





"""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__						= logging.get_logger(__name__)

lowerCamelCase__						= "The Nymphenburg Palace is a beautiful palace in Munich!"




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							{
          "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   :						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
      _UpperCamelCase   :						List[str]    =							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=lowercase_		,output_all_encodings=lowercase_		,use_residual=predefined_args["use_residual"]		,activation=predefined_args.get("activation"		,"gelu"      )		,layer_norm_eps=predefined_args.get("layer_norm_eps"		,lowercase_      )		,)

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

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

      _UpperCamelCase   :						List[Any]    =							nlp.model.BERTModel(
          lowercase_		,len(lowercase_      )		,units=predefined_args["units"]		,embed_size=predefined_args["embed_size"]		,embed_dropout=predefined_args["embed_dropout"]		,word_embed=predefined_args["word_embed"]		,use_pooler=lowercase_		,use_token_type_embed=lowercase_		,token_type_vocab_size=predefined_args["token_type_vocab_size"]		,use_classifier=lowercase_		,use_decoder=lowercase_		,)

      original_bort.load_parameters(lowercase_		,cast_dtype=lowercase_		,ignore_extra=lowercase_      )
      _UpperCamelCase   :						Union[str, Any]    =							original_bort._collect_params_with_prefix()

      # Build our config 🤗
      _UpperCamelCase   :						Tuple    =							{
          "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(lowercase_      ),
      }

      _UpperCamelCase   :						Tuple    =							BertConfig.from_dict(lowercase_      )
      _UpperCamelCase   :						str    =							BertForMaskedLM(lowercase_      )
      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(lowercase_      ) -> 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(lowercase_		,lowercase_      ):
            _UpperCamelCase   :						Dict    =							hf_param.shape

            _UpperCamelCase   :						List[str]    =							to_torch(params[gluon_param]      )
            _UpperCamelCase   :						Union[str, 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   :						Dict    =							check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight		,"word_embed.0.weight"      )
      _UpperCamelCase   :						int    =							check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight		,"encoder.position_weight"      )
      _UpperCamelCase   :						Optional[Any]    =							check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias		,"encoder.layer_norm.beta"      )
      _UpperCamelCase   :						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   :						str    =							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   :						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[Any]    =							check_and_map_params(
                self_attn.query.bias.data		,F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''      )
            _UpperCamelCase   :						Any    =							check_and_map_params(
                self_attn.query.weight.data		,F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''      )
            _UpperCamelCase   :						List[Any]    =							check_and_map_params(
                self_attn.value.bias.data		,F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''      )
            _UpperCamelCase   :						Optional[int]    =							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   :						Optional[Any]    =							check_and_map_params(
                self_output.dense.bias		,F'''encoder.transformer_cells.{i}.proj.bias'''      )
            _UpperCamelCase   :						List[Any]    =							check_and_map_params(
                self_output.dense.weight		,F'''encoder.transformer_cells.{i}.proj.weight'''      )
            _UpperCamelCase   :						Union[str, Any]    =							check_and_map_params(
                self_output.LayerNorm.bias		,F'''encoder.transformer_cells.{i}.layer_norm.beta'''      )
            _UpperCamelCase   :						Tuple    =							check_and_map_params(
                self_output.LayerNorm.weight		,F'''encoder.transformer_cells.{i}.layer_norm.gamma'''      )

            # intermediate
            _UpperCamelCase   :						BertIntermediate    =							layer.intermediate

            _UpperCamelCase   :						int    =							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[str]    =							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   :						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   :						Optional[Any]    =							RobertaTokenizer.from_pretrained("roberta-base"      )

      _UpperCamelCase   :						Optional[Any]    =							tokenizer.encode_plus(lowercase_      )["input_ids"]

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

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(lowercase_      )
      _UpperCamelCase   :						int    =							BertModel.from_pretrained(lowercase_      )
      hf_bort_model.eval()

      _UpperCamelCase   :						Optional[Any]    =							tokenizer.encode_plus(lowercase_		,return_tensors="pt"      )
      _UpperCamelCase   :						Optional[int]    =							hf_bort_model(**lowercase_      )[0]

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

      _UpperCamelCase   :						Dict    =							np.max(np.abs(hf_layer - gluon_layer      )      ).item()
      _UpperCamelCase   :						List[str]    =							np.allclose(lowercase_		,lowercase_		,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:"		,lowercase_      )


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







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
