




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__    :					Dict					=						logging.get_logger(__name__)

lowercase__    :					List[Any]					=						{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__    :					Tuple					=						{
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase__    :					Union[str, Any]					=						{"Helsinki-NLP/opus-mt-en-de": 512}
lowercase__    :					Optional[Any]					=						{}
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       VOCAB_FILES_NAMES
						lowerCAmelCase_							=       PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=       PRETRAINED_INIT_CONFIGURATION
						lowerCAmelCase_							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=       ['''input_ids''', '''attention_mask''']
						lowerCAmelCase_							=       re.compile('''>>.+<<'''      )  # type: re.Pattern






						def __init__(						self							:					List[str]	,     __lowercase							:					Any	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					List[str]	,     __lowercase							:					List[str]=None	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					Tuple=None	,     __lowercase							:					Optional[Any]="<unk>"	,     __lowercase							:					int="</s>"	,     __lowercase							:					List[str]="<pad>"	,     __lowercase							:					Optional[Any]=5_12	,     __lowercase							:					Optional[Dict[str, Any]] = None	,     __lowercase							:					Any=False	,     **__lowercase							:					str	,     ):






							"""simple docstring"""






							snake_case_		   =					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
							    source_lang=__lowercase	,     target_lang=__lowercase	,     unk_token=__lowercase	,     eos_token=__lowercase	,     pad_token=__lowercase	,     model_max_length=__lowercase	,     sp_model_kwargs=self.sp_model_kwargs	,     target_vocab_file=__lowercase	,     separate_vocabs=__lowercase	,     **__lowercase	,     )
							assert Path(__lowercase						).exists(), f"cannot find spm source {source_spm}"

							snake_case_		   =					separate_vocabs
							snake_case_		   =					load_json(__lowercase						)
							if self.unk_token not in self.encoder:
								raise KeyError("<unk> token must be in vocab"						)
							assert self.pad_token in self.encoder

							if separate_vocabs:
								snake_case_		   =					load_json(__lowercase						)
								snake_case_		   =					{v: k for k, v in self.target_encoder.items()}
								snake_case_		   =					[]
							else:
								snake_case_		   =					{v: k for k, v in self.encoder.items()}
								snake_case_		   =					[k for k in self.encoder if k.startswith(">>"						) and k.endswith("<<"						)]

							snake_case_		   =					source_lang
							snake_case_		   =					target_lang
							snake_case_		   =					[source_spm, target_spm]

							# load SentencePiece model for pre-processing
							snake_case_		   =					load_spm(__lowercase	,     self.sp_model_kwargs						)
							snake_case_		   =					load_spm(__lowercase	,     self.sp_model_kwargs						)
							snake_case_		   =					self.spm_source
							snake_case_		   =					self.encoder

							# Multilingual target side: default to using first supported language code.

							self._setup_normalizer()






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							try:
								from sacremoses import MosesPunctNormalizer

								snake_case_		   =					MosesPunctNormalizer(self.source_lang						).normalize
							except (ImportError, FileNotFoundError):
								warnings.warn("Recommended: pip install sacremoses."						)
								snake_case_		   =					lambda __lowercase						: x






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					str						):






							"""simple docstring"""






							return self.punc_normalizer(__lowercase						) if x else ""






						def 	snake_case__						(						self							:					Any	,     __lowercase							:					Optional[int]						):






							"""simple docstring"""






							return self.current_encoder.get(__lowercase	,     self.current_encoder[self.unk_token]						)






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					self.language_code_re.match(__lowercase						)
							snake_case_		   =					[match.group(0						)] if match else []
							return code, self.language_code_re.sub(""	,     __lowercase						)






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					str						):






							"""simple docstring"""






							snake_case_   ,				snake_case_		   =					self.remove_language_code(__lowercase						)
							snake_case_		   =					self.current_spm.encode(__lowercase	,     out_type=__lowercase						)
							return code + pieces






						def 	snake_case__						(						self							:					Dict	,     __lowercase							:					int						):






							"""simple docstring"""






							return self.decoder.get(__lowercase	,     self.unk_token						)






						def 	snake_case__						(						self							:					int	,     __lowercase							:					List[Any]	,     **__lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							return super().batch_decode(__lowercase	,     **__lowercase						)






						def 	snake_case__						(						self							:					Any	,     __lowercase							:					Optional[Any]	,     **__lowercase							:					Tuple						):






							"""simple docstring"""






							return super().decode(__lowercase	,     **__lowercase						)






						def 	snake_case__						(						self							:					str	,     __lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							snake_case_		   =					[]
							snake_case_		   =					""
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									out_string += sp_model.decode_pieces(__lowercase						) + token + " "
									snake_case_		   =					[]
								else:
									current_sub_tokens.append(__lowercase						)
							out_string += sp_model.decode_pieces(__lowercase						)
							return out_string.strip()






						def 	snake_case__						(						self							:					List[str]	,     __lowercase							:					Union[str, Any]	,     __lowercase							:					Optional[int]=None						):






