






'''simple docstring'''







import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


_A     :      Optional[int]   					=Mapping[str, np.ndarray]
_A     :      int   					=Mapping[str, Any]  # Is a nested dict.
_A     :      Dict   					=0.01

@dataclasses.dataclass(frozen=lowercase__		)
class _lowercase     :
       a    				=	42  # [num_res, num_atom_type, 3]

       # Amino-acid type for each residue represented as an integer between 0 and
       # 20, where 20 is 'X'.
       a    				=	42  # [num_res]

       # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
       # is present and 0.0 if not. This should be used for loss masking.
       a    				=	42  # [num_res, num_atom_type]

       # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
       a    				=	42  # [num_res]

       # B-factors, or temperature factors, of each residue (in sq. angstroms units),
       # representing the displacement of the residue from its ground truth mean
       # value.
       a    				=	42  # [num_res, num_atom_type]

       # Chain indices for multi-chain predictions
       a    				=	None

       # Optional remark about the protein. Included as a comment in output PDB
       # files
       a    				=	None

       # Templates used to generate this protein (prediction-only)
       a    				=	None

       # Chain corresponding to each parent
       a    				=	None





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Tuple:
       lowerCamelCase__ :     Any	  =	R'(\[[A-Z]+\]\n)'
       lowerCamelCase__ :     List[str]	  =	[tag.strip() for tag in re.split(__a    ,      __a				) if len(__a				) > 0]
       lowerCamelCase__ :     Iterator[Tuple[str, List[str]]]	  =	zip(tags[0::2]    ,      [l.split("""\n"""				) for l in tags[1::2]]				)

       lowerCamelCase__ :     List[str]	  =	["N", "CA", "C"]
       lowerCamelCase__ :     int	  =	None
       lowerCamelCase__ :     List[str]	  =	None
       lowerCamelCase__ :     Optional[Any]	  =	None
       for g in groups:
              if "[PRIMARY]" == g[0]:
                     lowerCamelCase__ :     int	  =	g[1][0].strip()
                     for i in range(len(__a				)				):
                            if seq[i] not in residue_constants.restypes:
                                   lowerCamelCase__ :     Dict	  =	'X'  # FIXME: strings are immutable
                     lowerCamelCase__ :     Tuple	  =	np.array(
                         [residue_constants.restype_order.get(__a    ,      residue_constants.restype_num				) for res_symbol in seq]				)
              elif "[TERTIARY]" == g[0]:
                     lowerCamelCase__ :     List[List[float]]	  =	[]
                     for axis in range(3				):
                            tertiary.append(list(map(__a    ,      g[1][axis].split()				)				)				)
                     lowerCamelCase__ :     str	  =	np.array(__a				)
                     lowerCamelCase__ :     Optional[Any]	  =	np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
                     for i, atom in enumerate(__a				):
                            lowerCamelCase__ :     int	  =	np.transpose(tertiary_np[:, i::3]				)
                     atom_positions *= PICO_TO_ANGSTROM
              elif "[MASK]" == g[0]:
                     lowerCamelCase__ :     List[str]	  =	np.array(list(map({"""-""": 0, """+""": 1}.get    ,      g[1][0].strip()				)				)				)
                     lowerCamelCase__ :     Union[str, Any]	  =	np.zeros(
                         (
                             len(__a				),
                             residue_constants.atom_type_num,
                         )				).astype(np.floataa				)
                     for i, atom in enumerate(__a				):
                            lowerCamelCase__ :     str	  =	1
                     atom_mask *= mask[..., None]

       assert aatype is not None

       return Protein(
           atom_positions=__a    ,      atom_mask=__a    ,      aatype=__a    ,      residue_index=np.arange(len(__a				)				)    ,      b_factors=__a    ,      )





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = 0				)				->	Union[str, Any]:
       lowerCamelCase__ :     List[str]	  =	[]

       lowerCamelCase__ :     str	  =	prot.remark
       if remark is not None:
              pdb_headers.append(f'''REMARK {remark}'''				)

       lowerCamelCase__ :     Dict	  =	prot.parents
       lowerCamelCase__ :     Union[str, Any]	  =	prot.parents_chain_index
       if parents is not None and parents_chain_index is not None:
              lowerCamelCase__ :     List[Any]	  =	[p for i, p in zip(__a    ,      __a				) if i == chain_id]

       if parents is None or len(__a				) == 0:
              lowerCamelCase__ :     Dict	  =	['N/A']

       pdb_headers.append(f'''PARENT {' '.join(__a				)}'''				)

       return pdb_headers





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
       lowerCamelCase__ :     List[str]	  =	[]
       lowerCamelCase__ :     Any	  =	pdb_str.split("""\n"""				)

       lowerCamelCase__ :     List[str]	  =	prot.remark
       if remark is not None:
              out_pdb_lines.append(f'''REMARK {remark}'''				)

       lowerCamelCase__ :     List[List[str]]
       if prot.parents is not None and len(prot.parents				) > 0:
              lowerCamelCase__ :     Optional[int]	  =	[]
              if prot.parents_chain_index is not None:
                     lowerCamelCase__ :     Dict[str, List[str]]	  =	{}
                     for p, i in zip(prot.parents    ,      prot.parents_chain_index				):
                            parent_dict.setdefault(str(__a				)    ,      []				)
                            parent_dict[str(__a				)].append(__a				)

                     lowerCamelCase__ :     List[Any]	  =	max([int(__a				) for chain_idx in parent_dict]				)
                     for i in range(max_idx + 1				):
                            lowerCamelCase__ :     Union[str, Any]	  =	parent_dict.get(str(__a				)    ,      ["""N/A"""]				)
                            parents_per_chain.append(__a				)
              else:
                     parents_per_chain.append(list(prot.parents				)				)
       else:
              lowerCamelCase__ :     List[str]	  =	[['N/A']]

