



"""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()
_lowerCAmelCase    : List[str]							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[str]							=							"The Nymphenburg Palace is a beautiful palace in Munich!"

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       List[str]:





					'''simple docstring'''

					_UpperCAmelCase      :     Optional[int]        =   {
					    "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,
					}

					_UpperCAmelCase      :     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
					_UpperCAmelCase      :     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=_SCREAMING_SNAKE_CASE						,  output_all_encodings=_SCREAMING_SNAKE_CASE						,  use_residual=predefined_args["use_residual"]						,  activation=predefined_args.get("activation"						,  "gelu"    )						,  layer_norm_eps=predefined_args.get("layer_norm_eps"						,  _SCREAMING_SNAKE_CASE    )						,  )

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

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

					_UpperCAmelCase      :     Dict        =   nlp.model.BERTModel(
					    _SCREAMING_SNAKE_CASE						,  len(_SCREAMING_SNAKE_CASE    )						,  units=predefined_args["units"]						,  embed_size=predefined_args["embed_size"]						,  embed_dropout=predefined_args["embed_dropout"]						,  word_embed=predefined_args["word_embed"]						,  use_pooler=_SCREAMING_SNAKE_CASE						,  use_token_type_embed=_SCREAMING_SNAKE_CASE						,  token_type_vocab_size=predefined_args["token_type_vocab_size"]						,  use_classifier=_SCREAMING_SNAKE_CASE						,  use_decoder=_SCREAMING_SNAKE_CASE						,  )

					original_bort.load_parameters(_SCREAMING_SNAKE_CASE						,  cast_dtype=_SCREAMING_SNAKE_CASE						,  ignore_extra=_SCREAMING_SNAKE_CASE    )
					_UpperCAmelCase      :     Optional[Any]        =   original_bort._collect_params_with_prefix()

					# Build our config 🤗
					_UpperCAmelCase      :     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(_SCREAMING_SNAKE_CASE    ),
					}

					_UpperCAmelCase      :     List[Any]        =   BertConfig.from_dict(_SCREAMING_SNAKE_CASE    )
					_UpperCAmelCase      :     Dict        =   BertForMaskedLM(_SCREAMING_SNAKE_CASE    )
					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(SCREAMING_SNAKE_CASE__     :						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(SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]    ):
										_UpperCAmelCase      :     Optional[Any]        =   hf_param.shape

										_UpperCAmelCase      :     Dict        =   to_torch(params[gluon_param]    )
										_UpperCAmelCase      :     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

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

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

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

										_UpperCAmelCase      :     List[str]        =   check_and_map_params(
										    self_attn.key.bias.data						,  f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'    )

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

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

										_UpperCAmelCase      :     List[Any]        =   check_and_map_params(
										    self_output.dense.bias						,  f'encoder.transformer_cells.{i}.proj.bias'    )
										_UpperCAmelCase      :     Any        =   check_and_map_params(
										    self_output.dense.weight						,  f'encoder.transformer_cells.{i}.proj.weight'    )
										_UpperCAmelCase      :     int        =   check_and_map_params(
										    self_output.LayerNorm.bias						,  f'encoder.transformer_cells.{i}.layer_norm.beta'    )
										_UpperCAmelCase      :     Dict        =   check_and_map_params(
										    self_output.LayerNorm.weight						,  f'encoder.transformer_cells.{i}.layer_norm.gamma'    )

										# intermediate
										_UpperCAmelCase      :     BertIntermediate        =   layer.intermediate

										_UpperCAmelCase      :     Dict        =   check_and_map_params(
										    intermediate.dense.bias						,  f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'    )
										_UpperCAmelCase      :     int        =   check_and_map_params(
										    intermediate.dense.weight						,  f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'    )

										# output
										_UpperCAmelCase      :     BertOutput        =   layer.output

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

					_UpperCAmelCase      :     List[Any]        =   tokenizer.encode_plus(_SCREAMING_SNAKE_CASE    )["input_ids"]

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

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

					_UpperCAmelCase      :     Union[str, Any]        =   tokenizer.encode_plus(_SCREAMING_SNAKE_CASE						,  return_tensors="pt"    )
					_UpperCAmelCase      :     int        =   hf_bort_model(**_SCREAMING_SNAKE_CASE    )[0]

					_UpperCAmelCase      :     List[Any]        =   output_gluon[0].asnumpy()
					_UpperCAmelCase      :     List[Any]        =   output_hf[0].detach().numpy()

					_UpperCAmelCase      :     List[str]        =   np.max(np.abs(hf_layer - gluon_layer    )    ).item()
					_UpperCAmelCase      :     Any        =   np.allclose(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  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:"						,  _SCREAMING_SNAKE_CASE    )


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





"""simple docstring"""




from __future__ import annotations

from typing import Any

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]    )  ->       None:





     '''simple docstring'''

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  []						,  0    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       None:





     '''simple docstring'''

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
 _lowerCAmelCase    : list[Any]							=							[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["A", "B", "C"])
 generate_all_subsequences(seq)
