
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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
lowercase        =  logging.get_logger(__name__)

lowercase        =  '''Hello world! cécé herlolip'''



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool       )  ->    List[str]:
							lowerCamelCase_							  =					FairseqRobertaModel.from_pretrained(UpperCAmelCase__       )
							roberta.eval()  # disable dropout
							lowerCamelCase_							  =					roberta.model.encoder.sentence_encoder
							lowerCamelCase_							  =					XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings			,				hidden_size=roberta.cfg.model.encoder_embed_dim			,				num_hidden_layers=roberta.cfg.model.encoder_layers			,				num_attention_heads=roberta.cfg.model.encoder_attention_heads			,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim			,				max_position_embeddings=5_1_4			,				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 RoBERTa config:"""			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					XLMRobertaXLForSequenceClassification(UpperCAmelCase__       ) if classification_head else XLMRobertaXLForMaskedLM(UpperCAmelCase__       )
							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.layer_norm.weight
							lowerCamelCase_							  =					roberta_sent_encoder.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]

														lowerCamelCase_							  =					layer.attention
														lowerCamelCase_							  =					roberta_layer.self_attn_layer_norm.weight
														lowerCamelCase_							  =					roberta_layer.self_attn_layer_norm.bias

														# 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

														# this one is final layer norm
														lowerCamelCase_							  =					roberta_layer.final_layer_norm.weight
														lowerCamelCase_							  =					roberta_layer.final_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
														# 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(UpperCAmelCase__       ).unsqueeze(0       )  # batch of size 1

							lowerCamelCase_							  =					model(UpperCAmelCase__       )[0]
							if classification_head:
														lowerCamelCase_							  =					roberta.model.classification_heads["""mnli"""](roberta.extract_features(UpperCAmelCase__       )       )
							else:
														lowerCamelCase_							  =					roberta.model(UpperCAmelCase__       )[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(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__":
		lowercase        =  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.'''
		)
		lowercase        =  parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)







import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
		from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    List[str]:
							if is_torch_version("""<"""			,				"""2.0.0"""       ) or not hasattr(UpperCAmelCase__			,				"""_dynamo"""       ):
														return False
							return isinstance(UpperCAmelCase__			,				torch._dynamo.eval_frame.OptimizedModule       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      bool = True       )  ->    str:
							lowerCamelCase_							  =					(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							lowerCamelCase_							  =					is_compiled_module(UpperCAmelCase__       )
							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					model.module

							if not keep_fpaa_wrapper:
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""forward"""       )
														lowerCamelCase_							  =					model.__dict__.pop("""_original_forward"""			,				UpperCAmelCase__       )
														if original_forward is not None:
																					while hasattr(UpperCAmelCase__			,				"""__wrapped__"""       ):
																												lowerCamelCase_							  =					forward.__wrapped__
																												if forward == original_forward:
																																			break
																					lowerCamelCase_							  =					forward
														if getattr(UpperCAmelCase__			,				"""_converted_to_transformer_engine"""			,				UpperCAmelCase__       ):
																					convert_model(UpperCAmelCase__			,				to_transformer_engine=UpperCAmelCase__       )

							if is_compiled:
														lowerCamelCase_							  =					model
														lowerCamelCase_							  =					compiled_model

							return model



def      __lowerCAmelCase						(							)  ->    Any:
							PartialState().wait_for_everyone()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Tuple       )  ->    Dict:
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(UpperCAmelCase__			,				UpperCAmelCase__       )
							elif PartialState().local_process_index == 0:
														torch.save(UpperCAmelCase__			,				UpperCAmelCase__       )



@contextmanager
def      __lowerCAmelCase						(							**UpperCAmelCase__				:      Any       )  ->    Dict:
							for key, value in kwargs.items():
														lowerCamelCase_							  =					str(UpperCAmelCase__       )

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    str:
							if not hasattr(UpperCAmelCase__			,				"""__qualname__"""       ) and not hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				"""__class__"""			,				UpperCAmelCase__       )
							if hasattr(UpperCAmelCase__			,				"""__qualname__"""       ):
														return obj.__qualname__
							if hasattr(UpperCAmelCase__			,				"""__name__"""       ):
														return obj.__name__
							return str(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:
							for key, value in source.items():
														if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
																					lowerCamelCase_							  =					destination.setdefault(UpperCAmelCase__			,				{}       )
																					merge_dicts(UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					lowerCamelCase_							  =					value

							return destination



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = None       )  ->    bool:
							if port is None:
														lowerCamelCase_							  =					2_9_5_0_0
							with socket.socket(socket.AF_INET			,				socket.SOCK_STREAM       ) as s:
														return s.connect_ex(("""localhost""", port)       ) == 0





