

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  :						int         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Dict         =    '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					{
         '''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,
     }

     SCREAMING_SNAKE_CASE__					=					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
     SCREAMING_SNAKE_CASE__					=					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
     SCREAMING_SNAKE_CASE__					=					'''openwebtext_ccnews_stories_books_cased'''

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

     SCREAMING_SNAKE_CASE__					=					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       )
     SCREAMING_SNAKE_CASE__					=					original_bort._collect_params_with_prefix()

     # Build our config 🤗
     SCREAMING_SNAKE_CASE__					=					{
         '''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(_A       ),
     }

     SCREAMING_SNAKE_CASE__					=					BertConfig.from_dict(_A       )
     SCREAMING_SNAKE_CASE__					=					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       ) -> 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   ,	_A       ):
          SCREAMING_SNAKE_CASE__					=					hf_param.shape

          SCREAMING_SNAKE_CASE__					=					to_torch(params[gluon_param]       )
          SCREAMING_SNAKE_CASE__					=					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

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

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

          # self attention
          SCREAMING_SNAKE_CASE__					=					layer.attention.self

          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.key.bias.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''       )

          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.key.weight.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.query.bias.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.query.weight.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.value.bias.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_attn.value.weight.data   ,	F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''       )

          # self attention output
          SCREAMING_SNAKE_CASE__					=					layer.attention.output

          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_output.dense.bias   ,	F'''encoder.transformer_cells.{i}.proj.bias'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_output.dense.weight   ,	F'''encoder.transformer_cells.{i}.proj.weight'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_output.LayerNorm.bias   ,	F'''encoder.transformer_cells.{i}.layer_norm.beta'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              self_output.LayerNorm.weight   ,	F'''encoder.transformer_cells.{i}.layer_norm.gamma'''       )

          # intermediate
          SCREAMING_SNAKE_CASE__					=					layer.intermediate

          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              intermediate.dense.bias   ,	F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''       )
          SCREAMING_SNAKE_CASE__					=					check_and_map_params(
              intermediate.dense.weight   ,	F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''       )

          # output
          SCREAMING_SNAKE_CASE__					=					layer.output

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

     SCREAMING_SNAKE_CASE__					=					tokenizer.encode_plus(_A       )['''input_ids''']

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

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

     SCREAMING_SNAKE_CASE__					=					tokenizer.encode_plus(_A   ,	return_tensors='''pt'''       )
     SCREAMING_SNAKE_CASE__					=					hf_bort_model(**_A       )[0]

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

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





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


