




'''simple docstring'''
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()
_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE =	"Hello world! cécé herlolip"





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		str:
			A_       		=   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
			roberta.eval()  # disable dropout
			A_       		=   roberta.model.encoder.sentence_encoder
			A_       		=   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:
						A_       		=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
			print('Our BERT config:'					,SCREAMING_SNAKE_CASE_    )

			A_       		=   RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_    ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_    )
			model.eval()

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

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

						# self attention
						A_       		=   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)    )
						)

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

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

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

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

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

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

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

			pathlib.Path(SCREAMING_SNAKE_CASE_    ).mkdir(parents=SCREAMING_SNAKE_CASE_					,exist_ok=SCREAMING_SNAKE_CASE_    )
			print(F'Saving model to {pytorch_dump_folder_path}'    )
			model.save_pretrained(SCREAMING_SNAKE_CASE_    )


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










'''simple docstring'''
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)
class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		["""input_ids""", """attention_mask"""]



					def __init__(  self    :	str			,					_lowercase    :	List[Any]="[PAD]"			,					_lowercase    :	Any="[BOS]"			,					_lowercase    :	Any="[EOS]"			,					_lowercase    :	str="[MASK]"			,					_lowercase    :	str="[CLS]"			,					_lowercase    :	Optional[Any]="[SEP]"			,					_lowercase    :	List[str]=2_048			,					**_lowercase    :	int			,					)						->       None:
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else pad_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else bos_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else eos_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else mask_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else cls_token
								A_       		=   AddedToken(_lowercase			,					lstrip=_lowercase			,					rstrip=_lowercase) if isinstance(_lowercase			,					_lowercase) else sep_token

								super().__init__(
								    pad_token=_lowercase			,					bos_token=_lowercase			,					eos_token=_lowercase			,					mask_token=_lowercase			,					cls_token=_lowercase			,					sep_token=_lowercase			,					model_max_length=_lowercase			,					**_lowercase			,					)

								A_       		=   2**8  # utf is 8 bits

								# define special tokens dict
								A_       		=   {
								    self.pad_token: 0,
								    self.bos_token: 1,
								    self.eos_token: 2,
								    self.mask_token: 3,
								    self.cls_token: 4,
								    self.sep_token: 5,
								}
								A_       		=   len(self.special_tokens_encoder)
								A_       		=   {v: k for k, v in self.special_tokens_encoder.items()}



					def 						__snake_case	(  self    :	List[str])						->       Dict[str, int]:
								A_       		=   self.special_tokens_encoder.copy()
								vocab.update(self.added_tokens_encoder)
								for i in range(self._utf_vocab_size):
											A_       		=   chr(_lowercase)
											A_       		=   i + len(self.special_tokens_encoder)
								return vocab



					@property
					def 						__snake_case	(  self    :	int)						->       Dict:
								return self._utf_vocab_size + self._num_special_tokens



					def 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	List[int]			,					_lowercase    :	Optional[List[int]] = None			,					_lowercase    :	bool = False)						->       List[int]:
								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_lowercase			,					token_ids_a=_lowercase			,					already_has_special_tokens=_lowercase)

								# normal case: some special tokens
								if token_ids_a is None:
											return [1] + [0] * len(_lowercase) + [1]
								return [1] + ([0] * len(_lowercase)) + [1] + ([0] * len(_lowercase)) + [1]



					def 						__snake_case	(  self    :	int			,					_lowercase    :	List[int]			,					_lowercase    :	Optional[List[int]] = None)						->       List[int]:
								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								else:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



					def 						__snake_case	(  self    :	str			,					_lowercase    :	str)						->       List[str]:
								A_       		=   [chr(_lowercase) for i in text.encode('utf-8')]
								return tokens



					def 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	Optional[Any])						->       Tuple:
								if token in self.special_tokens_encoder:
											A_       		=   self.special_tokens_encoder[token]
								elif token in self.added_tokens_encoder:
											A_       		=   self.added_tokens_encoder[token]
								elif len(_lowercase) != 1:
											A_       		=   self.unk_token_id
								else:
											A_       		=   ord(_lowercase) + self._num_special_tokens
								return token_id



					def 						__snake_case	(  self    :	str			,					_lowercase    :	List[str])						->       Dict:
								if index in self.special_tokens_decoder:
											A_       		=   self.special_tokens_decoder[index]
								elif index in self.added_tokens_decoder:
											A_       		=   self.added_tokens_decoder[index]
								else:
											A_       		=   chr(index - self._num_special_tokens)
								return token



					def 						__snake_case	(  self    :	str			,					_lowercase    :	Dict)						->       int:
								A_       		=   B''
								for token in tokens:
											if token in self.special_tokens_decoder:
														A_       		=   self.special_tokens_decoder[token].encode('utf-8')
											elif token in self.added_tokens_decoder:
														A_       		=   self.special_tokens_decoder[token].encode('utf-8')
											elif token in self.special_tokens_encoder:
														A_       		=   token.encode('utf-8')
											elif token in self.added_tokens_encoder:
														A_       		=   token.encode('utf-8')
											else:
														A_       		=   bytes([ord(_lowercase)])
											bstring += tok_string
								A_       		=   bstring.decode('utf-8'			,					errors='replace')
								return string



					def 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	str			,					_lowercase    :	Optional[str] = None)						->       Tuple[str]:
								return ()




