
'''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__   :		Any 		=logging.get_logger(__name__)

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		{
      """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,
  }

  __A		:     Tuple												=		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
  __A		:     Union[str, 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=__SCREAMING_SNAKE_CASE			,     output_all_encodings=__SCREAMING_SNAKE_CASE			,     use_residual=predefined_args["""use_residual"""]			,     activation=predefined_args.get("""activation"""			,     """gelu""" )			,     layer_norm_eps=predefined_args.get("""layer_norm_eps"""			,     __SCREAMING_SNAKE_CASE )			,     )

  # Vocab information needs to be fetched first
  # It's the same as RoBERTa, so RobertaTokenizer can be used later
  __A		:     Optional[Any]												=		"""openwebtext_ccnews_stories_books_cased"""

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

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

  original_bort.load_parameters(__SCREAMING_SNAKE_CASE			,     cast_dtype=__SCREAMING_SNAKE_CASE			,     ignore_extra=__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		original_bort._collect_params_with_prefix()

  # Build our config 🤗
  __A		:     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(__SCREAMING_SNAKE_CASE ),
  }

  __A		:     Optional[int]												=		BertConfig.from_dict(__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		BertForMaskedLM(__SCREAMING_SNAKE_CASE )
  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(__SCREAMING_SNAKE_CASE						:  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(__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Dict ):
    __A		:     Optional[Any]												=		hf_param.shape

    __A		:     List[str]												=		to_torch(params[gluon_param] )
    __A		:     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

  __A		:     List[str]												=		check_and_map_params(
      hf_bort_model.bert.embeddings.word_embeddings.weight			,     """word_embed.0.weight""" )
  __A		:     Any												=		check_and_map_params(
      hf_bort_model.bert.embeddings.position_embeddings.weight			,     """encoder.position_weight""" )
  __A		:     Optional[int]												=		check_and_map_params(
      hf_bort_model.bert.embeddings.LayerNorm.bias			,     """encoder.layer_norm.beta""" )
  __A		:     Optional[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)
  __A		:     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 ):
    __A		:     BertLayer												=		hf_bort_model.bert.encoder.layer[i]

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

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

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

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

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

    # intermediate
    __A		:     BertIntermediate												=		layer.intermediate

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

    # output
    __A		:     BertOutput												=		layer.output

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

  __A		:     List[Any]												=		tokenizer.encode_plus(__SCREAMING_SNAKE_CASE )["""input_ids"""]

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

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

  __A		:     Optional[int]												=		tokenizer.encode_plus(__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" )
  __A		:     str												=		hf_bort_model(**__SCREAMING_SNAKE_CASE )[0]

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

  __A		:     List[Any]												=		np.max(np.abs(hf_layer - gluon_layer ) ).item()
  __A		:     List[str]												=		np.allclose(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     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:"""			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		List[Any] 		=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__   :		Dict 		=parser.parse_args()
     convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''
from collections.abc import Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



