

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_    :						Dict    		=      logging.get_logger(__name__)

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							List[Any]:
 '''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=_UpperCamelCase		,							output_all_encodings=_UpperCamelCase		,							use_residual=predefined_args['use_residual']		,							activation=predefined_args.get('activation'		,							'gelu'	)		,							layer_norm_eps=predefined_args.get('layer_norm_eps'		,							_UpperCamelCase	)		,							)

 # 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(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							cls=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			nlp.model.BERTModel(
     _UpperCamelCase		,							len(_UpperCamelCase	)		,							units=predefined_args['units']		,							embed_size=predefined_args['embed_size']		,							embed_dropout=predefined_args['embed_dropout']		,							word_embed=predefined_args['word_embed']		,							use_pooler=_UpperCamelCase		,							use_token_type_embed=_UpperCamelCase		,							token_type_vocab_size=predefined_args['token_type_vocab_size']		,							use_classifier=_UpperCamelCase		,							use_decoder=_UpperCamelCase		,							)

 original_bort.load_parameters(_UpperCamelCase		,							cast_dtype=_UpperCamelCase		,							ignore_extra=_UpperCamelCase	)
 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': 0.02,
     '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(_UpperCamelCase	),
 }

 SCREAMING_SNAKE_CASE								=			BertConfig.from_dict(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			BertForMaskedLM(_UpperCamelCase	)
 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(_UpperCamelCase		:  Union[str, Any]	) -> 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(_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  List[Any]	):
  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(_UpperCamelCase	)['input_ids']

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

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

 SCREAMING_SNAKE_CASE								=			tokenizer.encode_plus(_UpperCamelCase		,							return_tensors='pt'	)
 SCREAMING_SNAKE_CASE								=			hf_bort_model(**_UpperCamelCase	)[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(_UpperCamelCase		,							_UpperCamelCase		,							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:'		,							_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Optional[Any]    		=      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_    :						Any    		=      parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




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


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

