






"""simple docstring"""

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
			import faiss

@require_faiss
class 						A_							(      UpperCamelCase__  ):

				'''simple docstring'''





				def     _snake_case					(							self							:						int	)				->					Any:
										__magic_name__            =     tempfile.mkdtemp()
										__magic_name__            =     8

										# DPR tok
										__magic_name__            =     [
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "want",
										    "##want",
										    "##ed",
										    "wa",
										    "un",
										    "runn",
										    "##ing",
										    ",",
										    "low",
										    "lowest",
										]
										__magic_name__            =     os.path.join(self.tmpdirname						,    "dpr_tokenizer"	)
										os.makedirs(__A						,    exist_ok=__A	)
										__magic_name__            =     os.path.join(__A						,    DPR_VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file						,    "w"						,    encoding="utf-8"	) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

										# BART tok
										__magic_name__            =     [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										__magic_name__            =     dict(zip(__A						,    range(len(__A	)	)	)	)
										__magic_name__            =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										__magic_name__            =     {"unk_token": "<unk>"}

										__magic_name__            =     os.path.join(self.tmpdirname						,    "bart_tokenizer"	)
										os.makedirs(__A						,    exist_ok=__A	)
										__magic_name__            =     os.path.join(__A						,    BART_VOCAB_FILES_NAMES["vocab_file"]	)
										__magic_name__            =     os.path.join(__A						,    BART_VOCAB_FILES_NAMES["merges_file"]	)
										with open(self.vocab_file						,    "w"						,    encoding="utf-8"	) as fp:
																fp.write(json.dumps(__A	) + "\n"	)
										with open(self.merges_file						,    "w"						,    encoding="utf-8"	) as fp:
																fp.write("\n".join(__A	)	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					DPRQuestionEncoderTokenizer:
										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,    "dpr_tokenizer"	)	)







				def     _snake_case					(							self							:						List[Any]	)				->					DPRContextEncoderTokenizer:
										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,    "dpr_tokenizer"	)	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					BartTokenizer:
										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,    "bart_tokenizer"	)	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										shutil.rmtree(self.tmpdirname	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     Dataset.from_dict(
										    {
										        "id": ["0", "1"],
										        "text": ["foo", "bar"],
										        "title": ["Foo", "Bar"],
										        "embeddings": [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
										    }	)
										dataset.add_faiss_index("embeddings"						,    string_factory="Flat"						,    metric_type=faiss.METRIC_INNER_PRODUCT	)
										return dataset







				def     _snake_case					(							self							:						str	)				->					str:
										__magic_name__            =     self.get_dummy_dataset()
										__magic_name__            =     RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size						,    question_encoder=DPRConfig().to_dict()						,    generator=BartConfig().to_dict()						,    )
										with patch("transformers.models.rag.retrieval_rag.load_dataset"	) as mock_load_dataset:
																__magic_name__            =     dataset
																__magic_name__            =     RagRetriever(
																    __A						,    question_encoder_tokenizer=self.get_dpr_tokenizer()						,    generator_tokenizer=self.get_bart_tokenizer()						,    )
										return retriever







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						bool	)				->					Union[str, Any]:
										__magic_name__            =     self.get_dummy_dataset()
										__magic_name__            =     RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size						,    question_encoder=DPRConfig().to_dict()						,    generator=BartConfig().to_dict()						,    index_name="custom"						,    )
										if from_disk:
																__magic_name__            =     os.path.join(self.tmpdirname						,    "dataset"	)
																__magic_name__            =     os.path.join(self.tmpdirname						,    "index.faiss"	)
																dataset.get_index("embeddings"	).save(os.path.join(self.tmpdirname						,    "index.faiss"	)	)
																dataset.drop_index("embeddings"	)
																dataset.save_to_disk(os.path.join(self.tmpdirname						,    "dataset"	)	)
																del dataset
																__magic_name__            =     RagRetriever(
																    __A						,    question_encoder_tokenizer=self.get_dpr_tokenizer()						,    generator_tokenizer=self.get_bart_tokenizer()						,    )
										else:
																__magic_name__            =     RagRetriever(
																    __A						,    question_encoder_tokenizer=self.get_dpr_tokenizer()						,    generator_tokenizer=self.get_bart_tokenizer()						,    index=CustomHFIndex(config.retrieval_vector_size						,    __A	)						,    )
										return retriever







				def     _snake_case					(							self							:						Tuple	)				->					Union[str, Any]:
										__magic_name__            =     Dataset.from_dict(
										    {
										        "id": ["0", "1"],
										        "text": ["foo", "bar"],
										        "title": ["Foo", "Bar"],
										        "embeddings": [np.ones(self.retrieval_vector_size + 1	), 2 * np.ones(self.retrieval_vector_size + 1	)],
										    }	)
										dataset.add_faiss_index("embeddings"						,    string_factory="Flat"						,    metric_type=faiss.METRIC_INNER_PRODUCT	)

