





import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   return np.dot(A				,							A     )

class     snake_case_		:






 '''simple docstring'''





 def __init__(  self,		*,
     A_ = np.inf,		A_ = "linear",		A_ = 0.0,		)	->       None:
    UpperCAmelCase__						=regularization
    UpperCAmelCase__						=gamma
    if kernel == "linear":
       UpperCAmelCase__						=self.__linear
    elif kernel == "rbf":
       if self.gamma == 0:
          raise ValueError("rbf kernel requires gamma"		)
       if not isinstance(self.gamma,		(float, int)		):
          raise ValueError("gamma must be float or int"		)
       if not self.gamma > 0:
          raise ValueError("gamma must be > 0"		)
       UpperCAmelCase__						=self.__rbf
       # in the future, there could be a default value like in sklearn
       # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
       # previously it was 1/(n_features)
    else:
       UpperCAmelCase__						=f"""Unknown kernel: {kernel}"""
       raise ValueError(A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       float:
    return np.dot(A_,		A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       float:
    return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       None:
    UpperCAmelCase__						=observations
    UpperCAmelCase__						=classes

    # using Wolfe's Dual to calculate w.
    # Primal problem: minimize 1/2*norm_squared(w)
    #   constraint: yn(w . xn + b) >= 1
    #
    # With l a vector
    # Dual problem: maximize sum_n(ln) -
    #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
    #   constraint: self.C >= ln >= 0
    #           and sum_n(ln*yn) = 0
    # Then we get w using w = sum_n(ln*yn*xn)
    # At the end we can get b ~= mean(yn - w . xn)
    #
    # Since we use kernels, we only need l_star to calculate b
    # and to classify observations

    ((UpperCAmelCase__)			,							)						=np.shape(A_		)

    def to_minimize(A_		) -> float:
       UpperCAmelCase__						=0
       ((UpperCAmelCase__)			,							)						=np.shape(A_		)
       for i in range(A_		):
          for j in range(A_		):
             s += (
                 candidate[i]
                 * candidate[j]
                 * classes[i]
                 * classes[j]
                 * self.kernel(observations[i],		observations[j]		)
             )
       return 1 / 2 * s - sum(A_		)

    UpperCAmelCase__						=LinearConstraint(A_,		0,		0		)
    UpperCAmelCase__						=Bounds(0,		self.regularization		)

    UpperCAmelCase__						=minimize(
        A_,		np.ones(A_		),		bounds=A_,		constraints=[ly_contraint]		).x
    UpperCAmelCase__						=l_star

    # calculating mean offset of separation plane to points
    UpperCAmelCase__						=0
    for i in range(A_		):
       for j in range(A_		):
          s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
              observations[i],		observations[j]		)
    UpperCAmelCase__						=s / n






 def  __UpperCAmelCase						(  self,		A_		)	->       int:
    UpperCAmelCase__						=sum(
        self.optimum[n]
        * self.classes[n]
        * self.kernel(self.observations[n],		A_		)
        for n in range(len(self.classes		)		)		)
    return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()










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()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	'The Nymphenburg Palace is a beautiful palace in Munich!'






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       "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,
   }

   UpperCAmelCase__						=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__						=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=A				,							output_all_encodings=A				,							use_residual=predefined_args["use_residual"]				,							activation=predefined_args.get("activation"				,							"gelu"     )				,							layer_norm_eps=predefined_args.get("layer_norm_eps"				,							A     )				,							)

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

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

   UpperCAmelCase__						=nlp.model.BERTModel(
       A				,							len(A     )				,							units=predefined_args["units"]				,							embed_size=predefined_args["embed_size"]				,							embed_dropout=predefined_args["embed_dropout"]				,							word_embed=predefined_args["word_embed"]				,							use_pooler=A				,							use_token_type_embed=A				,							token_type_vocab_size=predefined_args["token_type_vocab_size"]				,							use_classifier=A				,							use_decoder=A				,							)

   original_bort.load_parameters(A				,							cast_dtype=A				,							ignore_extra=A     )
   UpperCAmelCase__						=original_bort._collect_params_with_prefix()

   # Build our config 🤗
   UpperCAmelCase__						={
       "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(A     ),
   }

   UpperCAmelCase__						=BertConfig.from_dict(A     )
   UpperCAmelCase__						=BertForMaskedLM(A     )
   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(A     ) -> 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(A				,							A     ):
      UpperCAmelCase__						=hf_param.shape

      UpperCAmelCase__						=to_torch(params[gluon_param]     )
      UpperCAmelCase__						=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__						=check_and_map_params(
       hf_bort_model.bert.embeddings.word_embeddings.weight				,							"word_embed.0.weight"     )
   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.position_embeddings.weight				,							"encoder.position_weight"     )
   UpperCAmelCase__						=check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias				,							"encoder.layer_norm.beta"     )
   UpperCAmelCase__						=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__						=torch.zeros_like(
       hf_bort_model.bert.embeddings.token_type_embeddings.weight.data     )

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

      # self attention
      UpperCAmelCase__						=layer.attention.self

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

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

      # self attention output
      UpperCAmelCase__						=layer.attention.output

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

      # intermediate
      UpperCAmelCase__						=layer.intermediate

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

      # output
      UpperCAmelCase__						=layer.output

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

   UpperCAmelCase__						=tokenizer.encode_plus(A     )["input_ids"]

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

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

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

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

   UpperCAmelCase__						=np.max(np.abs(hf_layer - gluon_layer     )     ).item()
   UpperCAmelCase__						=np.allclose(A				,							A				,							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:"				,							A     )


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



