




import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax

						from transformers.models.big_bird.modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						)







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def __init__(			self      ,snake_case__      ,snake_case__=2      ,snake_case__=56      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=2      ,snake_case__=7      ,snake_case__="gelu_new"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=16      ,snake_case__=2      ,snake_case__=0.02      ,snake_case__=4      ,snake_case__="block_sparse"      ,snake_case__=True      ,snake_case__=False      ,snake_case__=2      ,snake_case__=3      ,):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      parent
							SCREAMING_SNAKE_CASE_   : int					  =      batch_size
							SCREAMING_SNAKE_CASE_   : str					  =      seq_length
							SCREAMING_SNAKE_CASE_   : Tuple					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_attention_mask
							SCREAMING_SNAKE_CASE_   : int					  =      use_token_type_ids
							SCREAMING_SNAKE_CASE_   : int					  =      use_labels
							SCREAMING_SNAKE_CASE_   : str					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : str					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      type_sequence_label_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_choices

							SCREAMING_SNAKE_CASE_   : Any					  =      rescale_embeddings
							SCREAMING_SNAKE_CASE_   : int					  =      attention_type
							SCREAMING_SNAKE_CASE_   : Any					  =      use_bias
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      block_size
							SCREAMING_SNAKE_CASE_   : Dict					  =      num_random_blocks






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )

							SCREAMING_SNAKE_CASE_   : int					  =      None
							if self.use_attention_mask:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      random_attention_mask([self.batch_size, self.seq_length] )

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

							SCREAMING_SNAKE_CASE_   : int					  =      BigBirdConfig(
							    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=snake_case__      ,initializer_range=self.initializer_range      ,attention_type=self.attention_type      ,block_size=self.block_size      ,num_random_blocks=self.num_random_blocks      ,use_bias=self.use_bias      ,rescale_embeddings=self.rescale_embeddings      ,)

							return config, input_ids, token_type_ids, attention_mask






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      config_and_inputs
							SCREAMING_SNAKE_CASE_   : int					  =      {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'attention_mask': attention_mask,
							}
							return config, inputs_dict







@require_flax
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       int						=     (
				    (
				        FlaxBigBirdForCausalLM,
				        FlaxBigBirdModel,
				        FlaxBigBirdForPreTraining,
				        FlaxBigBirdForMaskedLM,
				        FlaxBigBirdForMultipleChoice,
				        FlaxBigBirdForQuestionAnswering,
				        FlaxBigBirdForSequenceClassification,
				        FlaxBigBirdForTokenClassification,
				    )
				    if is_flax_available()
				    else ()
				)

				__a     :       List[Any]						=     False
				__a     :       Any						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      FlaxBigBirdModelTester(self )






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_save_pretrained()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_from_pretrained_with_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_no_automatic_init()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							super().test_hidden_states_output()






				@slow
				def 		snake_case	(			self ):
							for model_class_name in self.all_model_classes:
										SCREAMING_SNAKE_CASE_   : Dict					  =      model_class_name.from_pretrained('google/bigbird-roberta-base' )
										self.assertIsNotNone(snake_case__ )






				def 		snake_case	(			self ):
							if self.test_attn_probs:
										super().test_attention_outputs()






				@slow
				# copied from `test_modeling_flax_common` because it takes much longer than other models
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__ ):
													SCREAMING_SNAKE_CASE_   : str					  =      self._prepare_for_class(snake_case__      ,snake_case__ )
													SCREAMING_SNAKE_CASE_   : Dict					  =      model_class(snake_case__ )

													@jax.jit
													def model_jitted(snake_case__      ,snake_case__=None      ,**snake_case__ ):
																return model(input_ids=snake_case__      ,attention_mask=snake_case__      ,**snake_case__ )

													with self.subTest('JIT Enabled' ):
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model_jitted(**snake_case__ ).to_tuple()

													with self.subTest('JIT Disabled' ):
																with jax.disable_jit():
																			SCREAMING_SNAKE_CASE_   : Any					  =      model_jitted(**snake_case__ ).to_tuple()

													self.assertEqual(len(snake_case__ )      ,len(snake_case__ ) )
													for jitted_output, output in zip(snake_case__      ,snake_case__ ):
																self.assertEqual(jitted_output.shape      ,output.shape )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=1E-5      ,snake_case__="outputs"      ,snake_case__=None ):
							# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
							# an effort was done to return `attention_probs` (yet to be verified).
							if name.startswith('outputs.attentions' ):
										return
							else:
										super().check_pt_flax_outputs(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,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__":
       __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)



