


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

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





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   ={
       '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,
   }

   _UpperCAmelCase       :       List[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
   _UpperCAmelCase       :       Tuple							   =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       :       List[str]							   ='openwebtext_ccnews_stories_books_cased'

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

   _UpperCAmelCase       :       Optional[Any]							   =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       :       Optional[int]							   =original_bort._collect_params_with_prefix()

   # Build our config 🤗
   _UpperCAmelCase       :       List[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': 0.02,
       '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							),
   }

   _UpperCAmelCase       :       Optional[Any]							   =BertConfig.from_dict(__lowerCamelCase							)
   _UpperCAmelCase       :       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(__lowerCamelCase	:							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(__lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							List[Any]							):
      _UpperCAmelCase       :       Optional[int]							   =hf_param.shape

      _UpperCAmelCase       :       int							   =to_torch(params[gluon_param]							)
      _UpperCAmelCase       :       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

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

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

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

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

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

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

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

      # intermediate
      _UpperCAmelCase       :       BertIntermediate							   =layer.intermediate

      _UpperCAmelCase       :       Optional[Any]							   =check_and_map_params(
          intermediate.dense.bias	,   f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"							)
      _UpperCAmelCase       :       Union[str, Any]							   =check_and_map_params(
          intermediate.dense.weight	,   f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"							)

      # output
      _UpperCAmelCase       :       BertOutput							   =layer.output

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

   _UpperCAmelCase       :       Any							   =tokenizer.encode_plus(__lowerCamelCase							)['input_ids']

   # Get gluon output
   _UpperCAmelCase       :       Any							   =mx.nd.array([input_ids]							)
   _UpperCAmelCase       :       int							   =original_bort(inputs=__lowerCamelCase	,   token_types=[]							)

   # Get Transformer output (save and reload model again)
   hf_bort_model.save_pretrained(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =BertModel.from_pretrained(__lowerCamelCase							)
   hf_bort_model.eval()

   _UpperCAmelCase       :       int							   =tokenizer.encode_plus(__lowerCamelCase	,   return_tensors='pt'							)
   _UpperCAmelCase       :       Dict							   =hf_bort_model(**__lowerCamelCase							)[0]

   _UpperCAmelCase       :       Optional[Any]							   =output_gluon[0].asnumpy()
   _UpperCAmelCase       :       Optional[Any]							   =output_hf[0].detach().numpy()

   _UpperCAmelCase       :       Dict							   =np.max(np.abs(hf_layer - gluon_layer							)							).item()
   _UpperCAmelCase       :       Optional[Any]							   =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							=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							=parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="glpn"






       def __init__(      self  ,							snake_case=3  ,							snake_case=4  ,							snake_case=[2, 2, 2, 2]  ,							snake_case=[8, 4, 2, 1]  ,							snake_case=[3_2, 6_4, 1_6_0, 2_5_6]  ,							snake_case=[7, 3, 3, 3]  ,							snake_case=[4, 2, 2, 2]  ,							snake_case=[1, 2, 5, 8]  ,							snake_case=[4, 4, 4, 4]  ,							snake_case="gelu"  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=0.1  ,							snake_case=1E-6  ,							snake_case=6_4  ,							snake_case=1_0  ,							snake_case=-1  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(**snake_case)

          _UpperCAmelCase       :       Any							   =num_channels
          _UpperCAmelCase       :       List[str]							   =num_encoder_blocks
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       str							   =sr_ratios
          _UpperCAmelCase       :       Dict							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =patch_sizes
          _UpperCAmelCase       :       Any							   =strides
          _UpperCAmelCase       :       List[str]							   =mlp_ratios
          _UpperCAmelCase       :       Dict							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[Any]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Tuple							   =drop_path_rate
          _UpperCAmelCase       :       str							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[int]							   =decoder_hidden_size
          _UpperCAmelCase       :       List[str]							   =max_depth
          _UpperCAmelCase       :       Dict							   =head_in_index

