

"""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()
_A							=  logging.get_logger(__name__)

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




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Optional[int]:
      UpperCAmelCase__			:							int							=    {
          """attention_cell""": """multi_head""",
          """num_layers""": 4,
          """units""": 10_24,
          """hidden_size""": 7_68,
          """max_length""": 5_12,
          """num_heads""": 8,
          """scaled""": True,
          """dropout""": 0.1,
          """use_residual""": True,
          """embed_size""": 10_24,
          """embed_dropout""": 0.1,
          """word_embed""": None,
          """layer_norm_eps""": 1E-5,
          """token_type_vocab_size""": 2,
      }

      UpperCAmelCase__			:							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__			:							Optional[Any]							=    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
      UpperCAmelCase__			:							Union[str, Any]							=    """openwebtext_ccnews_stories_books_cased"""

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

      UpperCAmelCase__			:							List[str]							=    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    )
      UpperCAmelCase__			:							Optional[int]							=    original_bort._collect_params_with_prefix()

      # Build our config 🤗
      UpperCAmelCase__			:							Union[str, 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""": 0.02,
          """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(lowerCAmelCase    ),
      }

      UpperCAmelCase__			:							str							=    BertConfig.from_dict(lowerCAmelCase    )
      UpperCAmelCase__			:							str							=    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    ):
            UpperCAmelCase__			:							str							=    hf_param.shape

            UpperCAmelCase__			:							List[Any]							=    to_torch(params[gluon_param]    )
            UpperCAmelCase__			:							Dict							=    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__			:							Tuple							=    check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight			,	"""encoder.position_weight"""    )
      UpperCAmelCase__			:							List[str]							=    check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias			,	"""encoder.layer_norm.beta"""    )
      UpperCAmelCase__			:							Dict							=    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__			:							List[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__			:							Optional[Any]							=    check_and_map_params(
                self_attn.key.bias.data			,	F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""    )

            UpperCAmelCase__			:							List[Any]							=    check_and_map_params(
                self_attn.key.weight.data			,	F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""    )
            UpperCAmelCase__			:							Optional[int]							=    check_and_map_params(
                self_attn.query.bias.data			,	F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""    )
            UpperCAmelCase__			:							Optional[int]							=    check_and_map_params(
                self_attn.query.weight.data			,	F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""    )
            UpperCAmelCase__			:							Optional[int]							=    check_and_map_params(
                self_attn.value.bias.data			,	F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""    )
            UpperCAmelCase__			:							Optional[Any]							=    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__			:							Union[str, Any]							=    check_and_map_params(
                self_output.dense.weight			,	F"""encoder.transformer_cells.{i}.proj.weight"""    )
            UpperCAmelCase__			:							Optional[Any]							=    check_and_map_params(
                self_output.LayerNorm.bias			,	F"""encoder.transformer_cells.{i}.layer_norm.beta"""    )
            UpperCAmelCase__			:							int							=    check_and_map_params(
                self_output.LayerNorm.weight			,	F"""encoder.transformer_cells.{i}.layer_norm.gamma"""    )

            # intermediate
            UpperCAmelCase__			:							BertIntermediate							=    layer.intermediate

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

            # output
            UpperCAmelCase__			:							BertOutput							=    layer.output

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

      UpperCAmelCase__			:							List[str]							=    tokenizer.encode_plus(lowerCAmelCase    )["""input_ids"""]

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

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

      UpperCAmelCase__			:							Any							=    tokenizer.encode_plus(lowerCAmelCase			,	return_tensors="""pt"""    )
      UpperCAmelCase__			:							Optional[Any]							=    hf_bort_model(**lowerCAmelCase    )[0]

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

      UpperCAmelCase__			:							Dict							=    np.max(np.abs(hf_layer - gluon_layer    )    ).item()
      UpperCAmelCase__			:							Dict							=    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__":
    _A							=  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."""
    )
    _A							=  parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""
_A							=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def 					a__						(						)	-> None:
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter message: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter key [alphanumeric]: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Encrypt/Decrypt [e/d]: """    )

      if mode.lower().startswith("""e"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """encrypt"""
            UpperCAmelCase__			:							List[Any]							=    encrypt_message(lowerCAmelCase			,	lowerCAmelCase    )
      elif mode.lower().startswith("""d"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """decrypt"""
            UpperCAmelCase__			:							Dict							=    decrypt_message(lowerCAmelCase			,	lowerCAmelCase    )

      print(F"""\n{mode.title()}ed message:"""    )
      print(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""encrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""decrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							Optional[Any]							=    []
      UpperCAmelCase__			:							int							=    0
      UpperCAmelCase__			:							str							=    key.upper()

      for symbol in message:
            UpperCAmelCase__			:							Optional[Any]							=    LETTERS.find(symbol.upper()    )
            if num != -1:
                  if mode == "encrypt":
                        num += LETTERS.find(key[key_index]    )
                  elif mode == "decrypt":
                        num -= LETTERS.find(key[key_index]    )

                  num %= len(lowerCAmelCase    )

                  if symbol.isupper():
                        translated.append(LETTERS[num]    )
                  elif symbol.islower():
                        translated.append(LETTERS[num].lower()    )

                  key_index += 1
                  if key_index == len(lowerCAmelCase    ):
                        UpperCAmelCase__			:							List[str]							=    0
            else:
                  translated.append(lowerCAmelCase    )
      return "".join(lowerCAmelCase    )


if __name__ == "__main__":
    main()

