from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)



@flax.struct.dataclass
class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	jnp.ndarray
  SCREAMING_SNAKE_CASE_	:	jnp.ndarray



class     _A   (    nn.Module							):
  SCREAMING_SNAKE_CASE_	:	int
  SCREAMING_SNAKE_CASE_	:	Tuple[int]						=(16, 32, 96, 2_56)
  SCREAMING_SNAKE_CASE_	:	jnp.dtype						=jnp.floataa






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      nn.Conv(
           self.block_out_channels[0]   ,				kernel_size=(3, 3)   ,				padding=((1, 1), (1, 1))   ,				dtype=self.dtype   ,				)

       UpperCamelCase__										=      []
       for i in range(len(self.block_out_channels	) - 1	):
            UpperCamelCase__										=      self.block_out_channels[i]
            UpperCamelCase__										=      self.block_out_channels[i + 1]
            UpperCamelCase__										=      nn.Conv(
                SCREAMING_SNAKE_CASE_   ,				kernel_size=(3, 3)   ,				padding=((1, 1), (1, 1))   ,				dtype=self.dtype   ,				)
            blocks.append(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      nn.Conv(
                SCREAMING_SNAKE_CASE_   ,				kernel_size=(3, 3)   ,				strides=(2, 2)   ,				padding=((1, 1), (1, 1))   ,				dtype=self.dtype   ,				)
            blocks.append(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      blocks

       UpperCamelCase__										=      nn.Conv(
           self.conditioning_embedding_channels   ,				kernel_size=(3, 3)   ,				padding=((1, 1), (1, 1))   ,				kernel_init=nn.initializers.zeros_init()   ,				bias_init=nn.initializers.zeros_init()   ,				dtype=self.dtype   ,				)






  def __call__(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.conv_in(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.silu(SCREAMING_SNAKE_CASE_	)

       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      nn.silu(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.conv_out(SCREAMING_SNAKE_CASE_	)

       return embedding



@flax_register_to_config
class     _A   (    nn.Module	, __UpperCamelCase	, __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	int						=32
  SCREAMING_SNAKE_CASE_	:	int						=4
  SCREAMING_SNAKE_CASE_	:	Tuple[str]						=(
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "DownBlock2D",
  )
  SCREAMING_SNAKE_CASE_	:	Union[bool, Tuple[bool]]						=False
  SCREAMING_SNAKE_CASE_	:	Tuple[int]						=(3_20, 6_40, 12_80, 12_80)
  SCREAMING_SNAKE_CASE_	:	int						=2
  SCREAMING_SNAKE_CASE_	:	Union[int, Tuple[int]]						=8
  SCREAMING_SNAKE_CASE_	:	Optional[Union[int, Tuple[int]]]						=None
  SCREAMING_SNAKE_CASE_	:	int						=12_80
  SCREAMING_SNAKE_CASE_	:	float						=0.0
  SCREAMING_SNAKE_CASE_	:	bool						=False
  SCREAMING_SNAKE_CASE_	:	jnp.dtype						=jnp.floataa
  SCREAMING_SNAKE_CASE_	:	bool						=True
  SCREAMING_SNAKE_CASE_	:	int						=0
  SCREAMING_SNAKE_CASE_	:	str						="rgb"
  SCREAMING_SNAKE_CASE_	:	Tuple[int]						=(16, 32, 96, 2_56)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      FrozenDict:





       '''simple docstring'''





       UpperCamelCase__										=      (1, self.in_channels, self.sample_size, self.sample_size)
       UpperCamelCase__										=      jnp.zeros(SCREAMING_SNAKE_CASE_   ,				dtype=jnp.floataa	)
       UpperCamelCase__										=      jnp.ones((1,)   ,				dtype=jnp.intaa	)
       UpperCamelCase__										=      jnp.zeros((1, 1, self.cross_attention_dim)   ,				dtype=jnp.floataa	)
       UpperCamelCase__										=      (1, 3, self.sample_size * 8, self.sample_size * 8)
       UpperCamelCase__										=      jnp.zeros(SCREAMING_SNAKE_CASE_   ,				dtype=jnp.floataa	)

