
"""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      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			int					=							"""The Nymphenburg Palace is a beautiful palace in Munich!"""
def 				a__   (   a   :				str       ,     a   :				str		):

  """simple docstring"""







  _snake_case     :						List[str]						=   {
      "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,
  }

  _snake_case     :						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
  _snake_case     :						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=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
  _snake_case     :						str						=   "openwebtext_ccnews_stories_books_cased"

  # Specify download folder to Gluonnlp's vocab
  _snake_case     :						Tuple						=   os.path.join(get_home_dir()       ,     "models"		)
  _snake_case     :						str						=   _load_vocab(a       ,     a       ,     a       ,     cls=a		)

  _snake_case     :						Tuple						=   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		)
  _snake_case     :						Optional[Any]						=   original_bort._collect_params_with_prefix()

  # Build our config 🤗
  _snake_case     :						str						=   {
      "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		),
  }

  _snake_case     :						Any						=   BertConfig.from_dict(a		)
  _snake_case     :						Dict						=   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   :				Any		) -> 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   :				List[Any]       ,     a   :				List[str]		):
    _snake_case     :						Optional[Any]						=   hf_param.shape

    _snake_case     :						str						=   to_torch(params[gluon_param]		)
    _snake_case     :						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

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

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

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

    _snake_case     :						int						=   check_and_map_params(
        self_attn.key.bias.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'		)

    _snake_case     :						Dict						=   check_and_map_params(
        self_attn.key.weight.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'		)
    _snake_case     :						Dict						=   check_and_map_params(
        self_attn.query.bias.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'		)
    _snake_case     :						str						=   check_and_map_params(
        self_attn.query.weight.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'		)
    _snake_case     :						Optional[int]						=   check_and_map_params(
        self_attn.value.bias.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'		)
    _snake_case     :						List[str]						=   check_and_map_params(
        self_attn.value.weight.data       ,     f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'		)

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

    _snake_case     :						Any						=   check_and_map_params(
        self_output.dense.bias       ,     f'encoder.transformer_cells.{i}.proj.bias'		)
    _snake_case     :						Dict						=   check_and_map_params(
        self_output.dense.weight       ,     f'encoder.transformer_cells.{i}.proj.weight'		)
    _snake_case     :						int						=   check_and_map_params(
        self_output.LayerNorm.bias       ,     f'encoder.transformer_cells.{i}.layer_norm.beta'		)
    _snake_case     :						Any						=   check_and_map_params(
        self_output.LayerNorm.weight       ,     f'encoder.transformer_cells.{i}.layer_norm.gamma'		)

    # intermediate
    _snake_case     :						BertIntermediate						=   layer.intermediate

    _snake_case     :						int						=   check_and_map_params(
        intermediate.dense.bias       ,     f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'		)
    _snake_case     :						Union[str, Any]						=   check_and_map_params(
        intermediate.dense.weight       ,     f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'		)

    # output
    _snake_case     :						BertOutput						=   layer.output

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

  _snake_case     :						Any						=   tokenizer.encode_plus(a		)["input_ids"]

  # Get gluon output
  _snake_case     :						Union[str, Any]						=   mx.nd.array([input_ids]		)
  _snake_case     :						Optional[int]						=   original_bort(inputs=a       ,     token_types=[]		)

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

  _snake_case     :						str						=   tokenizer.encode_plus(a       ,     return_tensors="pt"		)
  _snake_case     :						Tuple						=   hf_bort_model(**a		)[0]

  _snake_case     :						Any						=   output_gluon[0].asnumpy()
  _snake_case     :						Tuple						=   output_hf[0].detach().numpy()

  _snake_case     :						Optional[int]						=   np.max(np.abs(hf_layer - gluon_layer		)		).item()
  _snake_case     :						List[Any]						=   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__":
    _a      :			Tuple					=							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      :			Tuple					=							parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""
from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				bool = False		):

  """simple docstring"""







  if radian_mode:
    return [magnitude * cos(a		), magnitude * sin(a		)]
  return [magnitude * cos(radians(a		)		), magnitude * sin(radians(a		)		)]
def 				a__   (   a   :				NDArray[floataa]       ,     a   :				NDArray[floataa]       ,     a   :				float = 10**-1		):

  """simple docstring"""







  _snake_case     :						NDArray[floataa]						=   cross(a       ,     a		)
  _snake_case     :						float						=   sum(a		)
  return abs(a		) < eps


if __name__ == "__main__":
    # Test to check if it works
    _a      :			Tuple					=							array(
        [
            polar_force(718.4, 180 - 30),
            polar_force(879.54, 45),
            polar_force(100, -90),
        ]
    )

    _a      :			NDArray[floataa]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    _a      :			List[Any]					=							array(
        [
            polar_force(30 * 9.81, 15),
            polar_force(215, 180 - 45),
            polar_force(264, 90 - 30),
        ]
    )

    _a      :			List[Any]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    _a      :			List[str]					=							array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])

    _a      :			Optional[int]					=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()

