"""simple docstring"""






from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Optional[int]=1e-12						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      jnp.divide(emb_a.T   ,						jnp.clip(jnp.linalg.norm(lowerCAmelCase__   ,						axis=1						)   ,						a_min=lowerCAmelCase__						)						).T
      lowercase								=      jnp.divide(emb_a.T   ,						jnp.clip(jnp.linalg.norm(lowerCAmelCase__   ,						axis=1						)   ,						a_min=lowerCAmelCase__						)						).T
      return jnp.matmul(lowerCAmelCase__   ,						norm_emb_a.T						)





class 					_A		(			nn.Module      ):
      snake_case__     :  CLIPConfig
      snake_case__     :  jnp.dtype				    =      jnp.floataa

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      FlaxCLIPVisionModule(self.config.vision_config		)
            lowercase								=      nn.Dense(self.config.projection_dim						,			use_bias=__lowerCAmelCase						,			dtype=self.dtype		)

            lowercase								=      self.param("""concept_embeds"""						,			jax.nn.initializers.ones						,			(17, self.config.projection_dim)		)
            lowercase								=      self.param(
                """special_care_embeds"""						,			jax.nn.initializers.ones						,			(3, self.config.projection_dim)		)

            lowercase								=      self.param("""concept_embeds_weights"""						,			jax.nn.initializers.ones						,			(17,)		)
            lowercase								=      self.param("""special_care_embeds_weights"""						,			jax.nn.initializers.ones						,			(3,)		)







      def __call__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.vision_model(__lowerCAmelCase		)[1]
            lowercase								=      self.visual_projection(__lowerCAmelCase		)

            lowercase								=      jax_cosine_distance(__lowerCAmelCase						,			self.special_care_embeds		)
            lowercase								=      jax_cosine_distance(__lowerCAmelCase						,			self.concept_embeds		)

            # increase this value to create a stronger `nfsw` filter
            # at the cost of increasing the possibility of filtering benign image inputs
            lowercase								=      0.0

            lowercase								=      special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
            lowercase								=      jnp.round(__lowerCAmelCase						,			3		)
            lowercase								=      jnp.any(special_scores > 0						,			axis=1						,			keepdims=__lowerCAmelCase		)
            # Use a lower threshold if an image has any special care concept
            lowercase								=      is_special_care * 0.0_1

            lowercase								=      cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
            lowercase								=      jnp.round(__lowerCAmelCase						,			3		)
            lowercase								=      jnp.any(concept_scores > 0						,			axis=1		)

            return has_nsfw_concepts






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Any				    =      CLIPConfig
      snake_case__     :  Dict				    =      'clip_input'
      snake_case__     :  Any				    =      FlaxStableDiffusionSafetyCheckerModule

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 0						,			__lowerCAmelCase = jnp.floataa						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if input_shape is None:
                  lowercase								=      (1, 224, 224, 3)
            lowercase								=      self.module_class(config=__lowerCAmelCase						,			dtype=__lowerCAmelCase						,			**__lowerCAmelCase		)
            super().__init__(__lowerCAmelCase						,			__lowerCAmelCase						,			input_shape=__lowerCAmelCase						,			seed=__lowerCAmelCase						,			dtype=__lowerCAmelCase						,			_do_init=_do_init		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      jax.random.normal(__lowerCAmelCase						,			__lowerCAmelCase		)

            lowercase				,				lowercase								=      jax.random.split(__lowerCAmelCase		)
            lowercase								=      {"""params""": params_rng, """dropout""": dropout_rng}

            lowercase								=      self.module.init(__lowerCAmelCase						,			__lowerCAmelCase		)["""params"""]

            return random_params







      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			):







            """simple docstring"""



            lowercase								=      jnp.transpose(__lowerCAmelCase						,			(0, 2, 3, 1)		)

            return self.module.apply(
                {"""params""": params or self.params}						,			jnp.array(__lowerCAmelCase						,			dtype=jnp.floataa		)						,			rngs={}						,			)




"""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)



