


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
else:
    lowerCAmelCase__   	=				None


lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__   	=				{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__   	=				{
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
lowerCAmelCase__   	=				['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE						    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE						    =				PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE						    =				["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE						    =				MBartaaTokenizer

     __SCREAMING_SNAKE_CASE						    =				[]
     __SCREAMING_SNAKE_CASE						    =				[]


     def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="<s>"				,		__lowerCamelCase="<unk>"				,		__lowerCamelCase="<pad>"				,		__lowerCamelCase="<mask>"				,		**__lowerCamelCase				,		)    ->       str:
            # Mask token behave like a normal word, i.e. include the space before it
            _A   : List[str]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else mask_token

            _A   : List[Any]       					=				kwargs.get("additional_special_tokens"				,		[])
            kwargs["additional_special_tokens"] += [
                code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
            ]

            super().__init__(
                __lowerCamelCase				,		src_lang=__lowerCamelCase				,		tgt_lang=__lowerCamelCase				,		tokenizer_file=__lowerCamelCase				,		eos_token=__lowerCamelCase				,		sep_token=__lowerCamelCase				,		cls_token=__lowerCamelCase				,		unk_token=__lowerCamelCase				,		pad_token=__lowerCamelCase				,		mask_token=__lowerCamelCase				,		**__lowerCamelCase				,		)

            _A   : str       					=				vocab_file
            _A   : Any       					=				False if not self.vocab_file else True

            _A   : Optional[Any]       					=				{
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A   : List[str]       					=				src_lang if src_lang is not None else "en_XX"
            _A   : int       					=				tgt_lang
            _A   : Any       					=				self.lang_code_to_id[self._src_lang]
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def 				_lowerCamelCase				(		self)    ->       str:
            return self._src_lang


     @src_lang.setter
     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            _A   : Optional[Any]       					=				new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = "en_XX"				,		__lowerCamelCase = None				,		__lowerCamelCase = "ro_RO"				,		**__lowerCamelCase				,		)    ->       BatchEncoding:
            _A   : int       					=				src_lang
            _A   : List[str]       					=				tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)


     def 				_lowerCamelCase				(		self)    ->       List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def 				_lowerCamelCase				(		self)    ->       Optional[int]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            _A   : List[Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
            _A   : Union[str, Any]       					=				[self.cur_lang_code_id]
            _A   : List[str]       					=				[self.eos_token_id]

            _A   : Tuple       					=				self.convert_ids_to_tokens(self.prefix_tokens)
            _A   : List[str]       					=				self.convert_ids_to_tokens(self.suffix_tokens)

            _A   : List[str]       					=				processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,		pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,		self.prefix_tokens + self.suffix_tokens))				,		)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
            _A   : Union[str, Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
            _A   : Dict       					=				[self.cur_lang_code_id]
            _A   : Optional[int]       					=				[self.eos_token_id]

            _A   : List[str]       					=				self.convert_ids_to_tokens(self.prefix_tokens)
            _A   : Tuple       					=				self.convert_ids_to_tokens(self.suffix_tokens)

            _A   : int       					=				processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,		pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,		self.prefix_tokens + self.suffix_tokens))				,		)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       int:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A   : Optional[int]       					=				src_lang
            _A   : Union[str, Any]       					=				self(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase				,		return_tensors=__lowerCamelCase				,		**__lowerCamelCase)
            _A   : int       					=				self.convert_tokens_to_ids(__lowerCamelCase)
            _A   : List[Any]       					=				tgt_lang_id
            return inputs


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory")
                   return
            _A   : List[str]       					=				os.path.join(
                __lowerCamelCase				,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

            if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                   copyfile(self.vocab_file				,		__lowerCamelCase)

            return (out_vocab_file,)




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()
lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				'Hello world! cécé herlolip'
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool		):
       _A   : Union[str, Any]       					=				FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
       roberta.eval()  # disable dropout
       _A   : Dict       					=				roberta.model.encoder.sentence_encoder
       _A   : int       					=				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   : List[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our BERT config:"       ,							UpperCamelCase__		)

       _A   : Dict       					=				RobertaForSequenceClassification(UpperCamelCase__		) if classification_head else RobertaForMaskedLM(UpperCamelCase__		)
       model.eval()

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

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

              # self attention
              _A   : 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)		)
              )

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

              # self-attention output
              _A   : BertSelfOutput       					=				layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _A   : Tuple       					=				roberta_layer.self_attn.out_proj.weight
              _A   : List[str]       					=				roberta_layer.self_attn.out_proj.bias
              _A   : Dict       					=				roberta_layer.self_attn_layer_norm.weight
              _A   : Optional[int]       					=				roberta_layer.self_attn_layer_norm.bias

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

              # output
              _A   : BertOutput       					=				layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _A   : int       					=				roberta_layer.fca.weight
              _A   : List[str]       					=				roberta_layer.fca.bias
              _A   : List[str]       					=				roberta_layer.final_layer_norm.weight
              _A   : Any       					=				roberta_layer.final_layer_norm.bias
              # end of layer

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

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

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