										__magic_name__            =     os.path.join(self.tmpdirname						,    "hf_bert_base.hnswSQ8_correct_phi_128.c_index"	)
										dataset.save_faiss_index("embeddings"						,    index_file_name + ".index.dpr"	)
										pickle.dump(dataset["id"]						,    open(index_file_name + ".index_meta.dpr"						,    "wb"	)	)

										__magic_name__            =     os.path.join(self.tmpdirname						,    "psgs_w100.tsv.pkl"	)
										__magic_name__            =     {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
										pickle.dump(__A						,    open(__A						,    "wb"	)	)

										__magic_name__            =     RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size						,    question_encoder=DPRConfig().to_dict()						,    generator=BartConfig().to_dict()						,    index_name="legacy"						,    index_path=self.tmpdirname						,    )
										__magic_name__            =     RagRetriever(
										    __A						,    question_encoder_tokenizer=self.get_dpr_tokenizer()						,    generator_tokenizer=self.get_bart_tokenizer()	)
										return retriever







				def     _snake_case					(							self							:						str	)				->					Union[str, Any]:
										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_canonical_hf_index_retriever()
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     retriever.retrieve(__A						,    n_docs=__A	)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertEqual(len(__A	)						,    2	)
										self.assertEqual(sorted(doc_dicts[0]	)						,    ["embeddings", "id", "text", "title"]	)
										self.assertEqual(len(doc_dicts[0]["id"]	)						,    __A	)
										self.assertEqual(doc_dicts[0]["id"][0]						,    "1"	)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]						,    "0"	)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()						,    [[1], [0]]	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Any:
										__magic_name__            =     self.get_dummy_canonical_hf_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
																with patch("transformers.models.rag.retrieval_rag.load_dataset"	) as mock_load_dataset:
																						__magic_name__            =     self.get_dummy_dataset()
																						retriever.save_pretrained(__A	)
																						__magic_name__            =     RagRetriever.from_pretrained(__A	)
																						self.assertIsInstance(__A						,    __A	)
																						__magic_name__            =     np.array(
																						    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
																						__magic_name__            =     retriever.retrieve(__A						,    n_docs=1	)
																						self.assertTrue(out is not None	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[Any]:
										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     retriever.retrieve(__A						,    n_docs=__A	)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertEqual(len(__A	)						,    2	)
										self.assertEqual(sorted(doc_dicts[0]	)						,    ["embeddings", "id", "text", "title"]	)
										self.assertEqual(len(doc_dicts[0]["id"]	)						,    __A	)
										self.assertEqual(doc_dicts[0]["id"][0]						,    "1"	)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]						,    "0"	)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()						,    [[1], [0]]	)







				def     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(__A	)
																__magic_name__            =     RagRetriever.from_pretrained(__A	)
																self.assertIsInstance(__A						,    __A	)
																__magic_name__            =     np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
																__magic_name__            =     retriever.retrieve(__A						,    n_docs=1	)
																self.assertTrue(out is not None	)







				def     _snake_case					(							self							:						Any	)				->					Optional[int]:
										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     retriever.retrieve(__A						,    n_docs=__A	)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertEqual(len(__A	)						,    2	)
										self.assertEqual(sorted(doc_dicts[0]	)						,    ["embeddings", "id", "text", "title"]	)
										self.assertEqual(len(doc_dicts[0]["id"]	)						,    __A	)
										self.assertEqual(doc_dicts[0]["id"][0]						,    "1"	)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["id"][0]						,    "0"	)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()						,    [[1], [0]]	)







				def     _snake_case					(							self							:						Any	)				->					List[str]:
										__magic_name__            =     self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(__A	)
																__magic_name__            =     RagRetriever.from_pretrained(__A	)
																self.assertIsInstance(__A						,    __A	)
																__magic_name__            =     np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
																__magic_name__            =     retriever.retrieve(__A						,    n_docs=1	)
																self.assertTrue(out is not None	)







