


"""simple docstring"""




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

import sentencepiece

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		'''▁'''

lowerCamelCase						=		{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase						=		{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

lowerCamelCase						=		{
    '''facebook/m2m100_418M''': 1024,
}

# fmt: off
lowerCamelCase						=		{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}





class  _a							(    _lowercase):
	_a  :  List[str]				   =							VOCAB_FILES_NAMES
	_a  :  List[Any]				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  Union[str, Any]				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  str				   =							['''input_ids''', '''attention_mask''']

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




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		str="</s>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="</s>"		, _SCREAMING_SNAKE_CASE						:		Dict="<pad>"		, _SCREAMING_SNAKE_CASE						:		List[str]="<unk>"		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="m2m100"		, _SCREAMING_SNAKE_CASE						:		Optional[Dict[str, Any]] = None		, _SCREAMING_SNAKE_CASE						:		int=8		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	None:
			lowerCAmelCase__       :      Tuple   					= {} if sp_model_kwargs is None else sp_model_kwargs

			lowerCAmelCase__       :      Optional[int]   					= language_codes
			lowerCAmelCase__       :      int   					= FAIRSEQ_LANGUAGE_CODES[language_codes]
			lowerCAmelCase__       :      Union[str, Any]   					= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

			lowerCAmelCase__       :      Optional[Any]   					= kwargs.get('''additional_special_tokens'''		, []		)
			kwargs["additional_special_tokens"] += [
			    self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			    for lang_code in fairseq_language_code
			    if self.get_lang_token(_SCREAMING_SNAKE_CASE		) not in kwargs["additional_special_tokens"]
			]

			super().__init__(
			    src_lang=_SCREAMING_SNAKE_CASE		, tgt_lang=_SCREAMING_SNAKE_CASE		, bos_token=_SCREAMING_SNAKE_CASE		, eos_token=_SCREAMING_SNAKE_CASE		, sep_token=_SCREAMING_SNAKE_CASE		, unk_token=_SCREAMING_SNAKE_CASE		, pad_token=_SCREAMING_SNAKE_CASE		, language_codes=_SCREAMING_SNAKE_CASE		, sp_model_kwargs=self.sp_model_kwargs		, num_madeup_words=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Optional[Any]   					= vocab_file
			lowerCAmelCase__       :      Optional[int]   					= load_json(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= {v: k for k, v in self.encoder.items()}
			lowerCAmelCase__       :      List[str]   					= spm_file
			lowerCAmelCase__       :      Dict   					= load_spm(_SCREAMING_SNAKE_CASE		, self.sp_model_kwargs		)

			lowerCAmelCase__       :      Union[str, Any]   					= len(self.encoder		)

			lowerCAmelCase__       :      List[Any]   					= {
			    self.get_lang_token(_SCREAMING_SNAKE_CASE		): self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE		)
			}
			lowerCAmelCase__       :      List[str]   					= {lang_code: self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE		)}
			lowerCAmelCase__       :      str   					= {v: k for k, v in self.lang_token_to_id.items()}

			lowerCAmelCase__       :      int   					= src_lang if src_lang is not None else '''en'''
			lowerCAmelCase__       :      List[Any]   					= tgt_lang
			lowerCAmelCase__       :      int   					= self.get_lang_id(self._src_lang		)
			self.set_src_lang_special_tokens(self._src_lang		)

			lowerCAmelCase__       :      Optional[Any]   					= num_madeup_words




	@property
	def 							UpperCAmelCase__( self						:		List[Any]		)->	int:
			return len(self.encoder		) + len(self.lang_token_to_id		)




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




	@src_lang.setter
	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      str   					= new_src_lang
			self.set_src_lang_special_tokens(self._src_lang		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE		, out_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[Any]:
			if token in self.lang_token_to_id:
					return self.lang_token_to_id[token]
			return self.encoder.get(_SCREAMING_SNAKE_CASE		, self.encoder[self.unk_token]		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		int		)->	str:
			if index in self.id_to_lang_token:
					return self.id_to_lang_token[index]
			return self.decoder.get(_SCREAMING_SNAKE_CASE		, self.unk_token		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		str		)->	Union[str, Any]:
			lowerCAmelCase__       :      str   					= []
			lowerCAmelCase__       :      str   					= ''''''
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		) + token
							lowerCAmelCase__       :      List[str]   					= []
					else:
							current_sub_tokens.append(_SCREAMING_SNAKE_CASE		)
			out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE		)
			return out_string.strip()




