






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       :       List[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       Optional[int]        =				'''The Nymphenburg Palace is a beautiful palace in Munich!'''
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							__lowercase					:							Union[str, Any]			=   {
							    """attention_cell""": """multi_head""",
							    """num_layers""": 4,
							    """units""": 1024,
							    """hidden_size""": 768,
							    """max_length""": 512,
							    """num_heads""": 8,
							    """scaled""": True,
							    """dropout""": 0.1,
							    """use_residual""": True,
							    """embed_size""": 1024,
							    """embed_dropout""": 0.1,
							    """word_embed""": None,
							    """layer_norm_eps""": 1e-5,
							    """token_type_vocab_size""": 2,
							}

							__lowercase					:							Optional[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
							__lowercase					:							int			=   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
							__lowercase					:							int			=   """openwebtext_ccnews_stories_books_cased"""

							# Specify download folder to Gluonnlp's vocab
							__lowercase					:							str			=   os.path.join(get_home_dir()				,				"""models"""					)
							__lowercase					:							Optional[int]			=   _load_vocab(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_				,				cls=lowerCAmelCase_					)

							__lowercase					:							Tuple			=   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_					)
							__lowercase					:							Optional[Any]			=   original_bort._collect_params_with_prefix()

							# Build our config 🤗
							__lowercase					:							Dict			=   {
							    """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(lowerCAmelCase_					),
							}

							__lowercase					:							Tuple			=   BertConfig.from_dict(lowerCAmelCase_					)
							__lowercase					:							Optional[int]			=   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_				:  int					) -> 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_				:  Optional[Any]					):
														__lowercase					:							str			=   hf_param.shape

														__lowercase					:							Optional[int]			=   to_torch(params[gluon_param]					)
														__lowercase					:							int			=   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

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

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

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

														__lowercase					:							int			=   check_and_map_params(
														    self_attn.key.bias.data				,				F"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"					)

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

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

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

														# intermediate
														__lowercase					:							BertIntermediate			=   layer.intermediate

														__lowercase					:							Tuple			=   check_and_map_params(
														    intermediate.dense.bias				,				F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"					)
														__lowercase					:							int			=   check_and_map_params(
														    intermediate.dense.weight				,				F"encoder.transformer_cells.{i}.ffn.ffn_1.weight"					)

														# output
														__lowercase					:							BertOutput			=   layer.output

														__lowercase					:							Dict			=   check_and_map_params(
														    bert_output.dense.bias				,				F"encoder.transformer_cells.{i}.ffn.ffn_2.bias"					)
														__lowercase					:							int			=   check_and_map_params(
														    bert_output.dense.weight				,				F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"					)
														__lowercase					:							Optional[int]			=   check_and_map_params(
														    bert_output.LayerNorm.bias				,				F"encoder.transformer_cells.{i}.ffn.layer_norm.beta"					)
														__lowercase					:							Any			=   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
							__lowercase					:							Union[str, Any]			=   RobertaTokenizer.from_pretrained("""roberta-base"""					)

							__lowercase					:							Any			=   tokenizer.encode_plus(lowerCAmelCase_					)["""input_ids"""]

							# Get gluon output
							__lowercase					:							Dict			=   mx.nd.array([input_ids]					)
							__lowercase					:							Optional[Any]			=   original_bort(inputs=lowerCAmelCase_				,				token_types=[]					)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(lowerCAmelCase_					)
							__lowercase					:							Union[str, Any]			=   BertModel.from_pretrained(lowerCAmelCase_					)
							hf_bort_model.eval()

							__lowercase					:							Any			=   tokenizer.encode_plus(lowerCAmelCase_				,				return_tensors="""pt"""					)
							__lowercase					:							str			=   hf_bort_model(**lowerCAmelCase_					)[0]

							__lowercase					:							Optional[Any]			=   output_gluon[0].asnumpy()
							__lowercase					:							Tuple			=   output_hf[0].detach().numpy()

							__lowercase					:							Tuple			=   np.max(np.abs(hf_layer - gluon_layer					)					).item()
							__lowercase					:							Union[str, Any]			=   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__":
	lowerCamelCase       :       Optional[int]        =				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       :       List[Any]        =				parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







from __future__ import annotations
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  int					):
							if partitions <= 0:
														raise ValueError("""partitions must be a positive number!"""					)
							if partitions > number_of_bytes:
														raise ValueError("""partitions can not > number_of_bytes!"""					)
							__lowercase					:							Dict			=   number_of_bytes // partitions
							__lowercase					:							Union[str, Any]			=   []
							for i in range(lowerCAmelCase_					):
														__lowercase					:							str			=   i * bytes_per_partition + 1
														__lowercase					:							List[Any]			=   (
														    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
														)
														allocation_list.append(F"{start_bytes}-{end_bytes}"					)
							return allocation_list


if __name__ == "__main__":
	import doctest

	doctest.testmod()