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()
lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			'The Nymphenburg Palace is a beautiful palace in Munich!'






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = {
           '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,
       }

       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=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
       lowerCAmelCase__					     = 'openwebtext_ccnews_stories_books_cased'

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

       lowerCAmelCase__					     = 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__     )
       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(lowerCAmelCase__     ),
       }

       lowerCAmelCase__					     = BertConfig.from_dict(lowerCAmelCase__     )
       lowerCAmelCase__					     = 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__     ):
              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(lowerCAmelCase__     )['input_ids']

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

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

       lowerCAmelCase__					     = tokenizer.encode_plus(lowerCAmelCase__     ,  return_tensors='pt'     )
       lowerCAmelCase__					     = hf_bort_model(**lowerCAmelCase__     )[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(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__":
    lowerCAmelCase__     						=			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.'
    )
    lowerCAmelCase__     						=			parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  pyspark.sql.DataFrame      ,   lowercase__ :  Optional[NamedSplit] = None      ,   lowercase__ :  Optional[Features] = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = "arrow"      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



        super().__init__(
            split=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   keep_in_memory=lowercase__      ,   streaming=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = load_from_cache_file
        lowerCAmelCase__					     = file_format
        lowerCAmelCase__					     = Spark(
            df=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   working_dir=lowercase__      ,   **lowercase__      ,   )




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        if self.streaming:
               return self.builder.as_streaming_dataset(split=self.split)
        lowerCAmelCase__					     = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowercase__      ,   file_format=self._file_format      ,   )
        return self.builder.as_dataset(split=self.split)
