
'''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()
_snake_case				:   List[str]     =				logging.get_logger(__name__)

_snake_case				:   Any     =				"""The Nymphenburg Palace is a beautiful palace in Munich!"""



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= {
						    "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,
						}

						_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= "openwebtext_ccnews_stories_books_cased"

						# Specify download folder to Gluonnlp's vocab
						_SCREAMING_SNAKE_CASE				= os.path.join(get_home_dir()	,			"models"  )
						_SCREAMING_SNAKE_CASE				= _load_vocab(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			cls=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= 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  )
						_SCREAMING_SNAKE_CASE				= original_bort._collect_params_with_prefix()

						# Build our config 🤗
						_SCREAMING_SNAKE_CASE				= {
						    "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  ),
						}

						_SCREAMING_SNAKE_CASE				= BertConfig.from_dict(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 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		:   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(_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Tuple  ):
												_SCREAMING_SNAKE_CASE				= hf_param.shape

												_SCREAMING_SNAKE_CASE				= to_torch(params[gluon_param]  )
												_SCREAMING_SNAKE_CASE				= 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

						_SCREAMING_SNAKE_CASE				= check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight	,			"word_embed.0.weight"  )
						_SCREAMING_SNAKE_CASE				= check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight	,			"encoder.position_weight"  )
						_SCREAMING_SNAKE_CASE				= check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias	,			"encoder.layer_norm.beta"  )
						_SCREAMING_SNAKE_CASE				= 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)
						_SCREAMING_SNAKE_CASE				= torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data  )

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

												# self attention
												_SCREAMING_SNAKE_CASE				= layer.attention.self

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

												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_attn.key.weight.data	,			F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_attn.query.bias.data	,			F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_attn.query.weight.data	,			F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_attn.value.bias.data	,			F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_attn.value.weight.data	,			F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'  )

												# self attention output
												_SCREAMING_SNAKE_CASE				= layer.attention.output

												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_output.dense.bias	,			F'encoder.transformer_cells.{i}.proj.bias'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_output.dense.weight	,			F'encoder.transformer_cells.{i}.proj.weight'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_output.LayerNorm.bias	,			F'encoder.transformer_cells.{i}.layer_norm.beta'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    self_output.LayerNorm.weight	,			F'encoder.transformer_cells.{i}.layer_norm.gamma'  )

												# intermediate
												_SCREAMING_SNAKE_CASE				= layer.intermediate

												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    intermediate.dense.bias	,			F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    intermediate.dense.weight	,			F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'  )

												# output
												_SCREAMING_SNAKE_CASE				= layer.output

												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    bert_output.dense.bias	,			F'encoder.transformer_cells.{i}.ffn.ffn_2.bias'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    bert_output.dense.weight	,			F'encoder.transformer_cells.{i}.ffn.ffn_2.weight'  )
												_SCREAMING_SNAKE_CASE				= check_and_map_params(
												    bert_output.LayerNorm.bias	,			F'encoder.transformer_cells.{i}.ffn.layer_norm.beta'  )
												_SCREAMING_SNAKE_CASE				= 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
						_SCREAMING_SNAKE_CASE				= RobertaTokenizer.from_pretrained("roberta-base"  )

						_SCREAMING_SNAKE_CASE				= tokenizer.encode_plus(_SCREAMING_SNAKE_CASE  )["input_ids"]

						# Get gluon output
						_SCREAMING_SNAKE_CASE				= mx.nd.array([input_ids]  )
						_SCREAMING_SNAKE_CASE				= original_bort(inputs=_SCREAMING_SNAKE_CASE	,			token_types=[]  )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= BertModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
						hf_bort_model.eval()

						_SCREAMING_SNAKE_CASE				= tokenizer.encode_plus(_SCREAMING_SNAKE_CASE	,			return_tensors="pt"  )
						_SCREAMING_SNAKE_CASE				= hf_bort_model(**_SCREAMING_SNAKE_CASE  )[0]

						_SCREAMING_SNAKE_CASE				= output_gluon[0].asnumpy()
						_SCREAMING_SNAKE_CASE				= output_hf[0].detach().numpy()

						_SCREAMING_SNAKE_CASE				= np.max(np.abs(hf_layer - gluon_layer  )  ).item()
						_SCREAMING_SNAKE_CASE				= 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__":
						_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				:   str     =				parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)