
"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   params

											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([len(SCREAMING_SNAKE_CASE__    ) for t in data]    )

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()



				def __getitem__(self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return (self.token_ids[index], self.lengths[index])



				def __len__(self    )					->		int:



											"""simple docstring"""




											return len(self.lengths    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											assert len(self.token_ids    ) == len(self.lengths    )
											assert all(self.lengths[i] == len(self.token_ids[i]    ) for i in range(len(self.lengths    )    )    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.params.max_model_input_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.lengths > max_len
											logger.info(F'''Splitting {sum(SCREAMING_SNAKE_CASE__    )} too long sequences.'''    )

											def divide_chunks(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		return [l[i : i + n] for i in range(0   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )]

											SCREAMING_SNAKE_CASE__						:       Tuple           =   []
											SCREAMING_SNAKE_CASE__						:       Any           =   []
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
											else:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

											for seq_, len_ in zip(self.token_ids   ,       self.lengths    ):
																		assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																		if len_ <= max_len:
																									new_tok_ids.append(seq_    )
																									new_lengths.append(len_    )
																		else:
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
																									for sub_s in divide_chunks(seq_   ,       max_len - 2    ):
																																if sub_s[0] != cls_id:
																																							SCREAMING_SNAKE_CASE__						:       int           =   np.insert(SCREAMING_SNAKE_CASE__   ,       0   ,       SCREAMING_SNAKE_CASE__    )
																																if sub_s[-1] != sep_id:
																																							SCREAMING_SNAKE_CASE__						:       str           =   np.insert(SCREAMING_SNAKE_CASE__   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )
																																assert len(SCREAMING_SNAKE_CASE__    ) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(SCREAMING_SNAKE_CASE__    )

																									new_tok_ids.extend(SCREAMING_SNAKE_CASE__    )
																									new_lengths.extend([len(SCREAMING_SNAKE_CASE__    ) for l in sub_seqs]    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   np.array(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths > 11
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if "unk_token" not in self.params.special_tok_ids:
																		return
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.params.special_tok_ids["""unk_token"""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       str           =   np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   (unk_occs / self.lengths) < 0.5
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if not self.params.is_master:
																		return
											logger.info(F'''{len(self    )} sequences'''    )
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [t[0] for t in batch]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [t[1] for t in batch]
											assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

											# Max for paddings
											SCREAMING_SNAKE_CASE__						:       int           =   max(SCREAMING_SNAKE_CASE__    )

											# Pad token ids
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.params.special_tok_ids["""pad_token"""]
											else:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""unk_token"""]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [list(t.astype(SCREAMING_SNAKE_CASE__    )    ) + [pad_idx] * (max_seq_len_ - len(SCREAMING_SNAKE_CASE__    )) for t in token_ids]
											assert len(tk_    ) == len(SCREAMING_SNAKE_CASE__    )
											assert all(len(SCREAMING_SNAKE_CASE__    ) == max_seq_len_ for t in tk_    )

											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(tk_    )  # (bs, max_seq_len_)
											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(SCREAMING_SNAKE_CASE__    )  # (bs)
											return tk_t, lg_t






"""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 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()
UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       Any           =   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=514   ,type_vocab_size=1   ,layer_norm_eps=1E-5   ,)
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   XLMRobertaXLForSequenceClassification(_snake_case						) if classification_head else XLMRobertaXLForMaskedLM(_snake_case						)
							model.eval()

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

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.layer_norm.bias

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

														SCREAMING_SNAKE_CASE__						:       RobertaAttention           =   layer.attention
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE__						:       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)						)
														)

														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__						:       int           =   roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.v_proj.bias

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

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE__						:       BertOutput           =   layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_layer.fca.bias
														# end of layer

							if classification_head:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.classification_heads["""mnli"""].dense.weight
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].dense.bias
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.weight
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.classification_heads["""mnli"""].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE__						:       str           =   roberta.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta.model.encoder.lm_head.bias

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_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(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Union[str, Any]    							= 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__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




