

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

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




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Union[str, Any]  )    -> int:







   """simple docstring"""
   __snake_case				:      str     							= {
       '''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,
   }

   __snake_case				:      str     							= 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				:      Dict     							= 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
   __snake_case				:      int     							= '''openwebtext_ccnews_stories_books_cased'''

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

   __snake_case				:      List[str]     							= 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_  )
   __snake_case				:      List[Any]     							= original_bort._collect_params_with_prefix()

   # Build our config 🤗
   __snake_case				:      Union[str, Any]     							= {
       '''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''': 0.02,
       '''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_  ),
   }

   __snake_case				:      Union[str, Any]     							= BertConfig.from_dict(lowerCAmelCase_  )
   __snake_case				:      List[str]     							= 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(A  :       Optional[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  :       Optional[int]  ,		A  :       Union[str, Any]  ):
      __snake_case				:      Dict     							= hf_param.shape

      __snake_case				:      Union[str, Any]     							= to_torch(params[gluon_param]  )
      __snake_case				:      str     							= 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				:      Union[str, Any]     							= 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				:      int     							= check_and_map_params(
       hf_bort_model.bert.embeddings.LayerNorm.bias  ,		'encoder.layer_norm.beta'  )
   __snake_case				:      int     							= 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				:      Tuple     							= 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				:      List[str]     							= check_and_map_params(
          self_attn.key.bias.data  ,		F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""  )

      __snake_case				:      List[str]     							= check_and_map_params(
          self_attn.key.weight.data  ,		F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""  )
      __snake_case				:      int     							= check_and_map_params(
          self_attn.query.bias.data  ,		F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""  )
      __snake_case				:      List[Any]     							= check_and_map_params(
          self_attn.query.weight.data  ,		F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""  )
      __snake_case				:      List[str]     							= check_and_map_params(
          self_attn.value.bias.data  ,		F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""  )
      __snake_case				:      Union[str, Any]     							= 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				:      Union[str, Any]     							= check_and_map_params(
          self_output.dense.bias  ,		F"""encoder.transformer_cells.{i}.proj.bias"""  )
      __snake_case				:      str     							= check_and_map_params(
          self_output.dense.weight  ,		F"""encoder.transformer_cells.{i}.proj.weight"""  )
      __snake_case				:      Any     							= check_and_map_params(
          self_output.LayerNorm.bias  ,		F"""encoder.transformer_cells.{i}.layer_norm.beta"""  )
      __snake_case				:      Tuple     							= check_and_map_params(
          self_output.LayerNorm.weight  ,		F"""encoder.transformer_cells.{i}.layer_norm.gamma"""  )

      # intermediate
      __snake_case				:      BertIntermediate     							= layer.intermediate

      __snake_case				:      List[str]     							= check_and_map_params(
          intermediate.dense.bias  ,		F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""  )
      __snake_case				:      Tuple     							= check_and_map_params(
          intermediate.dense.weight  ,		F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""  )

      # output
      __snake_case				:      BertOutput     							= layer.output

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

   __snake_case				:      Union[str, Any]     							= tokenizer.encode_plus(lowerCAmelCase_  )['''input_ids''']

   # Get gluon output
   __snake_case				:      Tuple     							= mx.nd.array([input_ids]  )
   __snake_case				:      int     							= original_bort(inputs=lowerCAmelCase_  ,		token_types=[]  )

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(lowerCAmelCase_  )
   __snake_case				:      Tuple     							= BertModel.from_pretrained(lowerCAmelCase_  )
   hf_bort_model.eval()

   __snake_case				:      Dict     							= tokenizer.encode_plus(lowerCAmelCase_  ,		return_tensors='pt'  )
   __snake_case				:      List[str]     							= hf_bort_model(**lowerCAmelCase_  )[0]

   __snake_case				:      Union[str, Any]     							= output_gluon[0].asnumpy()
   __snake_case				:      Dict     							= output_hf[0].detach().numpy()

   __snake_case				:      Dict     							= np.max(np.abs(hf_layer - gluon_layer  )  ).item()
   __snake_case				:      int     							= 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__":
      __A    	=   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.'''
      )
      __A    	=   parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DistilBertForMaskedLM''',
          '''DistilBertForMultipleChoice''',
          '''DistilBertForQuestionAnswering''',
          '''DistilBertForSequenceClassification''',
          '''DistilBertForTokenClassification''',
          '''DistilBertModel''',
          '''DistilBertPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDistilBertForMaskedLM''',
          '''TFDistilBertForMultipleChoice''',
          '''TFDistilBertForQuestionAnswering''',
          '''TFDistilBertForSequenceClassification''',
          '''TFDistilBertForTokenClassification''',
          '''TFDistilBertMainLayer''',
          '''TFDistilBertModel''',
          '''TFDistilBertPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''FlaxDistilBertForMaskedLM''',
          '''FlaxDistilBertForMultipleChoice''',
          '''FlaxDistilBertForQuestionAnswering''',
          '''FlaxDistilBertForSequenceClassification''',
          '''FlaxDistilBertForTokenClassification''',
          '''FlaxDistilBertModel''',
          '''FlaxDistilBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)