				def     _snake_case					(							self							:						Dict	)				->					int:
										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_legacy_index_retriever()
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     retriever.retrieve(__A						,    n_docs=__A	)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertEqual(len(__A	)						,    2	)
										self.assertEqual(sorted(doc_dicts[0]	)						,    ["text", "title"]	)
										self.assertEqual(len(doc_dicts[0]["text"]	)						,    __A	)
										self.assertEqual(doc_dicts[0]["text"][0]						,    "bar"	)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]["text"][0]						,    "foo"	)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()						,    [[1], [0]]	)







				def     _snake_case					(							self							:						int	)				->					Optional[Any]:
										__magic_name__            =     self.get_dummy_legacy_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(__A	)
																__magic_name__            =     RagRetriever.from_pretrained(__A	)
																self.assertIsInstance(__A						,    __A	)
																__magic_name__            =     np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
																__magic_name__            =     retriever.retrieve(__A						,    n_docs=1	)
																self.assertTrue(out is not None	)







				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def     _snake_case					(							self							:						Dict	)				->					Tuple:
										import torch

										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_canonical_hf_index_retriever()
										__magic_name__            =     [[5, 7], [1_0, 1_1]]
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__            =     retriever(__A						,    __A						,    prefix=retriever.config.generator.prefix						,    n_docs=__A	)
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     (
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertIsInstance(__A						,    __A	)
										self.assertIsInstance(__A						,    __A	)
										self.assertIsInstance(__A						,    np.ndarray	)

										__magic_name__            =     retriever(
										    __A						,    __A						,    prefix=retriever.config.generator.prefix						,    n_docs=__A						,    return_tensors="pt"						,    )
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     (  # noqa: F841
										    out["context_input_ids"],
										    out["context_attention_mask"],
										    out["retrieved_doc_embeds"],
										    out["doc_ids"],
										)
										self.assertEqual(retrieved_doc_embeds.shape						,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertIsInstance(__A						,    torch.Tensor	)
										self.assertIsInstance(__A						,    torch.Tensor	)
										self.assertIsInstance(__A						,    torch.Tensor	)







				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def     _snake_case					(							self							:						List[str]	)				->					int:
										__magic_name__            =     self.get_dpr_ctx_encoder_tokenizer()
										__magic_name__            =     1
										__magic_name__            =     self.get_dummy_custom_hf_index_retriever(from_disk=__A	)
										retriever.set_ctx_encoder_tokenizer(__A	)

										__magic_name__            =     [[5, 7], [1_0, 1_1]]
										__magic_name__            =     np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]						,    dtype=np.floataa	)
										__magic_name__            =     retriever(__A						,    __A						,    prefix=retriever.config.generator.prefix						,    n_docs=__A	)

										self.assertEqual(
										    len(__A	)						,    6	)  # check whether the retriever output consist of 6 attributes including tokenized docs
										self.assertEqual(
										    all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")	)						,    __A	)  # check for doc token related keys in dictionary.











"""simple docstring"""

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()
lowercase    			=       logging.get_logger(__name__)

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






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     {
						    "attention_cell": "multi_head",
						    "num_layers": 4,
						    "units": 1_0_2_4,
						    "hidden_size": 7_6_8,
						    "max_length": 5_1_2,
						    "num_heads": 8,
						    "scaled": True,
						    "dropout": 0.1,
						    "use_residual": True,
						    "embed_size": 1_0_2_4,
						    "embed_dropout": 0.1,
						    "word_embed": None,
						    "layer_norm_eps": 1E-5,
						    "token_type_vocab_size": 2,
						}

						__magic_name__            =     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
						__magic_name__            =     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=__lowerCamelCase ,						output_all_encodings=__lowerCamelCase ,						use_residual=predefined_args["use_residual"] ,						activation=predefined_args.get("activation" ,						"gelu"   ) ,						layer_norm_eps=predefined_args.get("layer_norm_eps" ,						__lowerCamelCase   ) ,						)

						# Vocab information needs to be fetched first
						# It's the same as RoBERTa, so RobertaTokenizer can be used later
						__magic_name__            =     "openwebtext_ccnews_stories_books_cased"

						# Specify download folder to Gluonnlp's vocab
						__magic_name__            =     os.path.join(get_home_dir() ,						"models"   )
						__magic_name__            =     _load_vocab(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						cls=__lowerCamelCase   )