       UpperCamelCase__						,       UpperCamelCase__										=      jax.random.split(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''params''': params_rng, '''dropout''': dropout_rng}

       return self.init(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)["params"]






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.block_out_channels
       UpperCamelCase__										=      block_out_channels[0] * 4

       # If `num_attention_heads` is not defined (which is the case for most models)
       # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
       # The reason for this behavior is to correct for incorrectly named variables that were introduced
       # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
       # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
       # which is why we correct for the naming here.
       UpperCamelCase__										=      self.num_attention_heads or self.attention_head_dim

       # input
       UpperCamelCase__										=      nn.Conv(
           block_out_channels[0]   ,				kernel_size=(3, 3)   ,				strides=(1, 1)   ,				padding=((1, 1), (1, 1))   ,				dtype=self.dtype   ,				)

       # time
       UpperCamelCase__										=      FlaxTimesteps(
           block_out_channels[0]   ,				flip_sin_to_cos=self.flip_sin_to_cos   ,				freq_shift=self.config.freq_shift	)
       UpperCamelCase__										=      FlaxTimestepEmbedding(SCREAMING_SNAKE_CASE_   ,				dtype=self.dtype	)

       UpperCamelCase__										=      FlaxControlNetConditioningEmbedding(
           conditioning_embedding_channels=block_out_channels[0]   ,				block_out_channels=self.conditioning_embedding_out_channels   ,				)

       UpperCamelCase__										=      self.only_cross_attention
       if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      (only_cross_attention,) * len(self.down_block_types	)

       if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      (num_attention_heads,) * len(self.down_block_types	)

       # down
       UpperCamelCase__										=      []
       UpperCamelCase__										=      []

       UpperCamelCase__										=      block_out_channels[0]

       UpperCamelCase__										=      nn.Conv(
           SCREAMING_SNAKE_CASE_   ,				kernel_size=(1, 1)   ,				padding='''VALID'''   ,				kernel_init=nn.initializers.zeros_init()   ,				bias_init=nn.initializers.zeros_init()   ,				dtype=self.dtype   ,				)
       controlnet_down_blocks.append(SCREAMING_SNAKE_CASE_	)

       for i, down_block_type in enumerate(self.down_block_types	):
            UpperCamelCase__										=      output_channel
            UpperCamelCase__										=      block_out_channels[i]
            UpperCamelCase__										=      i == len(SCREAMING_SNAKE_CASE_	) - 1

            if down_block_type == "CrossAttnDownBlock2D":
                 UpperCamelCase__										=      FlaxCrossAttnDownBlockaD(
                     in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				dropout=self.dropout   ,				num_layers=self.layers_per_block   ,				num_attention_heads=num_attention_heads[i]   ,				add_downsample=not is_final_block   ,				use_linear_projection=self.use_linear_projection   ,				only_cross_attention=only_cross_attention[i]   ,				dtype=self.dtype   ,				)
            else:
                 UpperCamelCase__										=      FlaxDownBlockaD(
                     in_channels=SCREAMING_SNAKE_CASE_   ,				out_channels=SCREAMING_SNAKE_CASE_   ,				dropout=self.dropout   ,				num_layers=self.layers_per_block   ,				add_downsample=not is_final_block   ,				dtype=self.dtype   ,				)

            down_blocks.append(SCREAMING_SNAKE_CASE_	)

            for _ in range(self.layers_per_block	):
                 UpperCamelCase__										=      nn.Conv(
                     SCREAMING_SNAKE_CASE_   ,				kernel_size=(1, 1)   ,				padding='''VALID'''   ,				kernel_init=nn.initializers.zeros_init()   ,				bias_init=nn.initializers.zeros_init()   ,				dtype=self.dtype   ,				)
                 controlnet_down_blocks.append(SCREAMING_SNAKE_CASE_	)

            if not is_final_block:
                 UpperCamelCase__										=      nn.Conv(
                     SCREAMING_SNAKE_CASE_   ,				kernel_size=(1, 1)   ,				padding='''VALID'''   ,				kernel_init=nn.initializers.zeros_init()   ,				bias_init=nn.initializers.zeros_init()   ,				dtype=self.dtype   ,				)
                 controlnet_down_blocks.append(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      down_blocks
       UpperCamelCase__										=      controlnet_down_blocks

