





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()
lowercase    : Tuple    	=  logging.get_logger(__name__)

lowercase    : List[str]    	=  """The Nymphenburg Palace is a beautiful palace in Munich!"""


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						{
           '''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,
       }

       _UpperCamelCase              =						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
       _UpperCamelCase              =						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
       _UpperCamelCase              =						'''openwebtext_ccnews_stories_books_cased'''

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

       _UpperCamelCase              =						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					)
       _UpperCamelCase              =						original_bort._collect_params_with_prefix()

       # Build our config 🤗
       _UpperCamelCase              =						{
           '''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					),
       }

       _UpperCamelCase              =						BertConfig.from_dict(lowerCAmelCase					)
       _UpperCamelCase              =						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					):
              _UpperCamelCase              =						hf_param.shape

              _UpperCamelCase              =						to_torch(params[gluon_param]					)
              _UpperCamelCase              =						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

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

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

              # self attention
              _UpperCamelCase              =						layer.attention.self

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

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

              # self attention output
              _UpperCamelCase              =						layer.attention.output

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

              # intermediate
              _UpperCamelCase              =						layer.intermediate

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

              # output
              _UpperCamelCase              =						layer.output

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

       _UpperCamelCase              =						tokenizer.encode_plus(lowerCAmelCase					)['''input_ids''']

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

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

       _UpperCamelCase              =						tokenizer.encode_plus(lowerCAmelCase    ,       return_tensors='''pt'''					)
       _UpperCamelCase              =						hf_bort_model(**lowerCAmelCase					)[0]

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

       _UpperCamelCase              =						np.max(np.abs(hf_layer - gluon_layer					)					).item()
       _UpperCamelCase              =						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__":
       lowercase    : str    	=  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."""
       )
       lowercase    : List[Any]    	=  parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






