


'''simple docstring'''

import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

a_  :				Any						= 2





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  *,  # begin keyword-only arguments
      UpperCamelCase="<s>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase=None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  bos, unk, pad, eos
   lowerCamelCase_      =  []
   lowerCamelCase_      =  []
   lowerCamelCase_      =  {}
   lowerCamelCase_      =  self.add_symbol(_UpperCAmelCase     )
   lowerCamelCase_      =  self.add_symbol(_UpperCAmelCase     )
   lowerCamelCase_      =  self.add_symbol(_UpperCAmelCase     )
   lowerCamelCase_      =  self.add_symbol(_UpperCAmelCase     )
   if extra_special_symbols:
    for s in extra_special_symbols:
     self.add_symbol(_UpperCAmelCase     )
   lowerCamelCase_      =  len(self.symbols     )






  def __eq__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.indices == other.indices






  def __getitem__(    self			,  UpperCamelCase     ):







   """simple docstring"""






   if idx < len(self.symbols     ):
    return self.symbols[idx]
   return self.unk_word






  def __len__(    self     ):







   """simple docstring"""
   return len(self.symbols     )






  def __contains__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return sym in self.indices






  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  cls()
   d.add_from_file(_UpperCAmelCase     )
   return d






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=1			,  UpperCamelCase=False     ):







   """simple docstring"""






   if word in self.indices and not overwrite:
    lowerCamelCase_      =  self.indices[word]
    lowerCamelCase_      =  self.count[idx] + n
    return idx
   else:
    lowerCamelCase_      =  len(self.symbols     )
    lowerCamelCase_      =  idx
    self.symbols.append(_UpperCAmelCase     )
    self.count.append(_UpperCAmelCase     )
    return idx






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return 0







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""






   if isinstance(_UpperCAmelCase			,  _UpperCAmelCase     ):
    try:
     with open(_UpperCAmelCase			,  "r"			,  encoding="utf-8"     ) as fd:
      self.add_from_file(_UpperCAmelCase     )
    except FileNotFoundError as fnfe:
     raise fnfe
    except UnicodeError:
     raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(_UpperCAmelCase     )     )
    return

   lowerCamelCase_      =  f.readlines()
   lowerCamelCase_      =  self._load_meta(_UpperCAmelCase     )

   for line in lines[indices_start_line:]:
    try:
     lowerCamelCase_      =  line.rstrip().rsplit(" "			,  1     )
     if field == "#fairseq:overwrite":
      lowerCamelCase_      =  True
      lowerCamelCase_      =  line.rsplit(" "			,  1     )
     else:
      lowerCamelCase_      =  False
     lowerCamelCase_      =  int(_UpperCAmelCase     )
     lowerCamelCase_      =  line
     if word in self and not overwrite:
      raise RuntimeError(
          "Duplicate word found when loading Dictionary: \'{}\'. "
          "Duplicate words can overwrite earlier ones by adding the "
          "#fairseq:overwrite flag at the end of the corresponding row "
          "in the dictionary file. If using the Camembert model, please "
          "download an updated copy of the model file.".format(_UpperCAmelCase     )     )
     self.add_symbol(_UpperCAmelCase			,  n=_UpperCAmelCase			,  overwrite=_UpperCAmelCase     )
    except ValueError:
     raise ValueError("Incorrect dictionary format, expected \'<token> <cnt> [flags]\'"     )





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
 # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
 lowerCamelCase_      =  dict((re.sub(r"@@$"					,				""					,				__snake_case		), v) if k.endswith("@@"		) else (re.sub(r"$"					,				"</w>"					,				__snake_case		), v) for k, v in d.items()		)
 lowerCamelCase_      =  '<s> <pad> </s> <unk>'.split()
 # restore the special tokens
 for k in keep_keys:
  del da[F'''{k}</w>''']
  lowerCamelCase_      =  d[k]  # restore
 return da





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[Any]		):
 # prep
 if not os.path.exists(__snake_case		):
  raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''		)
 os.makedirs(__snake_case					,				exist_ok=__snake_case		)
 print(F'''Writing results to {pytorch_dump_folder_path}'''		)

 # handle various types of models

 lowerCamelCase_      =  os.path.join(__snake_case					,				"checkpoint.pt"		)
 if not os.path.isfile(__snake_case		):
  raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''		)
 lowerCamelCase_      =  torch.load(__snake_case					,				map_location="cpu"		)

 lowerCamelCase_      =  chkpt['cfg']['model']

 # dicts
 lowerCamelCase_      =  os.path.join(__snake_case					,				"dict.txt"		)
 if not os.path.isfile(__snake_case		):
  raise ValueError(F'''path to the file {dict_file} does not exist!'''		)
 lowerCamelCase_      =  Dictionary.load(__snake_case		)
 lowerCamelCase_      =  rewrite_dict_keys(src_dict.indices		)
 lowerCamelCase_      =  len(__snake_case		)
 lowerCamelCase_      =  os.path.join(__snake_case					,				VOCAB_FILES_NAMES["vocab_file"]		)
 print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''		)
 with open(__snake_case					,				"w"					,				encoding="utf-8"		) as f:
  f.write(json.dumps(__snake_case					,				ensure_ascii=__snake_case					,				indent=__snake_case		)		)