       def make_parent_line(UpperCamelCase				) -> str:
              return f'''PARENT {' '.join(__a				)}'''

       out_pdb_lines.append(make_parent_line(parents_per_chain[0]				)				)

       lowerCamelCase__ :     List[str]	  =	0
       for i, l in enumerate(__a				):
              if "PARENT" not in l and "REMARK" not in l:
                     out_pdb_lines.append(__a				)
              if "TER" in l and "END" not in lines[i + 1]:
                     chain_counter += 1
                     if not chain_counter >= len(__a				):
                            lowerCamelCase__ :     Optional[int]	  =	parents_per_chain[chain_counter]
                     else:
                            lowerCamelCase__ :     Tuple	  =	['N/A']

                     out_pdb_lines.append(make_parent_line(__a				)				)

       return "\n".join(__a				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
       lowerCamelCase__ :     List[Any]	  =	residue_constants.restypes + ['X']

       def res_atoa(UpperCamelCase				) -> str:
              return residue_constants.restype_atoa.get(restypes[r]    ,      """UNK"""				)

       lowerCamelCase__ :     Union[str, Any]	  =	residue_constants.atom_types

       lowerCamelCase__ :     List[str]	  =	[]

       lowerCamelCase__ :     List[Any]	  =	prot.atom_mask
       lowerCamelCase__ :     Union[str, Any]	  =	prot.aatype
       lowerCamelCase__ :     Tuple	  =	prot.atom_positions
       lowerCamelCase__ :     Tuple	  =	prot.residue_index.astype(np.intaa				)
       lowerCamelCase__ :     str	  =	prot.b_factors
       lowerCamelCase__ :     Union[str, Any]	  =	prot.chain_index

       if np.any(aatype > residue_constants.restype_num				):
              raise ValueError("""Invalid aatypes."""				)

       lowerCamelCase__ :     int	  =	get_pdb_headers(__a				)
       if len(__a				) > 0:
              pdb_lines.extend(__a				)

       lowerCamelCase__ :     Optional[int]	  =	aatype.shape[0]
       lowerCamelCase__ :     List[Any]	  =	1
       lowerCamelCase__ :     Optional[Any]	  =	0
       lowerCamelCase__ :     Optional[Any]	  =	string.ascii_uppercase
       lowerCamelCase__ :     Tuple	  =	None
       # Add all atom sites.
       for i in range(__a				):
              lowerCamelCase__ :     str	  =	res_atoa(aatype[i]				)
              for atom_name, pos, mask, b_factor in zip(__a    ,      atom_positions[i]    ,      atom_mask[i]    ,      b_factors[i]				):
                     if mask < 0.5:
                            continue

                     lowerCamelCase__ :     Dict	  =	'ATOM'
                     lowerCamelCase__ :     str	  =	atom_name if len(__a				) == 4 else f''' {atom_name}'''
                     lowerCamelCase__ :     Any	  =	''
                     lowerCamelCase__ :     Optional[Any]	  =	''
                     lowerCamelCase__ :     List[str]	  =	1.00
                     lowerCamelCase__ :     Any	  =	atom_name[0]  # Protein supports only C, N, O, S, this works.
                     lowerCamelCase__ :     List[str]	  =	''

                     lowerCamelCase__ :     Optional[Any]	  =	'A'
                     if chain_index is not None:
                            lowerCamelCase__ :     List[str]	  =	chain_tags[chain_index[i]]

                     # PDB is a columnar format, every space matters here!
                     lowerCamelCase__ :     int	  =	(
                         f'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
                         f'''{res_name_a:>3} {chain_tag:>1}'''
                         f'''{residue_index[i]:>4}{insertion_code:>1}   '''
                         f'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
                         f'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
                         f'''{element:>2}{charge:>2}'''
                     )
                     pdb_lines.append(__a				)
                     atom_index += 1

              lowerCamelCase__ :     Optional[int]	  =	i == n - 1
              if chain_index is not None:
                     if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                            lowerCamelCase__ :     List[Any]	  =	True
                            lowerCamelCase__ :     Union[str, Any]	  =	chain_index[i + 1]

              if should_terminate:
                     # Close the chain.
                     lowerCamelCase__ :     Tuple	  =	'TER'
                     lowerCamelCase__ :     List[str]	  =	(
                         f'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]				):>3} {chain_tag:>1}{residue_index[i]:>4}'''
                     )
                     pdb_lines.append(__a				)
                     atom_index += 1

                     if i != n - 1:
                            # "prev" is a misnomer here. This happens at the beginning of
                            # each new chain.
                            pdb_lines.extend(get_pdb_headers(__a    ,      __a				)				)

       pdb_lines.append("""END"""				)
       pdb_lines.append(""""""				)
       return "\n".join(__a				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[int]:
       return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      UpperCamelCase = None    ,      )				->	Optional[int]:
       return Protein(
           aatype=features["""aatype"""]    ,      atom_positions=result["""final_atom_positions"""]    ,      atom_mask=result["""final_atom_mask"""]    ,      residue_index=features["""residue_index"""] + 1    ,      b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]				)    ,      chain_index=__a    ,      remark=__a    ,      parents=__a    ,      parents_chain_index=__a    ,      )









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

			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])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

			_A     :      Optional[Any]   					=Sequential()
			model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
			model.add(LSTM(64, input_shape=(128, 1)))
			model.add(Dense(forward_days))
			model.compile(loss='''mean_squared_error''', optimizer='''adam''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