						__magic_name__            =     nlp.model.BERTModel(
						    __lowerCamelCase ,						len(__lowerCamelCase   ) ,						units=predefined_args["units"] ,						embed_size=predefined_args["embed_size"] ,						embed_dropout=predefined_args["embed_dropout"] ,						word_embed=predefined_args["word_embed"] ,						use_pooler=__lowerCamelCase ,						use_token_type_embed=__lowerCamelCase ,						token_type_vocab_size=predefined_args["token_type_vocab_size"] ,						use_classifier=__lowerCamelCase ,						use_decoder=__lowerCamelCase ,						)

						original_bort.load_parameters(__lowerCamelCase ,						cast_dtype=__lowerCamelCase ,						ignore_extra=__lowerCamelCase   )
						__magic_name__            =     original_bort._collect_params_with_prefix()

						# Build our config 🤗
						__magic_name__            =     {
						    "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": 0.0_2,
						    "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(__lowerCamelCase   ),
						}

						__magic_name__            =     BertConfig.from_dict(__lowerCamelCase   )
						__magic_name__            =     BertForMaskedLM(__lowerCamelCase   )
						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(__lowerCamelCase:Tuple   ) -> 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(__lowerCamelCase:str ,						__lowerCamelCase:Union[str, Any]   ):
												__magic_name__            =     hf_param.shape

												__magic_name__            =     to_torch(params[gluon_param]   )
												__magic_name__            =     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

						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight ,						"word_embed.0.weight"   )
						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight ,						"encoder.position_weight"   )
						__magic_name__            =     check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias ,						"encoder.layer_norm.beta"   )
						__magic_name__            =     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)
						__magic_name__            =     torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

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

												# self attention
												__magic_name__            =     layer.attention.self

												__magic_name__            =     check_and_map_params(
												    self_attn.key.bias.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''   )

												__magic_name__            =     check_and_map_params(
												    self_attn.key.weight.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''   )
												__magic_name__            =     check_and_map_params(
												    self_attn.query.bias.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''   )
												__magic_name__            =     check_and_map_params(
												    self_attn.query.weight.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''   )
												__magic_name__            =     check_and_map_params(
												    self_attn.value.bias.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''   )
												__magic_name__            =     check_and_map_params(
												    self_attn.value.weight.data ,						f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''   )

												# self attention output
												__magic_name__            =     layer.attention.output

												__magic_name__            =     check_and_map_params(
												    self_output.dense.bias ,						f'''encoder.transformer_cells.{i}.proj.bias'''   )
												__magic_name__            =     check_and_map_params(
												    self_output.dense.weight ,						f'''encoder.transformer_cells.{i}.proj.weight'''   )
												__magic_name__            =     check_and_map_params(
												    self_output.LayerNorm.bias ,						f'''encoder.transformer_cells.{i}.layer_norm.beta'''   )
												__magic_name__            =     check_and_map_params(
												    self_output.LayerNorm.weight ,						f'''encoder.transformer_cells.{i}.layer_norm.gamma'''   )

												# intermediate
												__magic_name__            =     layer.intermediate

												__magic_name__            =     check_and_map_params(
												    intermediate.dense.bias ,						f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''   )
												__magic_name__            =     check_and_map_params(
												    intermediate.dense.weight ,						f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''   )

												# output
												__magic_name__            =     layer.output

												__magic_name__            =     check_and_map_params(
												    bert_output.dense.bias ,						f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''   )
												__magic_name__            =     check_and_map_params(
												    bert_output.dense.weight ,						f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''   )
												__magic_name__            =     check_and_map_params(
												    bert_output.LayerNorm.bias ,						f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''   )
												__magic_name__            =     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
						__magic_name__            =     RobertaTokenizer.from_pretrained("roberta-base"   )

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase   )["input_ids"]

						# Get gluon output
						__magic_name__            =     mx.nd.array([input_ids]   )
						__magic_name__            =     original_bort(inputs=__lowerCamelCase ,						token_types=[]   )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(__lowerCamelCase   )
						__magic_name__            =     BertModel.from_pretrained(__lowerCamelCase   )
						hf_bort_model.eval()

						__magic_name__            =     tokenizer.encode_plus(__lowerCamelCase ,						return_tensors="pt"   )
						__magic_name__            =     hf_bort_model(**__lowerCamelCase   )[0]

						__magic_name__            =     output_gluon[0].asnumpy()
						__magic_name__            =     output_hf[0].detach().numpy()

						__magic_name__            =     np.max(np.abs(hf_layer - gluon_layer   )   ).item()
						__magic_name__            =     np.allclose(__lowerCamelCase ,						__lowerCamelCase ,						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:" ,						__lowerCamelCase   )


if __name__ == "__main__":
			lowercase    			=       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.'''
			)
			lowercase    			=       parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



