


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_nllb import NllbTokenizer
else:
		lowerCamelCase						=		None


lowerCamelCase						=		logging.get_logger(__name__)


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

lowerCamelCase						=		{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase						=		{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
lowerCamelCase						=		['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']





class  _a							(    _lowercase):
	_a  :  Optional[Any]				   =							VOCAB_FILES_NAMES
	_a  :  str				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Optional[int]				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  Optional[Any]				   =							['''input_ids''', '''attention_mask''']
	_a  :  Tuple				   =							NllbTokenizer

	_a  :  List[int]				   =							[]
	_a  :  List[int]				   =							[]




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Dict=None		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="</s>"		, _SCREAMING_SNAKE_CASE						:		int="</s>"		, _SCREAMING_SNAKE_CASE						:		Dict="<s>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="<unk>"		, _SCREAMING_SNAKE_CASE						:		Dict="<pad>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<mask>"		, _SCREAMING_SNAKE_CASE						:		Any=None		, _SCREAMING_SNAKE_CASE						:		Any=None		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=None		, _SCREAMING_SNAKE_CASE						:		Any=False		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	int:
			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase__       :      Tuple   					= AddedToken(_SCREAMING_SNAKE_CASE		, lstrip=_SCREAMING_SNAKE_CASE		, rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) else mask_token
			lowerCAmelCase__       :      List[Any]   					= legacy_behaviour
			super().__init__(
			    vocab_file=_SCREAMING_SNAKE_CASE		, tokenizer_file=_SCREAMING_SNAKE_CASE		, bos_token=_SCREAMING_SNAKE_CASE		, eos_token=_SCREAMING_SNAKE_CASE		, sep_token=_SCREAMING_SNAKE_CASE		, cls_token=_SCREAMING_SNAKE_CASE		, unk_token=_SCREAMING_SNAKE_CASE		, pad_token=_SCREAMING_SNAKE_CASE		, mask_token=_SCREAMING_SNAKE_CASE		, src_lang=_SCREAMING_SNAKE_CASE		, tgt_lang=_SCREAMING_SNAKE_CASE		, additional_special_tokens=_SCREAMING_SNAKE_CASE		, legacy_behaviour=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      int   					= vocab_file
			lowerCAmelCase__       :      str   					= False if not self.vocab_file else True

			lowerCAmelCase__       :      List[str]   					= FAIRSEQ_LANGUAGE_CODES.copy()

			if additional_special_tokens is not None:
					# Only add those special tokens if they are not already there.
					_additional_special_tokens.extend(
					    [t for t in additional_special_tokens if t not in _additional_special_tokens]		)

			self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}		)
			lowerCAmelCase__       :      Tuple   					= {
			    lang_code: self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		) for lang_code in FAIRSEQ_LANGUAGE_CODES
			}

			lowerCAmelCase__       :      Tuple   					= src_lang if src_lang is not None else '''eng_Latn'''
			lowerCAmelCase__       :      Optional[Any]   					= self.convert_tokens_to_ids(self._src_lang		)
			lowerCAmelCase__       :      Union[str, Any]   					= tgt_lang
			self.set_src_lang_special_tokens(self._src_lang		)




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




	@src_lang.setter
	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Union[str, Any]   					= new_src_lang
			self.set_src_lang_special_tokens(self._src_lang		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = 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 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		)->	List[int]:
			lowerCAmelCase__       :      str   					= [self.sep_token_id]
			lowerCAmelCase__       :      Tuple   					= [self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	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'''		)
			lowerCAmelCase__       :      List[Any]   					= src_lang
			lowerCAmelCase__       :      Tuple   					= self(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      int   					= tgt_lang_id
			return inputs




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str = "eng_Latn"		, _SCREAMING_SNAKE_CASE						:		Optional[List[str]] = None		, _SCREAMING_SNAKE_CASE						:		str = "fra_Latn"		, **_SCREAMING_SNAKE_CASE						:		Any		, )->	BatchEncoding:
			lowerCAmelCase__       :      Any   					= src_lang
			lowerCAmelCase__       :      Optional[int]   					= tgt_lang
			return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Tuple:
			return self.set_src_lang_special_tokens(self.src_lang		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	List[Any]:
			return self.set_tgt_lang_special_tokens(self.tgt_lang		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	None:
			lowerCAmelCase__       :      str   					= self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)

