



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 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   bool		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[int]						=      FairseqRobertaModel.from_pretrained(_UpperCAmelCase		)
		roberta.eval()  # disable dropout
		__UpperCAmelCase       :	List[Any]						=      roberta.model.encoder.sentence_encoder
		__UpperCAmelCase       :	List[Any]						=      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=5_14					,  type_vocab_size=1					,  layer_norm_eps=1e-5					,  )
		if classification_head:
				__UpperCAmelCase       :	Union[str, Any]						=      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
		print('''Our BERT config:'''					,  _UpperCAmelCase		)

		__UpperCAmelCase       :	Optional[int]						=      RobertaForSequenceClassification(_UpperCAmelCase		) if classification_head else RobertaForMaskedLM(_UpperCAmelCase		)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		__UpperCAmelCase       :	Union[str, Any]						=      roberta_sent_encoder.embed_tokens.weight
		__UpperCAmelCase       :	int						=      roberta_sent_encoder.embed_positions.weight
		__UpperCAmelCase       :	Dict						=      torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
		__UpperCAmelCase       :	Any						=      roberta_sent_encoder.emb_layer_norm.weight
		__UpperCAmelCase       :	Union[str, Any]						=      roberta_sent_encoder.emb_layer_norm.bias

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

				# self attention
				__UpperCAmelCase       :	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)		)
				)

				__UpperCAmelCase       :	Dict						=      roberta_layer.self_attn.q_proj.weight
				__UpperCAmelCase       :	List[str]						=      roberta_layer.self_attn.q_proj.bias
				__UpperCAmelCase       :	Any						=      roberta_layer.self_attn.k_proj.weight
				__UpperCAmelCase       :	Optional[int]						=      roberta_layer.self_attn.k_proj.bias
				__UpperCAmelCase       :	List[Any]						=      roberta_layer.self_attn.v_proj.weight
				__UpperCAmelCase       :	List[Any]						=      roberta_layer.self_attn.v_proj.bias

				# self-attention output
				__UpperCAmelCase       :	BertSelfOutput						=      layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				__UpperCAmelCase       :	Union[str, Any]						=      roberta_layer.self_attn.out_proj.weight
				__UpperCAmelCase       :	Dict						=      roberta_layer.self_attn.out_proj.bias
				__UpperCAmelCase       :	List[str]						=      roberta_layer.self_attn_layer_norm.weight
				__UpperCAmelCase       :	Optional[int]						=      roberta_layer.self_attn_layer_norm.bias

				# intermediate
				__UpperCAmelCase       :	BertIntermediate						=      layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				__UpperCAmelCase       :	Optional[Any]						=      roberta_layer.fca.weight
				__UpperCAmelCase       :	Union[str, Any]						=      roberta_layer.fca.bias

				# output
				__UpperCAmelCase       :	BertOutput						=      layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__UpperCAmelCase       :	Union[str, Any]						=      roberta_layer.fca.weight
				__UpperCAmelCase       :	List[Any]						=      roberta_layer.fca.bias
				__UpperCAmelCase       :	Any						=      roberta_layer.final_layer_norm.weight
				__UpperCAmelCase       :	List[str]						=      roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				__UpperCAmelCase       :	Dict						=      roberta.model.classification_heads['''mnli'''].dense.weight
				__UpperCAmelCase       :	Optional[Any]						=      roberta.model.classification_heads['''mnli'''].dense.bias
				__UpperCAmelCase       :	List[str]						=      roberta.model.classification_heads['''mnli'''].out_proj.weight
				__UpperCAmelCase       :	Tuple						=      roberta.model.classification_heads['''mnli'''].out_proj.bias
		else:
				# LM Head
				__UpperCAmelCase       :	List[Any]						=      roberta.model.encoder.lm_head.dense.weight
				__UpperCAmelCase       :	Tuple						=      roberta.model.encoder.lm_head.dense.bias
				__UpperCAmelCase       :	List[str]						=      roberta.model.encoder.lm_head.layer_norm.weight
				__UpperCAmelCase       :	List[Any]						=      roberta.model.encoder.lm_head.layer_norm.bias
				__UpperCAmelCase       :	str						=      roberta.model.encoder.lm_head.weight
				__UpperCAmelCase       :	List[str]						=      roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		__UpperCAmelCase       :	torch.Tensor						=      roberta.encode(_UpperCAmelCase		).unsqueeze(0		)  # batch of size 1

		__UpperCAmelCase       :	Optional[int]						=      model(_UpperCAmelCase		)[0]
		if classification_head:
				__UpperCAmelCase       :	Any						=      roberta.model.classification_heads['''mnli'''](roberta.extract_features(_UpperCAmelCase		)		)
		else:
				__UpperCAmelCase       :	Union[str, Any]						=      roberta.model(_UpperCAmelCase		)[0]
		print(our_output.shape					,  their_output.shape		)
		__UpperCAmelCase       :	int						=      torch.max(torch.abs(our_output - their_output		)		).item()
		print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
		__UpperCAmelCase       :	Any						=      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__":
				__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
				)










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