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		)->	List[int]:

			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_SCREAMING_SNAKE_CASE		, token_ids_a=_SCREAMING_SNAKE_CASE		, already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= [1] * len(self.prefix_tokens		)
			lowerCAmelCase__       :      Tuple   					= [1] * len(self.suffix_tokens		)
			if token_ids_a is None:
					return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones
			return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones




	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						:		List[Any]		)->	Dict:
			lowerCAmelCase__       :      Optional[int]   					= {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
			vocab.update(self.added_tokens_encoder		)
			return vocab




	def __getstate__( self						:		str		)->	Dict:
			lowerCAmelCase__       :      Tuple   					= self.__dict__.copy()
			lowerCAmelCase__       :      Any   					= None
			return state




	def __setstate__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	None:
			lowerCAmelCase__       :      Dict   					= d

			# for backward compatibility
			if not hasattr(self		, '''sp_model_kwargs'''		):
					lowerCAmelCase__       :      Optional[int]   					= {}

			lowerCAmelCase__       :      List[Any]   					= load_spm(self.spm_file		, self.sp_model_kwargs		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		)->	Tuple[str]:
			lowerCAmelCase__       :      int   					= Path(_SCREAMING_SNAKE_CASE		)
			if not save_dir.is_dir():
					raise OSError(F'{save_directory} should be a directory'		)
			lowerCAmelCase__       :      List[str]   					= save_dir / (
			    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
			)
			lowerCAmelCase__       :      Any   					= save_dir / (
			    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
			)

			save_json(self.encoder		, _SCREAMING_SNAKE_CASE		)

			if os.path.abspath(self.spm_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.spm_file		):
					copyfile(self.spm_file		, _SCREAMING_SNAKE_CASE		)
			elif not os.path.isfile(self.spm_file		):
					with open(_SCREAMING_SNAKE_CASE		, '''wb'''		) as fi:
							lowerCAmelCase__       :      List[str]   					= self.sp_model.serialized_model_proto()
							fi.write(_SCREAMING_SNAKE_CASE		)

			return (str(_SCREAMING_SNAKE_CASE		), str(_SCREAMING_SNAKE_CASE		))




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str = "en"		, _SCREAMING_SNAKE_CASE						:		Optional[List[str]] = None		, _SCREAMING_SNAKE_CASE						:		str = "ro"		, **_SCREAMING_SNAKE_CASE						:		str		, )->	BatchEncoding:
			lowerCAmelCase__       :      Dict   					= src_lang
			lowerCAmelCase__       :      Tuple   					= tgt_lang
			self.set_src_lang_special_tokens(self.src_lang		)
			return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, _SCREAMING_SNAKE_CASE						:		Optional[str]		, **_SCREAMING_SNAKE_CASE						:		str		)->	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__       :      Union[str, Any]   					= src_lang
			lowerCAmelCase__       :      Tuple   					= self(_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= self.get_lang_id(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= tgt_lang_id
			return inputs




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




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	List[str]:
			self.set_tgt_lang_special_tokens(self.tgt_lang		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Union[str, Any]   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= self.lang_token_to_id[lang_token]
			lowerCAmelCase__       :      Any   					= [self.cur_lang_id]
			lowerCAmelCase__       :      Optional[int]   					= [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		str		)->	None:
			lowerCAmelCase__       :      Dict   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= self.lang_token_to_id[lang_token]
			lowerCAmelCase__       :      Dict   					= [self.cur_lang_id]
			lowerCAmelCase__       :      Tuple   					= [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.lang_code_to_token[lang]




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		)->	int:
			lowerCAmelCase__       :      List[str]   					= self.get_lang_token(_SCREAMING_SNAKE_CASE		)
			return self.lang_token_to_id[lang_token]






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= sentencepiece.SentencePieceProcessor(**_a	)
		spm.Load(str(_a	)	)
		return spm






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		with open(_a     ,       '''r'''	) as f:
				return json.load(_a	)






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		with open(_a     ,       '''w'''	) as f:
				json.dump(_a     ,       _a     ,       indent=2	)








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)