			if self.legacy_behaviour:
					lowerCAmelCase__       :      Tuple   					= []
					lowerCAmelCase__       :      Optional[Any]   					= [self.eos_token_id, self.cur_lang_code]
			else:
					lowerCAmelCase__       :      Optional[Any]   					= [self.cur_lang_code]
					lowerCAmelCase__       :      Optional[int]   					= [self.eos_token_id]

			lowerCAmelCase__       :      Tuple   					= self.convert_ids_to_tokens(self.prefix_tokens		)
			lowerCAmelCase__       :      Tuple   					= self.convert_ids_to_tokens(self.suffix_tokens		)

			lowerCAmelCase__       :      List[Any]   					= 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 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Any   					= self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
			if self.legacy_behaviour:
					lowerCAmelCase__       :      List[str]   					= []
					lowerCAmelCase__       :      List[Any]   					= [self.eos_token_id, self.cur_lang_code]
			else:
					lowerCAmelCase__       :      List[str]   					= [self.cur_lang_code]
					lowerCAmelCase__       :      int   					= [self.eos_token_id]

			lowerCAmelCase__       :      List[str]   					= self.convert_ids_to_tokens(self.prefix_tokens		)
			lowerCAmelCase__       :      Union[str, Any]   					= self.convert_ids_to_tokens(self.suffix_tokens		)

			lowerCAmelCase__       :      Optional[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 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = 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(_SCREAMING_SNAKE_CASE		):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory.'		)
					return
			lowerCAmelCase__       :      Dict   					= os.path.join(
			    _SCREAMING_SNAKE_CASE		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

			return (out_vocab_file,)








import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
		raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
		raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		'''The Nymphenburg Palace is a beautiful palace in Munich!'''






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {
		    '''attention_cell''': '''multi_head''',
		    '''num_layers''': 4,
		    '''units''': 1_024,
		    '''hidden_size''': 768,
		    '''max_length''': 512,
		    '''num_heads''': 8,
		    '''scaled''': True,
		    '''dropout''': 0.1,
		    '''use_residual''': True,
		    '''embed_size''': 1_024,
		    '''embed_dropout''': 0.1,
		    '''word_embed''': None,
		    '''layer_norm_eps''': 1e-5,
		    '''token_type_vocab_size''': 2,
		}

		lowerCAmelCase__       :      int   					= bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowerCAmelCase__       :      List[Any]   					= BERTEncoder(
		    attention_cell=predefined_args['''attention_cell''']     ,       num_layers=predefined_args['''num_layers''']     ,       units=predefined_args['''units''']     ,       hidden_size=predefined_args['''hidden_size''']     ,       max_length=predefined_args['''max_length''']     ,       num_heads=predefined_args['''num_heads''']     ,       scaled=predefined_args['''scaled''']     ,       dropout=predefined_args['''dropout''']     ,       output_attention=_a     ,       output_all_encodings=_a     ,       use_residual=predefined_args['''use_residual''']     ,       activation=predefined_args.get('''activation'''     ,       '''gelu'''	)     ,       layer_norm_eps=predefined_args.get('''layer_norm_eps'''     ,       _a	)     ,       )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase__       :      Union[str, Any]   					= '''openwebtext_ccnews_stories_books_cased'''

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase__       :      Optional[Any]   					= os.path.join(get_home_dir()     ,       '''models'''	)
		lowerCAmelCase__       :      Optional[int]   					= _load_vocab(_a     ,       _a     ,       _a     ,       cls=_a	)

		lowerCAmelCase__       :      Any   					= nlp.model.BERTModel(
		    _a     ,       len(_a	)     ,       units=predefined_args['''units''']     ,       embed_size=predefined_args['''embed_size''']     ,       embed_dropout=predefined_args['''embed_dropout''']     ,       word_embed=predefined_args['''word_embed''']     ,       use_pooler=_a     ,       use_token_type_embed=_a     ,       token_type_vocab_size=predefined_args['''token_type_vocab_size''']     ,       use_classifier=_a     ,       use_decoder=_a     ,       )

		original_bort.load_parameters(_a     ,       cast_dtype=_a     ,       ignore_extra=_a	)
		lowerCAmelCase__       :      Tuple   					= original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase__       :      int   					= {
		    '''architectures''': ['''BertForMaskedLM'''],
		    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_act''': '''gelu''',
		    '''hidden_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_size''': predefined_args['''embed_size'''],
		    '''initializer_range''': 0.02,
		    '''intermediate_size''': predefined_args['''hidden_size'''],
		    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
		    '''max_position_embeddings''': predefined_args['''max_length'''],
		    '''model_type''': '''bort''',
		    '''num_attention_heads''': predefined_args['''num_heads'''],
		    '''num_hidden_layers''': predefined_args['''num_layers'''],
		    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''vocab_size''': len(_a	),
		}

		lowerCAmelCase__       :      str   					= BertConfig.from_dict(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= BertForMaskedLM(_a	)
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(_a	) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