							"""simple docstring"""






							if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					self.spm_source
							snake_case_		   =					self.encoder






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.spm_target
							if self.separate_vocabs:
								snake_case_		   =					self.target_encoder






						@property
						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							return len(self.encoder						)






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					str	,     __lowercase							:					Optional[str] = None						):






							"""simple docstring"""






							if not os.path.isdir(__lowercase						):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
								return
							snake_case_		   =					[]

							if self.separate_vocabs:
								snake_case_		   =					os.path.join(
								    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]	,     )
								snake_case_		   =					os.path.join(
								    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]	,     )
								save_json(self.encoder	,     __lowercase						)
								save_json(self.target_encoder	,     __lowercase						)
								saved_files.append(__lowercase						)
								saved_files.append(__lowercase						)
							else:
								snake_case_		   =					os.path.join(
								    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]						)
								save_json(self.encoder	,     __lowercase						)
								saved_files.append(__lowercase						)

							for spm_save_filename, spm_orig_path, spm_model in zip(
							    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]	,     self.spm_files	,     [self.spm_source, self.spm_target]	,     ):
								snake_case_		   =					os.path.join(
								    __lowercase	,     (filename_prefix + "-" if filename_prefix else "") + spm_save_filename						)
								if os.path.abspath(__lowercase						) != os.path.abspath(__lowercase						) and os.path.isfile(__lowercase						):
									copyfile(__lowercase	,     __lowercase						)
									saved_files.append(__lowercase						)
								elif not os.path.isfile(__lowercase						):
									with open(__lowercase	,     "wb"						) as fi:
										snake_case_		   =					spm_model.serialized_model_proto()
										fi.write(__lowercase						)
									saved_files.append(__lowercase						)

							return tuple(__lowercase						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							return self.get_src_vocab()






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							return dict(self.encoder	,     **self.added_tokens_encoder						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							return dict(self.target_encoder	,     **self.added_tokens_decoder						)






						def __getstate__(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.__dict__.copy()
							state.update(
							    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}						)
							return state






						def __setstate__(						self							:					Optional[Any]	,     __lowercase							:					Dict						):






							"""simple docstring"""






							snake_case_		   =					d

							# for backward compatibility
							if not hasattr(self	,     "sp_model_kwargs"						):
								snake_case_		   =					{}

							snake_case_   ,				snake_case_		   =					(load_spm(__lowercase	,     self.sp_model_kwargs						) for f in self.spm_files)
							snake_case_		   =					self.spm_source
							self._setup_normalizer()






						def 	snake_case__						(						self							:					Optional[int]	,     *__lowercase							:					Union[str, Any]	,     **__lowercase							:					Tuple						):






							"""simple docstring"""






							return 1






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							snake_case_		   =					set(self.all_special_ids						)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
							return [1 if x in all_special_ids else 0 for x in seq]






						def 	snake_case__						(						self							:					Any	,     __lowercase							:					List	,     __lowercase							:					Optional[List] = None	,     __lowercase							:					bool = False						):






							"""simple docstring"""






							if already_has_special_tokens:
								return self._special_token_mask(__lowercase						)
							elif token_ids_a is None:
								return self._special_token_mask(__lowercase						) + [1]
							else:
								return self._special_token_mask(token_ids_a + token_ids_a						) + [1]







def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					sentencepiece.SentencePieceProcessor(**_A							)
	spm.Load(_A							)
	return spm


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	with open(_A					,     "w"							) as f:
		json.dump(_A					,     _A					,     indent=2							)






def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	with open(_A					,     "r"							) as f:
		return json.load(_A							)






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()
lowercase__    :					Dict					=						logging.get_logger(__name__)

lowercase__    :					Any					=						"Hello world! cécé herlolip"


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					FairseqRobertaModel.from_pretrained(_A							)
	roberta.eval()  # disable dropout
	snake_case_		   =					roberta.model.encoder.sentence_encoder
	snake_case_		   =					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:
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
	print("Our BERT config:"					,     _A							)

	snake_case_		   =					RobertaForSequenceClassification(_A							) if classification_head else RobertaForMaskedLM(_A							)
	model.eval()

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

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

		# self attention
		snake_case_		   =					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)							)
		)

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

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

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

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

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

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

	snake_case_		   =					model(_A							)[0]
	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"](roberta.extract_features(_A							)							)
	else:
		snake_case_		   =					roberta.model(_A							)[0]
	print(our_output.shape					,     their_output.shape							)
	snake_case_		   =					torch.max(torch.abs(our_output - their_output							)							).item()
	print(f"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
	snake_case_		   =					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__":
		lowercase__    :					int					=						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__    :					Optional[Any]					=						parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
