





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()
UpperCAmelCase__  :	Tuple     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[Any]     ='''Hello world! cécé herlolip'''





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[str]:
				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=5_14	,					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__":
							UpperCAmelCase__  :	Dict     =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.'''
							)
							UpperCAmelCase__  :	Dict     =parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)













from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



