



"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__a      =				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a      =				{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__a      =				{
    "unc-nlp/lxmert-base-uncased": 5_12,
}

__a      =				{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			str						=     VOCAB_FILES_NAMES
					_A		:			int						=     PRETRAINED_VOCAB_FILES_MAP
					_A		:			Dict						=     PRETRAINED_INIT_CONFIGURATION
					_A		:			List[str]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A		:			Dict						=     LxmertTokenizer






					def __init__(       self:							str						,     snake_case:							Optional[int]=None						,     snake_case:							Dict=None						,     snake_case:							Union[str, Any]=True						,     snake_case:							str="[UNK]"						,     snake_case:							List[str]="[SEP]"						,     snake_case:							Dict="[PAD]"						,     snake_case:							Dict="[CLS]"						,     snake_case:							Union[str, Any]="[MASK]"						,     snake_case:							Union[str, Any]=True						,     snake_case:							Tuple=None						,     **snake_case:							Optional[Any]						,     )   ->	Tuple:
											super().__init__(
											    snake_case						,     tokenizer_file=snake_case						,     do_lower_case=snake_case						,     unk_token=snake_case						,     sep_token=snake_case						,     pad_token=snake_case						,     cls_token=snake_case						,     mask_token=snake_case						,     tokenize_chinese_chars=snake_case						,     strip_accents=snake_case						,     **snake_case						,     )

											snake_case_ :Union[str, Any]						=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
											if (
											    normalizer_state.get("""lowercase"""						,     snake_case						) != do_lower_case
											    or normalizer_state.get("""strip_accents"""						,     snake_case						) != strip_accents
											    or normalizer_state.get("""handle_chinese_chars"""						,     snake_case						) != tokenize_chinese_chars
											):
																	snake_case_ :List[Any]						=			getattr(snake_case						,     normalizer_state.pop("""type"""						)						)
																	snake_case_ :Union[str, Any]						=			do_lower_case
																	snake_case_ :Optional[int]						=			strip_accents
																	snake_case_ :int						=			tokenize_chinese_chars
																	snake_case_ :str						=			normalizer_class(**snake_case						)

											snake_case_ :List[str]						=			do_lower_case






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Optional[Any]						,     snake_case:							Union[str, Any]=None						)   ->	List[Any]:
											snake_case_ :Dict						=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

											return output






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						)   ->	List[int]:
											snake_case_ :int						=			[self.sep_token_id]
											snake_case_ :List[str]						=			[self.cls_token_id]
											if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
											return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						,     snake_case:							Optional[str] = None						)   ->	Tuple[str]:
											snake_case_ :List[str]						=			self._tokenizer.model.save(snake_case						,     name=snake_case						)
											return tuple(snake_case						)





"""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      =				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(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")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
