




"""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 						_snake_case     (					_snake_case							:					Any				,  _snake_case							:					Union[str, Any]							)    ->					List[str]:






      '''simple docstring'''


      _A					   =   {
          '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,
      }

      _A					   =   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
      _A					   =   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=_snake_case				,  output_all_encodings=_snake_case				,  use_residual=predefined_args['use_residual']				,  activation=predefined_args.get('activation'				,  'gelu'							)				,  layer_norm_eps=predefined_args.get('layer_norm_eps'				,  _snake_case							)				,  )

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

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

      _A					   =   nlp.model.BERTModel(
          _snake_case				,  len(_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=_snake_case				,  use_token_type_embed=_snake_case				,  token_type_vocab_size=predefined_args['token_type_vocab_size']				,  use_classifier=_snake_case				,  use_decoder=_snake_case				,  )

      original_bort.load_parameters(_snake_case				,  cast_dtype=_snake_case				,  ignore_extra=_snake_case							)
      _A					   =   original_bort._collect_params_with_prefix()

      # Build our config 🤗
      _A					   =   {
          '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(_snake_case							),
      }

      _A					   =   BertConfig.from_dict(_snake_case							)
      _A					   =   BertForMaskedLM(_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(_snake_case							:					List[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(_snake_case							:					str				,  _snake_case							:					int							):
            _A					   =   hf_param.shape

            _A					   =   to_torch(params[gluon_param]							)
            _A					   =   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

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

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

            # self attention
            _A					   =   layer.attention.self

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

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

            # self attention output
            _A					   =   layer.attention.output

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

            # intermediate
            _A					   =   layer.intermediate

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

            # output
            _A					   =   layer.output

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

      _A					   =   tokenizer.encode_plus(_snake_case							)['input_ids']

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

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

      _A					   =   tokenizer.encode_plus(_snake_case				,  return_tensors='pt'							)
      _A					   =   hf_bort_model(**_snake_case							)[0]

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

      _A					   =   np.max(np.abs(hf_layer - gluon_layer							)							).item()
      _A					   =   np.allclose(_snake_case				,  _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:'				,  _snake_case							)


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"""
import math
import sys






def 					A__				(			UpperCamelCase       ):
							A		=  ""
							try:
														with open(UpperCamelCase					,  "rb"       ) as binary_file:
																					A		=  binary_file.read()
														for dat in data:
																					A		=  F"{dat:08b}"
																					result += curr_byte
														return result
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  {"0": "0", "1": "1"}
							A,	A		=  "", ""
							A		=  len(UpperCamelCase       )

							for i in range(len(UpperCamelCase       )       ):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														A		=  lexicon[curr_string]
														result += last_match_id
														A		=  last_match_id + "0"

														if math.loga(UpperCamelCase       ).is_integer():
																					A		=  {}
																					for curr_key in list(UpperCamelCase       ):
																												A		=  lexicon.pop(UpperCamelCase       )
																					A		=  new_lex

														A		=  last_match_id + "1"
														index += 1
														A		=  ""
							return result






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  8
							try:
														with open(UpperCamelCase					,  "wb"       ) as opened_file:
																					A		=  [
																					    to_write[i : i + byte_length]
																					    for i in range(0					,  len(UpperCamelCase       )					,  UpperCamelCase       )
																					]

																					if len(result_byte_array[-1]       ) % byte_length == 0:
																												result_byte_array.append("10000000"       )
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]       ) - 1
																												)

																					for elem in result_byte_array[:-1]:
																												opened_file.write(int(UpperCamelCase					,  2       ).to_bytes(1					,  byteorder="big"       )       )
							except OSError:
														print("File not accessible"       )
														sys.exit()






def 					A__				(			UpperCamelCase       ):
							A		=  0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

							A		=  data_bits[counter:]
							A		=  data_bits[counter + 1 :]
							return data_bits






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  read_file_binary(UpperCamelCase       )
							A		=  remove_prefix(UpperCamelCase       )
							A		=  decompress_data(UpperCamelCase       )
							write_file_binary(UpperCamelCase					,  UpperCamelCase       )


if __name__ == "__main__":
		compress(sys.argv[1], sys.argv[2])


