamelCase__      =							recipient
																				unmatched_donors.append(a)
																				unmatched_donors.remove(a)
										else:
															lowerCamelCase__      =							donor
															lowerCamelCase__      =							recipient
															unmatched_donors.remove(a)
					return donor_record








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
						)

