

"""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_	:				Any										= logging.get_logger(__name__)

UpperCamelCase_	:				Any										= """The Nymphenburg Palace is a beautiful palace in Munich!"""



def     __lowercase					(    a :				str       ,		a :				str )					->					Union[str, Any]:
      __snake_case      :    str  			={
          '''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,
      }

      __snake_case      :    Union[str, Any]  			=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
      __snake_case      :    Optional[int]  			=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
      __snake_case      :    Union[str, Any]  			='''openwebtext_ccnews_stories_books_cased'''

      # Specify download folder to Gluonnlp's vocab
      __snake_case      :    str  			=os.path.join(get_home_dir()       ,		'''models''' )
      __snake_case      :    List[Any]  			=_load_vocab(a       ,		a       ,		a       ,		cls=a )

      __snake_case      :    Optional[int]  			=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 )
      __snake_case      :    int  			=original_bort._collect_params_with_prefix()

      # Build our config 🤗
      __snake_case      :    str  			={
          '''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 ),
      }

      __snake_case      :    Optional[Any]  			=BertConfig.from_dict(a )
      __snake_case      :    List[str]  			=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 :				int ) -> 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 :				List[Any]       ,		a :				Optional[int] ):
            __snake_case      :    str  			=hf_param.shape

            __snake_case      :    str  			=to_torch(params[gluon_param] )
            __snake_case      :    Tuple  			=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

      __snake_case      :    Optional[int]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight       ,		'''word_embed.0.weight''' )
      __snake_case      :    Union[str, Any]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight       ,		'''encoder.position_weight''' )
      __snake_case      :    Union[str, Any]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias       ,		'''encoder.layer_norm.beta''' )
      __snake_case      :    List[Any]  			=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)
      __snake_case      :    str  			=torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

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

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

            __snake_case      :    Optional[int]  			=check_and_map_params(
                self_attn.key.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )

            __snake_case      :    List[Any]  			=check_and_map_params(
                self_attn.key.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
            __snake_case      :    Tuple  			=check_and_map_params(
                self_attn.query.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_attn.query.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_attn.value.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
            __snake_case      :    List[str]  			=check_and_map_params(
                self_attn.value.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )

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

            __snake_case      :    Optional[int]  			=check_and_map_params(
                self_output.dense.bias       ,		f'''encoder.transformer_cells.{i}.proj.bias''' )
            __snake_case      :    Union[str, Any]  			=check_and_map_params(
                self_output.dense.weight       ,		f'''encoder.transformer_cells.{i}.proj.weight''' )
            __snake_case      :    Dict  			=check_and_map_params(
                self_output.LayerNorm.bias       ,		f'''encoder.transformer_cells.{i}.layer_norm.beta''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_output.LayerNorm.weight       ,		f'''encoder.transformer_cells.{i}.layer_norm.gamma''' )

            # intermediate
            __snake_case      :    BertIntermediate  			=layer.intermediate

            __snake_case      :    Dict  			=check_and_map_params(
                intermediate.dense.bias       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
            __snake_case      :    List[Any]  			=check_and_map_params(
                intermediate.dense.weight       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )

            # output
            __snake_case      :    BertOutput  			=layer.output

            __snake_case      :    Union[str, Any]  			=check_and_map_params(
                bert_output.dense.bias       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                bert_output.dense.weight       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
            __snake_case      :    str  			=check_and_map_params(
                bert_output.LayerNorm.bias       ,		f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
            __snake_case      :    str  			=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
      __snake_case      :    str  			=RobertaTokenizer.from_pretrained('''roberta-base''' )

      __snake_case      :    Dict  			=tokenizer.encode_plus(a )['''input_ids''']

      # Get gluon output
      __snake_case      :    Optional[Any]  			=mx.nd.array([input_ids] )
      __snake_case      :    Dict  			=original_bort(inputs=a       ,		token_types=[] )

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(a )
      __snake_case      :    List[str]  			=BertModel.from_pretrained(a )
      hf_bort_model.eval()

      __snake_case      :    int  			=tokenizer.encode_plus(a       ,		return_tensors='''pt''' )
      __snake_case      :    Any  			=hf_bort_model(**a )[0]

      __snake_case      :    str  			=output_gluon[0].asnumpy()
      __snake_case      :    int  			=output_hf[0].detach().numpy()

      __snake_case      :    int  			=np.max(np.abs(hf_layer - gluon_layer ) ).item()
      __snake_case      :    Tuple  			=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__":
    UpperCamelCase_	:				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."""
    )
    UpperCamelCase_	:				List[str]										= parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""





def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return x if y == 0 else greatest_common_divisor(a       ,		x % y )



def     __lowercase					(    a :				int       ,		a :				int )					->					int:
      return (x * y) // greatest_common_divisor(a       ,		a )



def     __lowercase					(    a :				int = 20 )					->					int:
      __snake_case      :    Optional[Any]  			=1
      for i in range(1       ,		n + 1 ):
            __snake_case      :    Optional[int]  			=lcm(a       ,		a )
      return g


if __name__ == "__main__":
    print(F'''{solution() = }''')


