



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    :						int  =						logging.get_logger(__name__)

snake_case    :						Optional[int]  =						'The Nymphenburg Palace is a beautiful palace in Munich!'
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Optional[int]:







						"""simple docstring"""

						A__	:							Dict						    = {
						    "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,
						}

						A__	:							Any						    = 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__	:							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=__lowercase							,     output_all_encodings=__lowercase							,     use_residual=predefined_args["use_residual"]							,     activation=predefined_args.get("activation"							,     "gelu"       )							,     layer_norm_eps=predefined_args.get("layer_norm_eps"							,     __lowercase       )							,     )

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

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

						A__	:							str						    = nlp.model.BERTModel(
						    __lowercase							,     len(__lowercase       )							,     units=predefined_args["units"]							,     embed_size=predefined_args["embed_size"]							,     embed_dropout=predefined_args["embed_dropout"]							,     word_embed=predefined_args["word_embed"]							,     use_pooler=__lowercase							,     use_token_type_embed=__lowercase							,     token_type_vocab_size=predefined_args["token_type_vocab_size"]							,     use_classifier=__lowercase							,     use_decoder=__lowercase							,     )

						original_bort.load_parameters(__lowercase							,     cast_dtype=__lowercase							,     ignore_extra=__lowercase       )
						A__	:							Optional[int]						    = original_bort._collect_params_with_prefix()

						# Build our config 🤗
						A__	:							Optional[Any]						    = {
						    "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(__lowercase       ),
						}

						A__	:							Optional[int]						    = BertConfig.from_dict(__lowercase       )
						A__	:							Union[str, Any]						    = BertForMaskedLM(__lowercase       )
						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(__lowercase       ) -> 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(__lowercase							,     __lowercase       ):
												A__	:							str						    = hf_param.shape

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

						A__	:							Dict						    = check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight							,     "word_embed.0.weight"       )
						A__	:							List[Any]						    = check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight							,     "encoder.position_weight"       )
						A__	:							Optional[int]						    = check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias							,     "encoder.layer_norm.beta"       )
						A__	:							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)
						A__	:							List[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__	:							List[Any]						    = check_and_map_params(
												    self_attn.key.bias.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'       )

												A__	:							Optional[int]						    = check_and_map_params(
												    self_attn.key.weight.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'       )
												A__	:							Union[str, 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__	:							int						    = check_and_map_params(
												    self_attn.value.bias.data							,     F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'       )
												A__	:							Dict						    = 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__	:							Optional[int]						    = check_and_map_params(
												    self_output.LayerNorm.bias							,     F'encoder.transformer_cells.{i}.layer_norm.beta'       )
												A__	:							int						    = check_and_map_params(
												    self_output.LayerNorm.weight							,     F'encoder.transformer_cells.{i}.layer_norm.gamma'       )

												# intermediate
												A__	:							BertIntermediate						    = layer.intermediate

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

												# output
												A__	:							BertOutput						    = layer.output

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

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

						# Get gluon output
						A__	:							Union[str, Any]						    = mx.nd.array([input_ids]       )
						A__	:							List[str]						    = original_bort(inputs=__lowercase							,     token_types=[]       )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(__lowercase       )
						A__	:							int						    = BertModel.from_pretrained(__lowercase       )
						hf_bort_model.eval()

						A__	:							List[Any]						    = tokenizer.encode_plus(__lowercase							,     return_tensors="pt"       )
						A__	:							Optional[Any]						    = hf_bort_model(**__lowercase       )[0]

						A__	:							List[str]						    = output_gluon[0].asnumpy()
						A__	:							Union[str, Any]						    = output_hf[0].detach().numpy()

						A__	:							Tuple						    = np.max(np.abs(hf_layer - gluon_layer       )       ).item()
						A__	:							int						    = np.allclose(__lowercase							,     __lowercase							,     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:"							,     __lowercase       )


if __name__ == "__main__":
		snake_case    :						Dict  =						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    :						Optional[int]  =						parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()