		# Check param shapes and map new HF param back
		def check_and_map_params(_a     ,       _a	):
				lowerCAmelCase__       :      Dict   					= hf_param.shape

				lowerCAmelCase__       :      List[str]   					= to_torch(params[gluon_param]	)
				lowerCAmelCase__       :      Any   					= gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

				return gluon_param

		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight     ,       '''word_embed.0.weight'''	)
		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight     ,       '''encoder.position_weight'''	)
		lowerCAmelCase__       :      Any   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias     ,       '''encoder.layer_norm.beta'''	)
		lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight     ,       '''encoder.layer_norm.gamma'''	)

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowerCAmelCase__       :      Union[str, Any]   					= torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

		for i in range(hf_bort_config.num_hidden_layers	):
				lowerCAmelCase__       :      BertLayer   					= hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowerCAmelCase__       :      BertSelfAttention   					= layer.attention.self

				lowerCAmelCase__       :      Optional[Any]   					= check_and_map_params(
				    self_attn.key.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'	)

				lowerCAmelCase__       :      str   					= check_and_map_params(
				    self_attn.key.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    self_attn.query.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'	)
				lowerCAmelCase__       :      Optional[Any]   					= check_and_map_params(
				    self_attn.query.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'	)
				lowerCAmelCase__       :      Any   					= check_and_map_params(
				    self_attn.value.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'	)
				lowerCAmelCase__       :      Any   					= check_and_map_params(
				    self_attn.value.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'	)

				# self attention output
				lowerCAmelCase__       :      BertSelfOutput   					= layer.attention.output

				lowerCAmelCase__       :      Dict   					= check_and_map_params(
				    self_output.dense.bias     ,       f'encoder.transformer_cells.{i}.proj.bias'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    self_output.dense.weight     ,       f'encoder.transformer_cells.{i}.proj.weight'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    self_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= check_and_map_params(
				    self_output.LayerNorm.weight     ,       f'encoder.transformer_cells.{i}.layer_norm.gamma'	)

				# intermediate
				lowerCAmelCase__       :      BertIntermediate   					= layer.intermediate

				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'	)
				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'	)

				# output
				lowerCAmelCase__       :      BertOutput   					= layer.output

				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    bert_output.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= check_and_map_params(
				    bert_output.LayerNorm.weight     ,       f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'	)

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowerCAmelCase__       :      Dict   					= RobertaTokenizer.from_pretrained('''roberta-base'''	)

		lowerCAmelCase__       :      List[str]   					= tokenizer.encode_plus(_a	)['''input_ids''']

		# Get gluon output
		lowerCAmelCase__       :      str   					= mx.nd.array([input_ids]	)
		lowerCAmelCase__       :      List[str]   					= original_bort(inputs=_a     ,       token_types=[]	)

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(_a	)
		lowerCAmelCase__       :      Optional[int]   					= BertModel.from_pretrained(_a	)
		hf_bort_model.eval()

		lowerCAmelCase__       :      Tuple   					= tokenizer.encode_plus(_a     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      Optional[Any]   					= hf_bort_model(**_a	)[0]

		lowerCAmelCase__       :      str   					= output_gluon[0].asnumpy()
		lowerCAmelCase__       :      Optional[Any]   					= output_hf[0].detach().numpy()

		lowerCAmelCase__       :      str   					= np.max(np.abs(hf_layer - gluon_layer	)	).item()
		lowerCAmelCase__       :      int   					= np.allclose(_a     ,       _a     ,       atol=1e-3	)

		if success:
				print('''✔️ Both model do output the same tensors'''	)
		else:
				print('''❌ Both model do **NOT** output the same tensors'''	)
				print('''Absolute difference is:'''     ,       _a	)


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCamelCase						=		parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