 # merges_file (bpecodes)
 lowerCamelCase_      =  os.path.join(__snake_case					,				"bpecodes"		)
 if not os.path.isfile(__snake_case		):
  raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''		)

 lowerCamelCase_      =  os.path.join(__snake_case					,				VOCAB_FILES_NAMES["merges_file"]		)
 shutil.copyfile(__snake_case					,				__snake_case		)

 # model config
 lowerCamelCase_      =  os.path.join(__snake_case					,				"config.json"		)

 lowerCamelCase_      =  {
     'activation_dropout': args['activation_dropout'],
     'architectures': ['BioGptForCausalLM'],
     'attention_probs_dropout_prob': args['attention_dropout'],
     'bos_token_id': 0,
     'eos_token_id': 2,
     'hidden_act': args['activation_fn'],
     'hidden_dropout_prob': args['dropout'],
     'hidden_size': args['decoder_embed_dim'],
     'initializer_range': 0.02,
     'intermediate_size': args['decoder_ffn_embed_dim'],
     'layer_norm_eps': 1E-1_2,
     'layerdrop': args['decoder_layerdrop'],
     'max_position_embeddings': args['max_target_positions'],
     'model_type': 'biogpt',
     'num_attention_heads': args['decoder_attention_heads'],
     'num_hidden_layers': args['decoder_layers'],
     'pad_token_id': 1,
     'scale_embedding': not args['no_scale_embedding'],
     'tie_word_embeddings': args['share_decoder_input_output_embed'],
     'vocab_size': src_vocab_size,
 }

 # good hparam defaults to start with

 print(F'''Generating {biogpt_model_config_file}'''		)
 with open(__snake_case					,				"w"					,				encoding="utf-8"		) as f:
  f.write(json.dumps(__snake_case					,				ensure_ascii=__snake_case					,				indent=__snake_case		)		)

 # tokenizer config
 lowerCamelCase_      =  os.path.join(__snake_case					,				__snake_case		)

 lowerCamelCase_      =  {
     'bos_token': '<s>',
     'eos_token': '</s>',
     'model_max_length': 1024,
     'pad_token': '<pad>',
     'special_tokens_map_file': None,
     'tokenizer_class': 'BioGptTokenizer',
     'unk_token': '<unk>',
 }

 print(F'''Generating {biogpt_tokenizer_config_file}'''		)
 with open(__snake_case					,				"w"					,				encoding="utf-8"		) as f:
  f.write(json.dumps(__snake_case					,				ensure_ascii=__snake_case					,				indent=__snake_case		)		)

 # model
 lowerCamelCase_      =  chkpt['model']

 # remove unneeded keys
 lowerCamelCase_      =  [
     'decoder.version',
 ]
 for k in ignore_keys:
  model_state_dict.pop(__snake_case					,				__snake_case		)

 lowerCamelCase_      =  list(model_state_dict.keys()		)
 for layer_name in layer_names:
  if layer_name.endswith("output_projection.weight"		):
   lowerCamelCase_      =  model_state_dict.pop(__snake_case		)
  else:
   lowerCamelCase_      =  model_state_dict.pop(__snake_case		)

 lowerCamelCase_      =  BioGptConfig.from_pretrained(__snake_case		)
 lowerCamelCase_      =  BioGptForCausalLM(__snake_case		)

 # check that it loads ok
 model_new.load_state_dict(__snake_case		)

 # save
 lowerCamelCase_      =  os.path.join(__snake_case					,				__snake_case		)
 print(F'''Generating {pytorch_weights_dump_path}'''		)
 torch.save(__snake_case					,				__snake_case		)

 print("Conversion is done!"		)


if __name__ == "__main__":
     a_  :				List[str]						= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--biogpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help=(
             """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
             """ bpecodes, etc."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     a_  :				Optional[Any]						= parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
  lowercase__    :      List[Any]						=    pd.read_csv('''sample_data.csv''', header=None)
  lowercase__    :      Union[str, Any]						=    df.shape[:1][0]
  # If you're using some other dataset input the target column
  lowercase__    :      Any						=    df.iloc[:, 1:2]
  lowercase__    :      int						=    actual_data.values.reshape(len_data, 1)
  lowercase__    :      int						=    MinMaxScaler().fit_transform(actual_data)
  lowercase__    :      Dict						=    10
  lowercase__    :      List[str]						=    5
  lowercase__    :      Dict						=    20
  lowercase__    :      Dict						=    len_data - periods * look_back
  lowercase__    :      Any						=    actual_data[:division]
  lowercase__    :      Optional[int]						=    actual_data[division - look_back :]
  lowercase__	,							lowercase__    :      Optional[Any]						=    [], []
  lowercase__	,							lowercase__    :      Tuple						=    [], []

  for i in range(0, len(train_data) - forward_days - look_back + 1):
    train_x.append(train_data[i : i + look_back])
    train_y.append(train_data[i + look_back : i + look_back + forward_days])
  for i in range(0, len(test_data) - forward_days - look_back + 1):
    test_x.append(test_data[i : i + look_back])
    test_y.append(test_data[i + look_back : i + look_back + forward_days])
  lowercase__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)