



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_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							"The Nymphenburg Palace is a beautiful palace in Munich!"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str				)->Optional[Any]:
      _lowerCAmelCase 			=  {
          '''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,
      }

      _lowerCAmelCase 			=  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
      _lowerCAmelCase 			=  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=_SCREAMING_SNAKE_CASE     ,				output_all_encodings=_SCREAMING_SNAKE_CASE     ,				use_residual=predefined_args['''use_residual''']     ,				activation=predefined_args.get('''activation'''     ,				'''gelu'''				)     ,				layer_norm_eps=predefined_args.get('''layer_norm_eps'''     ,				_SCREAMING_SNAKE_CASE				)     ,				)

      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      _lowerCAmelCase 			=  '''openwebtext_ccnews_stories_books_cased'''

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

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

      original_bort.load_parameters(_SCREAMING_SNAKE_CASE     ,				cast_dtype=_SCREAMING_SNAKE_CASE     ,				ignore_extra=_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  original_bort._collect_params_with_prefix()

      # Build our config 🤗
      _lowerCAmelCase 			=  {
          '''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(_SCREAMING_SNAKE_CASE				),
      }

      _lowerCAmelCase 			=  BertConfig.from_dict(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  BertForMaskedLM(_SCREAMING_SNAKE_CASE				)
      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(_SCREAMING_SNAKE_CASE   :	Optional[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(_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[str]				):
            _lowerCAmelCase 			=  hf_param.shape

            _lowerCAmelCase 			=  to_torch(params[gluon_param]				)
            _lowerCAmelCase 			=  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

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

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

            # self attention
            _lowerCAmelCase 			=  layer.attention.self

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

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

            # self attention output
            _lowerCAmelCase 			=  layer.attention.output

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

            # intermediate
            _lowerCAmelCase 			=  layer.intermediate

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

            # output
            _lowerCAmelCase 			=  layer.output

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

      _lowerCAmelCase 			=  tokenizer.encode_plus(_SCREAMING_SNAKE_CASE				)['''input_ids''']

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

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

      _lowerCAmelCase 			=  tokenizer.encode_plus(_SCREAMING_SNAKE_CASE     ,				return_tensors='''pt'''				)
      _lowerCAmelCase 			=  hf_bort_model(**_SCREAMING_SNAKE_CASE				)[0]

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

      _lowerCAmelCase 			=  np.max(np.abs(hf_layer - gluon_layer				)				).item()
      _lowerCAmelCase 			=  np.allclose(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				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:'''     ,				_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							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_									=							parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list[int]     ,				_SCREAMING_SNAKE_CASE   :	str				)->list[int]:
      _lowerCAmelCase 			=  int(_SCREAMING_SNAKE_CASE				)

      # Initialize Result
      _lowerCAmelCase 			=  []

      # Traverse through all denomination
      for denomination in reversed(_SCREAMING_SNAKE_CASE				):
            # Find denominations
            while int(_SCREAMING_SNAKE_CASE				) >= int(_SCREAMING_SNAKE_CASE				):
                  total_value -= int(_SCREAMING_SNAKE_CASE				)
                  answer.append(_SCREAMING_SNAKE_CASE				)  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
    UpperCAmelCase_									=							[]
    UpperCAmelCase_									=							"0"

    if (
        input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
        == "y"
    ):
        UpperCAmelCase_									=							int(input("Enter the number of denominations you want to add: ").strip())

        for i in range(0, n):
            denominations.append(int(input(F"""Denomination {i}: """).strip()))
        UpperCAmelCase_									=							input("Enter the change you want to make in Indian Currency: ").strip()
    else:
        # All denominations of Indian Currency if user does not enter
        UpperCAmelCase_									=							[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
        UpperCAmelCase_									=							input("Enter the change you want to make: ").strip()

    if int(value) == 0 or int(value) < 0:
        print("The total value cannot be zero or negative.")

    else:
        print(F"""Following is minimal change for {value}: """)
        UpperCAmelCase_									=							find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=" ")