
'''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()
SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

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







def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Dict				       =							{
      "attention_cell": "multi_head",
      "num_layers": 4,
      "units": 1_0_2_4,
      "hidden_size": 7_6_8,
      "max_length": 5_1_2,
      "num_heads": 8,
      "scaled": True,
      "dropout": 0.1,
      "use_residual": True,
      "embed_size": 1_0_2_4,
      "embed_dropout": 0.1,
      "word_embed": None,
      "layer_norm_eps": 1E-5,
      "token_type_vocab_size": 2,
  }

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

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

  UpperCAmelCase__      :					Dict				       =							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__      :					List[Any]				       =							original_bort._collect_params_with_prefix()

  # Build our config 🤗
  UpperCAmelCase__      :					Optional[int]				       =							{
      "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__      :					List[Any]				       =							BertConfig.from_dict(lowercase__		)
  UpperCAmelCase__      :					List[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__      :					int				       =							hf_param.shape

    UpperCAmelCase__      :					Tuple				       =							to_torch(params[gluon_param]		)
    UpperCAmelCase__      :					List[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__      :					int				       =							check_and_map_params(
      hf_bort_model.bert.embeddings.word_embeddings.weight   ,       "word_embed.0.weight"		)
  UpperCAmelCase__      :					Optional[Any]				       =							check_and_map_params(
      hf_bort_model.bert.embeddings.position_embeddings.weight   ,       "encoder.position_weight"		)
  UpperCAmelCase__      :					List[Any]				       =							check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.bias   ,       "encoder.layer_norm.beta"		)
  UpperCAmelCase__      :					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)
  UpperCAmelCase__      :					List[Any]				       =							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[int]				       =							check_and_map_params(
        self_attn.key.bias.data   ,       F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""		)

    UpperCAmelCase__      :					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__      :					str				       =							check_and_map_params(
        self_attn.query.weight.data   ,       F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""		)
    UpperCAmelCase__      :					Dict				       =							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__      :					Dict				       =							check_and_map_params(
        self_output.dense.bias   ,       F"""encoder.transformer_cells.{i}.proj.bias"""		)
    UpperCAmelCase__      :					Tuple				       =							check_and_map_params(
        self_output.dense.weight   ,       F"""encoder.transformer_cells.{i}.proj.weight"""		)
    UpperCAmelCase__      :					int				       =							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__      :					Optional[int]				       =							check_and_map_params(
        intermediate.dense.bias   ,       F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""		)
    UpperCAmelCase__      :					Any				       =							check_and_map_params(
        intermediate.dense.weight   ,       F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""		)

    # output
    UpperCAmelCase__      :					BertOutput				       =							layer.output

    UpperCAmelCase__      :					Optional[int]				       =							check_and_map_params(
        bert_output.dense.bias   ,       F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""		)
    UpperCAmelCase__      :					Any				       =							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__      :					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__      :					str				       =							RobertaTokenizer.from_pretrained("roberta-base"		)

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

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

  # Get Transformer output (save and reload model again)
  hf_bort_model.save_pretrained(lowercase__		)
  UpperCAmelCase__      :					List[Any]				       =							BertModel.from_pretrained(lowercase__		)
  hf_bort_model.eval()

  UpperCAmelCase__      :					List[str]				       =							tokenizer.encode_plus(lowercase__   ,       return_tensors="pt"		)
  UpperCAmelCase__      :					Dict				       =							hf_bort_model(**lowercase__		)[0]

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

  UpperCAmelCase__      :					Dict				       =							np.max(np.abs(hf_layer - gluon_layer		)		).item()
  UpperCAmelCase__      :					List[Any]				       =							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__":
      SCREAMING_SNAKE_CASE     =       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."""
      )
      SCREAMING_SNAKE_CASE     =       parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





import math







def snake_case_     (					lowercase__		):

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,       int(math.sqrt(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



