


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()
snake_case__			 =						logging.get_logger(__name__)

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


def 				lowerCamelCase__	(      a	:   str	,  a	:   str				)    ->	str:





			"""simple docstring"""
			a__ :Optional[Any]						  =    {
			    "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,
			}

			a__ :Dict						  =    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
			a__ :str						  =    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
			a__ :Tuple						  =    "openwebtext_ccnews_stories_books_cased"

			# Specify download folder to Gluonnlp's vocab
			a__ :List[Any]						  =    os.path.join(get_home_dir()	,  "models"				)
			a__ :Optional[Any]						  =    _load_vocab(a	,  a	,  a	,  cls=a				)

			a__ :Optional[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				)
			a__ :str						  =    original_bort._collect_params_with_prefix()

			# Build our config 🤗
			a__ :Tuple						  =    {
			    "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(a				),
			}

			a__ :Dict						  =    BertConfig.from_dict(a				)
			a__ :Union[str, 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	:   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(a	:   Optional[Any]	,  a	:   int				):
						a__ :Any						  =    hf_param.shape

						a__ :Tuple						  =    to_torch(params[gluon_param]				)
						a__ :Dict						  =    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

			a__ :Any						  =    check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight	,  "word_embed.0.weight"				)
			a__ :Any						  =    check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight	,  "encoder.position_weight"				)
			a__ :List[Any]						  =    check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias	,  "encoder.layer_norm.beta"				)
			a__ :str						  =    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)
			a__ :Any						  =    torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

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

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

						a__ :Optional[int]						  =    check_and_map_params(
						    self_attn.key.bias.data	,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''				)

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

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

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

						# intermediate
						a__ :BertIntermediate						  =    layer.intermediate

						a__ :List[str]						  =    check_and_map_params(
						    intermediate.dense.bias	,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''				)
						a__ :Optional[Any]						  =    check_and_map_params(
						    intermediate.dense.weight	,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''				)

						# output
						a__ :BertOutput						  =    layer.output

						a__ :int						  =    check_and_map_params(
						    bert_output.dense.bias	,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''				)
						a__ :Tuple						  =    check_and_map_params(
						    bert_output.dense.weight	,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''				)
						a__ :List[Any]						  =    check_and_map_params(
						    bert_output.LayerNorm.bias	,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''				)
						a__ :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
			a__ :List[Any]						  =    RobertaTokenizer.from_pretrained("roberta-base"				)

			a__ :Union[str, Any]						  =    tokenizer.encode_plus(a				)["input_ids"]

			# Get gluon output
			a__ :Dict						  =    mx.nd.array([input_ids]				)
			a__ :Optional[int]						  =    original_bort(inputs=a	,  token_types=[]				)

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(a				)
			a__ :str						  =    BertModel.from_pretrained(a				)
			hf_bort_model.eval()

			a__ :Optional[int]						  =    tokenizer.encode_plus(a	,  return_tensors="pt"				)
			a__ :Optional[Any]						  =    hf_bort_model(**a				)[0]

			a__ :Dict						  =    output_gluon[0].asnumpy()
			a__ :str						  =    output_hf[0].detach().numpy()

			a__ :Dict						  =    np.max(np.abs(hf_layer - gluon_layer				)				).item()
			a__ :Union[str, Any]						  =    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__":
		snake_case__			 =						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.'''
		)
		snake_case__			 =						parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


