
"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy
  import tensorflow as tf

  from transformers import (
      TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BertConfig,
      DPRConfig,
      TFDPRContextEncoder,
      TFDPRQuestionEncoder,
      TFDPRReader,
  )


class       __magic_name__							:
       def __init__(  self			,					A_			,					A_=13			,					A_=7			,					A_=True			,					A_=True			,					A_=True			,					A_=True			,					A_=99			,					A_=32			,					A_=2			,					A_=4			,					A_=37			,					A_="gelu"			,					A_=0.1			,					A_=0.1			,					A_=512			,					A_=16			,					A_=2			,					A_=0.02			,					A_=3			,					A_=4			,					A_=None			,					A_=0			,					)  ->						str:





         """simple docstring"""




         _lowercase:      List[Any]   			=  parent
         _lowercase:      int   			=  batch_size
         _lowercase:      str   			=  seq_length
         _lowercase:      List[Any]   			=  is_training
         _lowercase:      Any   			=  use_input_mask
         _lowercase:      Optional[Any]   			=  use_token_type_ids
         _lowercase:      Any   			=  use_labels
         _lowercase:      int   			=  vocab_size
         _lowercase:      Optional[Any]   			=  hidden_size
         _lowercase:      Dict   			=  num_hidden_layers
         _lowercase:      Tuple   			=  num_attention_heads
         _lowercase:      int   			=  intermediate_size
         _lowercase:      List[str]   			=  hidden_act
         _lowercase:      str   			=  hidden_dropout_prob
         _lowercase:      int   			=  attention_probs_dropout_prob
         _lowercase:      Tuple   			=  max_position_embeddings
         _lowercase:      Optional[Any]   			=  type_vocab_size
         _lowercase:      Dict   			=  type_sequence_label_size
         _lowercase:      Optional[int]   			=  initializer_range
         _lowercase:      Dict   			=  num_labels
         _lowercase:      Optional[Any]   			=  num_choices
         _lowercase:      List[str]   			=  scope
         _lowercase:      Any   			=  projection_dim
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      str   			=  ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size							)

         _lowercase:      Optional[int]   			=  None
         if self.use_input_mask:
           # follow test_modeling_tf_ctrl.py
           _lowercase:      Any   			=  random_attention_mask([self.batch_size, self.seq_length]							)

         _lowercase:      List[Any]   			=  None
         if self.use_token_type_ids:
           _lowercase:      List[Any]   			=  ids_tensor([self.batch_size, self.seq_length]			,					self.type_vocab_size							)

         _lowercase:      int   			=  None
         _lowercase:      Tuple   			=  None
         _lowercase:      str   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)
           _lowercase:      Any   			=  ids_tensor([self.batch_size, self.seq_length]			,					self.num_labels							)
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.num_choices							)

         _lowercase:      List[Any]   			=  BertConfig(
             vocab_size=self.vocab_size			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					max_position_embeddings=self.max_position_embeddings			,					type_vocab_size=self.type_vocab_size			,					is_decoder=__lowerCAmelCase			,					initializer_range=self.initializer_range			,					)
         _lowercase:      Optional[Any]   			=  DPRConfig(projection_dim=self.projection_dim			,					**config.to_dict()							)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  TFDPRContextEncoder(config=__lowerCAmelCase							)
         _lowercase:      Optional[int]   			=  model(__lowerCAmelCase			,					attention_mask=__lowerCAmelCase			,					token_type_ids=__lowerCAmelCase							)
         _lowercase:      Union[str, Any]   			=  model(__lowerCAmelCase			,					token_type_ids=__lowerCAmelCase							)
         _lowercase:      Dict   			=  model(__lowerCAmelCase							)
         self.parent.assertEqual(result.pooler_output.shape			,					(self.batch_size, self.projection_dim or self.hidden_size)							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  TFDPRQuestionEncoder(config=__lowerCAmelCase							)
         _lowercase:      Optional[int]   			=  model(__lowerCAmelCase			,					attention_mask=__lowerCAmelCase			,					token_type_ids=__lowerCAmelCase							)
         _lowercase:      List[str]   			=  model(__lowerCAmelCase			,					token_type_ids=__lowerCAmelCase							)
         _lowercase:      Tuple   			=  model(__lowerCAmelCase							)
         self.parent.assertEqual(result.pooler_output.shape			,					(self.batch_size, self.projection_dim or self.hidden_size)							)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      str   			=  TFDPRReader(config=__lowerCAmelCase							)
         _lowercase:      Optional[Any]   			=  model(__lowerCAmelCase			,					attention_mask=__lowerCAmelCase							)

         self.parent.assertEqual(result.start_logits.shape			,					(self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.end_logits.shape			,					(self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.relevance_logits.shape			,					(self.batch_size,)							)



