


"""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[Any]					   =	logging.get_logger(__name__)

_lowerCAmelCase							:    Optional[Any]					   =	'''The Nymphenburg Palace is a beautiful palace in Munich!'''







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     {
       "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,
   }

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

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

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

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

   original_bort.load_parameters(_lowerCamelCase			,		cast_dtype=_lowerCamelCase			,		ignore_extra=_lowerCamelCase       )
   _lowerCamelCase   :				int							=     original_bort._collect_params_with_prefix()

   # Build our config 🤗
   _lowerCamelCase   :				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": 0.0_2,
       "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(_lowerCamelCase       ),
   }

   _lowerCamelCase   :				int							=     BertConfig.from_dict(_lowerCamelCase       )
   _lowerCamelCase   :				Optional[Any]							=     BertForMaskedLM(_lowerCamelCase       )
   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(_lowerCamelCase       ) -> 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(_lowerCamelCase			,		_lowerCamelCase       ):
      _lowerCamelCase   :				List[str]							=     hf_param.shape

      _lowerCamelCase   :				Tuple							=     to_torch(params[gluon_param]       )
      _lowerCamelCase   :				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

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

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

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

      _lowerCamelCase   :				Optional[Any]							=     check_and_map_params(
          self_attn.key.bias.data			,		F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""       )

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

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

      _lowerCamelCase   :				str							=     check_and_map_params(
          self_output.dense.bias			,		F"""encoder.transformer_cells.{i}.proj.bias"""       )
      _lowerCamelCase   :				Any							=     check_and_map_params(
          self_output.dense.weight			,		F"""encoder.transformer_cells.{i}.proj.weight"""       )
      _lowerCamelCase   :				Optional[Any]							=     check_and_map_params(
          self_output.LayerNorm.bias			,		F"""encoder.transformer_cells.{i}.layer_norm.beta"""       )
      _lowerCamelCase   :				Optional[Any]							=     check_and_map_params(
          self_output.LayerNorm.weight			,		F"""encoder.transformer_cells.{i}.layer_norm.gamma"""       )

      # intermediate
      _lowerCamelCase   :				BertIntermediate							=     layer.intermediate

      _lowerCamelCase   :				Optional[int]							=     check_and_map_params(
          intermediate.dense.bias			,		F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""       )
      _lowerCamelCase   :				List[str]							=     check_and_map_params(
          intermediate.dense.weight			,		F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""       )

      # output
      _lowerCamelCase   :				BertOutput							=     layer.output

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

   _lowerCamelCase   :				Union[str, Any]							=     tokenizer.encode_plus(_lowerCamelCase       )["input_ids"]

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

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

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

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

   _lowerCamelCase   :				Optional[int]							=     np.max(np.abs(hf_layer - gluon_layer       )       ).item()
   _lowerCamelCase   :				Union[str, Any]							=     np.allclose(_lowerCamelCase			,		_lowerCamelCase			,		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:"			,		_lowerCamelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    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.'''
  )
  _lowerCAmelCase							:    List[str]					   =	parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

from __future__ import annotations

__a         =   8.988E9  # units = N * m^s * C^-2




def 							a		(				snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''
				lowercase_	    =   abs(chargea * chargea   )

				if (force, chargea, chargea, distance).count(0   ) != 1:
								raise ValueError('''One and only one argument must be 0'''   )
				if distance < 0:
								raise ValueError('''Distance cannot be negative'''   )
				if force == 0:
								lowercase_	    =   COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								lowercase_	    =   (COULOMBS_CONSTANT * charge_product / abs(snake_case__   )) ** 0.5
								return {"distance": distance}
				raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




