
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 :		Any							      =  logging.get_logger(__name__)

snake_case :		Optional[int]							      =  "The Nymphenburg Palace is a beautiful palace in Munich!"






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  str   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		int	   =     {
	    "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,
	}

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

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

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

	__magic_name__		:		Any	   =     nlp.model.BERTModel(
	    _snake_case      ,   len(_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=_snake_case      ,   use_token_type_embed=_snake_case      ,   token_type_vocab_size=predefined_args["token_type_vocab_size"]      ,   use_classifier=_snake_case      ,   use_decoder=_snake_case      ,   )

	original_bort.load_parameters(_snake_case      ,   cast_dtype=_snake_case      ,   ignore_extra=_snake_case   )
	__magic_name__		:		str	   =     original_bort._collect_params_with_prefix()

	# Build our config 🤗
	__magic_name__		:		List[str]	   =     {
	    "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(_snake_case   ),
	}

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

		__magic_name__		:		str	   =     to_torch(params[gluon_param]   )
		__magic_name__		:		str	   =     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__		:		List[Any]	   =     check_and_map_params(
	    hf_bort_model.bert.embeddings.word_embeddings.weight      ,   "word_embed.0.weight"   )
	__magic_name__		:		Dict	   =     check_and_map_params(
	    hf_bort_model.bert.embeddings.position_embeddings.weight      ,   "encoder.position_weight"   )
	__magic_name__		:		List[str]	   =     check_and_map_params(
	    hf_bort_model.bert.embeddings.LayerNorm.bias      ,   "encoder.layer_norm.beta"   )
	__magic_name__		:		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)
	__magic_name__		:		Any	   =     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__		:		BertLayer	   =     hf_bort_model.bert.encoder.layer[i]

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

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

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

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

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

		# intermediate
		__magic_name__		:		BertIntermediate	   =     layer.intermediate

		__magic_name__		:		Dict	   =     check_and_map_params(
		    intermediate.dense.bias      ,   F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''   )
		__magic_name__		:		Tuple	   =     check_and_map_params(
		    intermediate.dense.weight      ,   F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''   )

		# output
		__magic_name__		:		BertOutput	   =     layer.output

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

	__magic_name__		:		Tuple	   =     tokenizer.encode_plus(_snake_case   )["input_ids"]

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

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

	__magic_name__		:		Optional[Any]	   =     tokenizer.encode_plus(_snake_case      ,   return_tensors="pt"   )
	__magic_name__		:		str	   =     hf_bort_model(**_snake_case   )[0]

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

	__magic_name__		:		Tuple	   =     np.max(np.abs(hf_layer - gluon_layer   )   ).item()
	__magic_name__		:		Optional[int]	   =     np.allclose(_snake_case      ,   _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:"      ,   _snake_case   )


if __name__ == "__main__":
				snake_case :		str							      =  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 :		int							      =  parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
