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

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






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[Any]:
    __lowerCamelCase		=  {
        '''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,
    }

    __lowerCamelCase		=  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
    __lowerCamelCase		=  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=UpperCamelCase__				,  output_all_encodings=UpperCamelCase__				,  use_residual=predefined_args['''use_residual''']				,  activation=predefined_args.get('''activation'''				,  '''gelu'''				)				,  layer_norm_eps=predefined_args.get('''layer_norm_eps'''				,  UpperCamelCase__				)				,  )

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

    # Specify download folder to Gluonnlp's vocab
    __lowerCamelCase		=  os.path.join(get_home_dir()				,  '''models'''				)
    __lowerCamelCase		=  _load_vocab(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  cls=UpperCamelCase__				)

    __lowerCamelCase		=  nlp.model.BERTModel(
        UpperCamelCase__				,  len(UpperCamelCase__				)				,  units=predefined_args['''units''']				,  embed_size=predefined_args['''embed_size''']				,  embed_dropout=predefined_args['''embed_dropout''']				,  word_embed=predefined_args['''word_embed''']				,  use_pooler=UpperCamelCase__				,  use_token_type_embed=UpperCamelCase__				,  token_type_vocab_size=predefined_args['''token_type_vocab_size''']				,  use_classifier=UpperCamelCase__				,  use_decoder=UpperCamelCase__				,  )

    original_bort.load_parameters(UpperCamelCase__				,  cast_dtype=UpperCamelCase__				,  ignore_extra=UpperCamelCase__				)
    __lowerCamelCase		=  original_bort._collect_params_with_prefix()

    # Build our config 🤗
    __lowerCamelCase		=  {
        '''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''': 0.0_2,
        '''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(UpperCamelCase__				),
    }

    __lowerCamelCase		=  BertConfig.from_dict(UpperCamelCase__				)
    __lowerCamelCase		=  BertForMaskedLM(UpperCamelCase__				)
    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(UpperCamelCase__				) -> 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(UpperCamelCase__				,  UpperCamelCase__				):
        __lowerCamelCase		=  hf_param.shape

        __lowerCamelCase		=  to_torch(params[gluon_param]				)
        __lowerCamelCase		=  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

    __lowerCamelCase		=  check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight				,  '''word_embed.0.weight'''				)
    __lowerCamelCase		=  check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight				,  '''encoder.position_weight'''				)
    __lowerCamelCase		=  check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias				,  '''encoder.layer_norm.beta'''				)
    __lowerCamelCase		=  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)
    __lowerCamelCase		=  torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

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

        # self attention
        __lowerCamelCase		=  layer.attention.self

        __lowerCamelCase		=  check_and_map_params(
            self_attn.key.bias.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""				)

        __lowerCamelCase		=  check_and_map_params(
            self_attn.key.weight.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_attn.query.bias.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_attn.query.weight.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_attn.value.bias.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_attn.value.weight.data				,  f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""				)

        # self attention output
        __lowerCamelCase		=  layer.attention.output

        __lowerCamelCase		=  check_and_map_params(
            self_output.dense.bias				,  f"""encoder.transformer_cells.{i}.proj.bias"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_output.dense.weight				,  f"""encoder.transformer_cells.{i}.proj.weight"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_output.LayerNorm.bias				,  f"""encoder.transformer_cells.{i}.layer_norm.beta"""				)
        __lowerCamelCase		=  check_and_map_params(
            self_output.LayerNorm.weight				,  f"""encoder.transformer_cells.{i}.layer_norm.gamma"""				)

        # intermediate
        __lowerCamelCase		=  layer.intermediate

        __lowerCamelCase		=  check_and_map_params(
            intermediate.dense.bias				,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""				)
        __lowerCamelCase		=  check_and_map_params(
            intermediate.dense.weight				,  f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""				)

        # output
        __lowerCamelCase		=  layer.output

        __lowerCamelCase		=  check_and_map_params(
            bert_output.dense.bias				,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""				)
        __lowerCamelCase		=  check_and_map_params(
            bert_output.dense.weight				,  f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""				)
        __lowerCamelCase		=  check_and_map_params(
            bert_output.LayerNorm.bias				,  f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""				)
        __lowerCamelCase		=  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
    __lowerCamelCase		=  RobertaTokenizer.from_pretrained('''roberta-base'''				)

    __lowerCamelCase		=  tokenizer.encode_plus(UpperCamelCase__				)['''input_ids''']

    # Get gluon output
    __lowerCamelCase		=  mx.nd.array([input_ids]				)
    __lowerCamelCase		=  original_bort(inputs=UpperCamelCase__				,  token_types=[]				)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(UpperCamelCase__				)
    __lowerCamelCase		=  BertModel.from_pretrained(UpperCamelCase__				)
    hf_bort_model.eval()

    __lowerCamelCase		=  tokenizer.encode_plus(UpperCamelCase__				,  return_tensors='''pt'''				)
    __lowerCamelCase		=  hf_bort_model(**UpperCamelCase__				)[0]

    __lowerCamelCase		=  output_gluon[0].asnumpy()
    __lowerCamelCase		=  output_hf[0].detach().numpy()

    __lowerCamelCase		=  np.max(np.abs(hf_layer - gluon_layer				)				).item()
    __lowerCamelCase		=  np.allclose(UpperCamelCase__				,  UpperCamelCase__				,  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:'''				,  UpperCamelCase__				)


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



'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
