




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a__		     =     logging.getLogger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Any					,     lowerCAmelCase		:						int=-1	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      label_idx






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]					,     lowerCAmelCase		:						Union[Split, str]	)	->       List[InputExample]:







						"""simple docstring"""







						if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											__UpperCamelCase					:	Optional[Any]             =      mode.value
						__UpperCamelCase					:	int             =      os.path.join(lowerCAmelCase					,     F'''{mode}.txt'''	)
						__UpperCamelCase					:	List[str]             =      1
						__UpperCamelCase					:	Dict             =      []
						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as f:
											__UpperCamelCase					:	Any             =      []
											__UpperCamelCase					:	int             =      []
											for line in f:
																if line.startswith("""-DOCSTART-"""	) or line == "" or line == "\n":
																					if words:
																										examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,     words=lowerCAmelCase					,     labels=lowerCAmelCase	)	)
																										guid_index += 1
																										__UpperCamelCase					:	int             =      []
																										__UpperCamelCase					:	Optional[int]             =      []
																else:
																					__UpperCamelCase					:	Union[str, Any]             =      line.split(""" """	)
																					words.append(splits[0]	)
																					if len(lowerCAmelCase	) > 1:
																										labels.append(splits[self.label_idx].replace("""\n"""					,     """"""	)	)
																					else:
																										# Examples could have no label for mode = "test"
																										labels.append("""O"""	)
											if words:
																examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,     words=lowerCAmelCase					,     labels=lowerCAmelCase	)	)
						return examples






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						TextIO					,     lowerCAmelCase		:						TextIO					,     lowerCAmelCase		:						List	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      0
						for line in test_input_reader:
											if line.startswith("""-DOCSTART-"""	) or line == "" or line == "\n":
																writer.write(lowerCAmelCase	)
																if not preds_list[example_id]:
																					example_id += 1
											elif preds_list[example_id]:
																__UpperCamelCase					:	List[Any]             =      line.split()[0] + """ """ + preds_list[example_id].pop(0	) + """\n"""
																writer.write(lowerCAmelCase	)
											else:
																logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""					,     line.split()[0]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						if path:
											with open(lowerCAmelCase					,     """r"""	) as f:
																__UpperCamelCase					:	Optional[int]             =      f.read().splitlines()
											if "O" not in labels:
																__UpperCamelCase					:	Any             =      ["""O"""] + labels
											return labels
						else:
											return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Any	)	->       List[str]:







						"""simple docstring"""







						super().__init__(label_idx=-2	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						if path:
											with open(lowerCAmelCase					,     """r"""	) as f:
																__UpperCamelCase					:	str             =      f.read().splitlines()
											if "O" not in labels:
																__UpperCamelCase					:	int             =      ["""O"""] + labels
											return labels
						else:
											return [
											    "O",
											    "B-ADVP",
											    "B-INTJ",
											    "B-LST",
											    "B-PRT",
											    "B-NP",
											    "B-SBAR",
											    "B-VP",
											    "B-ADJP",
											    "B-CONJP",
											    "B-PP",
											    "I-ADVP",
											    "I-INTJ",
											    "I-LST",
											    "I-PRT",
											    "I-NP",
											    "I-SBAR",
											    "I-VP",
											    "I-ADJP",
											    "I-CONJP",
											    "I-PP",
											]






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Union[Split, str]	)	->       List[InputExample]:







						"""simple docstring"""







						if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											__UpperCamelCase					:	Optional[int]             =      mode.value
						__UpperCamelCase					:	Union[str, Any]             =      os.path.join(lowerCAmelCase					,     F'''{mode}.txt'''	)
						__UpperCamelCase					:	Optional[Any]             =      1
						__UpperCamelCase					:	Any             =      []

						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as f:
											for sentence in parse_incr(lowerCAmelCase	):
																__UpperCamelCase					:	List[str]             =      []
																__UpperCamelCase					:	Any             =      []
																for token in sentence:
																					words.append(token["""form"""]	)
																					labels.append(token["""upos"""]	)
																assert len(lowerCAmelCase	) == len(lowerCAmelCase	)
																if words:
																					examples.append(InputExample(guid=F'''{mode}-{guid_index}'''					,     words=lowerCAmelCase					,     labels=lowerCAmelCase	)	)
																					guid_index += 1
						return examples






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						TextIO					,     lowerCAmelCase		:						TextIO					,     lowerCAmelCase		:						List	)	->       Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      0
						for sentence in parse_incr(lowerCAmelCase	):
											__UpperCamelCase					:	Optional[int]             =      preds_list[example_id]
											__UpperCamelCase					:	Any             =      """"""
											for token in sentence:
																out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0	)}) '''
											out += "\n"
											writer.write(lowerCAmelCase	)
											example_id += 1






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						if path:
											with open(lowerCAmelCase					,     """r"""	) as f:
																return f.read().splitlines()
						else:
											return [
											    "ADJ",
											    "ADP",
											    "ADV",
											    "AUX",
											    "CCONJ",
											    "DET",
											    "INTJ",
											    "NOUN",
											    "NUM",
											    "PART",
											    "PRON",
											    "PROPN",
											    "PUNCT",
											    "SCONJ",
											    "SYM",
											    "VERB",
											    "X",
											]












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

	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__		     =     np.array(train_x)
	a__		     =     np.array(test_x)
	a__		     =     np.array([list(i.ravel()) for i in train_y])
	a__		     =     np.array([list(i.ravel()) for i in test_y])

	a__		     =     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__		     =     model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	a__		     =     model.predict(x_test)






