





'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
  from transformers import (
      AutoConfig,
      BertConfig,
      GPTaConfig,
      TaConfig,
      TFAutoModel,
      TFAutoModelForCausalLM,
      TFAutoModelForMaskedLM,
      TFAutoModelForPreTraining,
      TFAutoModelForQuestionAnswering,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSequenceClassification,
      TFAutoModelWithLMHead,
      TFBertForMaskedLM,
      TFBertForPreTraining,
      TFBertForQuestionAnswering,
      TFBertForSequenceClassification,
      TFBertModel,
      TFGPTaLMHeadModel,
      TFRobertaForMaskedLM,
      TFTaForConditionalGeneration,
  )
  from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
  from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
  from transformers import (
      AutoModel,
      AutoModelForCausalLM,
      AutoModelForMaskedLM,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelWithLMHead,
      BertForMaskedLM,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertModel,
      GPTaLMHeadModel,
      RobertaForMaskedLM,
      TaForConditionalGeneration,
  )







@is_pt_tf_cross_test
class   A_	(  unittest.TestCase			):




 @slow
 def      lowercase	(					self       :    List[Any]    ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModel.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModel.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    str    ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForPreTraining.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForPreTraining.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    List[str]    ):
      for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForCausalLM.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   TFAutoModelForCausalLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForCausalLM.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   AutoModelForCausalLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    Union[str, Any]    ):
      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelWithLMHead.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelWithLMHead.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    Any    ):
      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForMaskedLM.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   TFAutoModelForMaskedLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForMaskedLM.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   AutoModelForMaskedLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    int    ):
      for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForSeqaSeqLM.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   TFAutoModelForSeqaSeqLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForSeqaSeqLM.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   AutoModelForSeqaSeqLM.from_pretrained(
               snake_case_	,			output_loading_info=snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    Tuple    ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForSequenceClassification.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForSequenceClassification.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 @slow
 def      lowercase	(					self       :    Tuple    ):
      # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      for model_name in ["bert-base-uncased"]:
           _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   TFAutoModelForQuestionAnswering.from_pretrained(snake_case_	,			from_pt=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )

           _UpperCAmelCase     							=   AutoModelForQuestionAnswering.from_pretrained(snake_case_	,			from_tf=snake_case_    )
           self.assertIsNotNone(snake_case_    )
           self.assertIsInstance(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   TFAutoModelWithLMHead.from_pretrained(snake_case_	,			from_pt=snake_case_    )
      self.assertIsInstance(snake_case_	,			snake_case_    )
      self.assertEqual(model.num_parameters()	,			1_4_4_1_0    )
      self.assertEqual(model.num_parameters(only_trainable=snake_case_    )	,			1_4_4_1_0    )

      _UpperCAmelCase     							=   AutoModelWithLMHead.from_pretrained(snake_case_	,			from_tf=snake_case_    )
      self.assertIsInstance(snake_case_	,			snake_case_    )
      self.assertEqual(model.num_parameters()	,			1_4_4_1_0    )
      self.assertEqual(model.num_parameters(only_trainable=snake_case_    )	,			1_4_4_1_0    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   TFAutoModelWithLMHead.from_pretrained(snake_case_	,			from_pt=snake_case_    )
      self.assertIsInstance(snake_case_	,			snake_case_    )
      self.assertEqual(model.num_parameters()	,			1_4_4_1_0    )
      self.assertEqual(model.num_parameters(only_trainable=snake_case_    )	,			1_4_4_1_0    )

      _UpperCAmelCase     							=   AutoModelWithLMHead.from_pretrained(snake_case_	,			from_tf=snake_case_    )
      self.assertIsInstance(snake_case_	,			snake_case_    )
      self.assertEqual(model.num_parameters()	,			1_4_4_1_0    )
      self.assertEqual(model.num_parameters(only_trainable=snake_case_    )	,			1_4_4_1_0    )









"""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)