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.prepare_config_and_inputs()
         (
             (
             _lowercase				
         )			,					(
             _lowercase				
         )			,					(
             _lowercase				
         )			,					(
             _lowercase				
         )			,					(
             _lowercase				
         )			,					(
             _lowercase				
         )			,					(
             _lowercase				
         )			,									
         ):      Tuple   			=  config_and_inputs
         _lowercase:      Optional[Any]   			=  {'''input_ids''': input_ids}
         return config, inputs_dict


@require_tf
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(
           (
               TFDPRContextEncoder,
               TFDPRQuestionEncoder,
               TFDPRReader,
           )
           if is_tf_available()
           else ()
       )
       UpperCamelCase_ =		{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  TFDPRModelTester(self							)
         _lowercase:      Union[str, Any]   			=  ConfigTester(self			,					config_class=__lowerCAmelCase			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         self.config_tester.run_common_tests()
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*__lowerCAmelCase							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*__lowerCAmelCase							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*__lowerCAmelCase							)



       @slow
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      Optional[int]   			=  TFDPRContextEncoder.from_pretrained(__lowerCAmelCase							)
           self.assertIsNotNone(__lowerCAmelCase							)

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      List[Any]   			=  TFDPRContextEncoder.from_pretrained(__lowerCAmelCase							)
           self.assertIsNotNone(__lowerCAmelCase							)

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      List[str]   			=  TFDPRQuestionEncoder.from_pretrained(__lowerCAmelCase							)
           self.assertIsNotNone(__lowerCAmelCase							)

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      List[Any]   			=  TFDPRReader.from_pretrained(__lowerCAmelCase							)
           self.assertIsNotNone(__lowerCAmelCase							)


@require_tf
class       __magic_name__							(							unittest.TestCase			):
       @slow
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''							)

         _lowercase:      Optional[Any]   			=  tf.constant(
             [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]							)  # [CLS] hello, is my dog cute? [SEP]
         _lowercase:      Optional[int]   			=  model(__lowerCAmelCase							)[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         _lowercase:      List[Any]   			=  tf.constant(
             [
                 [
                     0.03_23_62_53,
                     0.12_75_33_35,
                     0.16_81_85_09,
                     0.00_27_97_86,
                     0.3_89_69_33,
                     0.24_26_49_45,
                     0.2_17_89_71,
                     -0.02_33_52_27,
                     -0.08_48_19_59,
                     -0.14_32_41_17,
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :10].numpy()			,					expected_slice.numpy()			,					atol=1E-4							)							)

"""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__":
       __lowerCAmelCase       :			int   	=pd.read_csv("""sample_data.csv""", header=None)
       __lowerCAmelCase       :			Optional[Any]   	=df.shape[:1][0]
       # If you're using some other dataset input the target column
       __lowerCAmelCase       :			Optional[int]   	=df.iloc[:, 1:2]
       __lowerCAmelCase       :			List[str]   	=actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase       :			int   	=MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase       :			List[Any]   	=1_0
       __lowerCAmelCase       :			int   	=5
       __lowerCAmelCase       :			str   	=2_0
       __lowerCAmelCase       :			Union[str, Any]   	=len_data - periods * look_back
       __lowerCAmelCase       :			Dict   	=actual_data[:division]
       __lowerCAmelCase       :			List[str]   	=actual_data[division - look_back :]
       __lowerCAmelCase	,	__lowerCAmelCase       :			List[str]   	=[], []
       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=[], []

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

       __lowerCAmelCase       :			Optional[Any]   	=Sequential()
       model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
       model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
       model.add(Dense(forward_days))
       model.compile(loss="""mean_squared_error""", optimizer="""adam""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



