


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp

@slow
@require_flax
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									return f'''gaussian_noise_s={seed}_shape={"_".join([str(_SCREAMING_SNAKE_CASE						) for s in shape]						)}.npy'''
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=(4, 4, 64, 64)      ,   _SCREAMING_SNAKE_CASE=False						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase					=      jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)      ,   dtype=_SCREAMING_SNAKE_CASE						)
									return image
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE="CompVis/stable-diffusion-v1-4"						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase					=      'bf16' if fpaa else None

									_UpperCAmelCase		,	_UpperCAmelCase					=      FlaxUNetaDConditionModel.from_pretrained(
									    _SCREAMING_SNAKE_CASE      ,   subfolder='unet'      ,   dtype=_SCREAMING_SNAKE_CASE      ,   revision=_SCREAMING_SNAKE_CASE						)
									return model, params
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=(4, 77, 768)      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      jnp.bfloataa if fpaa else jnp.floataa
									_UpperCAmelCase					=      jnp.array(load_hf_numpy(self.get_file_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)      ,   dtype=_SCREAMING_SNAKE_CASE						)
									return hidden_states
		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
		        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
		        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
		        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
		        # fmt: on
		    ]						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,   fpaa=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.get_latents(_SCREAMING_SNAKE_CASE      ,   fpaa=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE      ,   fpaa=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      model.apply(
									    {'params': params}      ,   _SCREAMING_SNAKE_CASE      ,   jnp.array(_SCREAMING_SNAKE_CASE      ,   dtype=jnp.intaa						)      ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,   ).sample

									assert sample.shape == latents.shape

									_UpperCAmelCase					=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)      ,   dtype=jnp.floataa						)
									_UpperCAmelCase					=      jnp.array(_SCREAMING_SNAKE_CASE      ,   dtype=jnp.floataa						)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-2						)






		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
		        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
		        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
		        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
		        # fmt: on
		    ]						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,   fpaa=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.get_latents(_SCREAMING_SNAKE_CASE      ,   shape=(4, 4, 96, 96)      ,   fpaa=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.get_encoder_hidden_states(_SCREAMING_SNAKE_CASE      ,   shape=(4, 77, 1024)      ,   fpaa=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      model.apply(
									    {'params': params}      ,   _SCREAMING_SNAKE_CASE      ,   jnp.array(_SCREAMING_SNAKE_CASE      ,   dtype=jnp.intaa						)      ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,   ).sample

									assert sample.shape == latents.shape

									_UpperCAmelCase					=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)      ,   dtype=jnp.floataa						)
									_UpperCAmelCase					=      jnp.array(_SCREAMING_SNAKE_CASE      ,   dtype=jnp.floataa						)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-2						)




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

		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__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							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__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
