




'''simple docstring'''

import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


a       :						int								=		logging.get_logger(__name__)


a       :						Any								=		r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    int      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						raise NotImplementedError("StoppingCriteria needs to be subclassed"							)







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Dict      ,		a_						:    int      ,		a_						:    Optional[int] = None							):



						"""simple docstring"""




						__snake_case			=		max_length
						__snake_case			=		max_position_embeddings






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		input_ids.shape[-1]
						__snake_case			=		cur_len >= self.max_length
						if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
									logger.warning_once(
									    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
									    f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
									    "exceptions, performance degradation, or nothing at all."							)
						return is_done







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    List[str]      ,		a_						:    int      ,		a_						:    int							):



						"""simple docstring"""




						warnings.warn(
						    "The class `MaxNewTokensCriteria` is deprecated. "
						    f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
						    "with `max_length = start_length + max_new_tokens` instead."      ,		a_      ,		)
						__snake_case			=		start_length
						__snake_case			=		max_new_tokens
						__snake_case			=		start_length + max_new_tokens






			@add_start_docstrings(a_							)
			def __call__(      self						:    Union[str, Any]      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						return input_ids.shape[-1] >= self.max_length







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Tuple      ,		a_						:    float      ,		a_						:    Optional[float] = None							):



						"""simple docstring"""




						__snake_case			=		max_time
						__snake_case			=		time.time() if initial_timestamp is None else initial_timestamp






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    str							):



						"""simple docstring"""




						return time.time() - self.initial_timestamp > self.max_time







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    Tuple      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						return any(criteria(a_      ,		a_							) for criteria in self							)






			@property
			def 							A (      self						:    Any							):



						"""simple docstring"""




						for stopping_criterium in self:
									if isinstance(a_      ,		a_							):
												return stopping_criterium.max_length
									elif isinstance(a_      ,		a_							):
												return stopping_criterium.max_length
						return None







def __UpperCAmelCase	(   _UpperCAmelCase		:   StoppingCriteriaList		,				_UpperCAmelCase		:   int						)      ->      StoppingCriteriaList:
			__snake_case			=		stopping_criteria.max_length
			__snake_case			=		deepcopy(_UpperCAmelCase						)
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"		,				_UpperCAmelCase						)
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCAmelCase						)						)
			return new_stopping_criteria











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)