       # mid
       UpperCamelCase__										=      block_out_channels[-1]
       UpperCamelCase__										=      FlaxUNetMidBlockaDCrossAttn(
           in_channels=SCREAMING_SNAKE_CASE_   ,				dropout=self.dropout   ,				num_attention_heads=num_attention_heads[-1]   ,				use_linear_projection=self.use_linear_projection   ,				dtype=self.dtype   ,				)

       UpperCamelCase__										=      nn.Conv(
           SCREAMING_SNAKE_CASE_   ,				kernel_size=(1, 1)   ,				padding='''VALID'''   ,				kernel_init=nn.initializers.zeros_init()   ,				bias_init=nn.initializers.zeros_init()   ,				dtype=self.dtype   ,				)






  def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 1.0   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = False   ,				)		->      Union[FlaxControlNetOutput, Tuple]:





       '''simple docstring'''





       UpperCamelCase__										=      self.controlnet_conditioning_channel_order
       if channel_order == "bgr":
            UpperCamelCase__										=      jnp.flip(SCREAMING_SNAKE_CASE_   ,				axis=1	)

       # 1. time
       if not isinstance(SCREAMING_SNAKE_CASE_   ,				jnp.ndarray	):
            UpperCamelCase__										=      jnp.array([timesteps]   ,				dtype=jnp.intaa	)
       elif isinstance(SCREAMING_SNAKE_CASE_   ,				jnp.ndarray	) and len(timesteps.shape	) == 0:
            UpperCamelCase__										=      timesteps.astype(dtype=jnp.floataa	)
            UpperCamelCase__										=      jnp.expand_dims(SCREAMING_SNAKE_CASE_   ,				0	)

       UpperCamelCase__										=      self.time_proj(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.time_embedding(SCREAMING_SNAKE_CASE_	)

       # 2. pre-process
       UpperCamelCase__										=      jnp.transpose(SCREAMING_SNAKE_CASE_   ,				(0, 2, 3, 1)	)
       UpperCamelCase__										=      self.conv_in(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      jnp.transpose(SCREAMING_SNAKE_CASE_   ,				(0, 2, 3, 1)	)
       UpperCamelCase__										=      self.controlnet_cond_embedding(SCREAMING_SNAKE_CASE_	)
       sample += controlnet_cond

       # 3. down
       UpperCamelCase__										=      (sample,)
       for down_block in self.down_blocks:
            if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__						,       UpperCamelCase__										=      down_block(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				deterministic=not train	)
            else:
                 UpperCamelCase__						,       UpperCamelCase__										=      down_block(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				deterministic=not train	)
            down_block_res_samples += res_samples

       # 4. mid
       UpperCamelCase__										=      self.mid_block(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				deterministic=not train	)

       # 5. contronet blocks
       UpperCamelCase__										=      ()
       for down_block_res_sample, controlnet_block in zip(SCREAMING_SNAKE_CASE_   ,				self.controlnet_down_blocks	):
            UpperCamelCase__										=      controlnet_block(SCREAMING_SNAKE_CASE_	)
            controlnet_down_block_res_samples += (down_block_res_sample,)

       UpperCamelCase__										=      controlnet_down_block_res_samples

       UpperCamelCase__										=      self.controlnet_mid_block(SCREAMING_SNAKE_CASE_	)

       # 6. scaling
       UpperCamelCase__										=      [sample * conditioning_scale for sample in down_block_res_samples]
       mid_block_res_sample *= conditioning_scale

       if not return_dict:
            return (down_block_res_samples, mid_block_res_sample)

       return FlaxControlNetOutput(
           down_block_res_samples=SCREAMING_SNAKE_CASE_   ,				mid_block_res_sample=SCREAMING_SNAKE_CASE_	)




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__":
     __magic_name__								=pd.read_csv('''sample_data.csv''', header=None)
     __magic_name__								=df.shape[:1][0]
     # If you're using some other dataset input the target column
     __magic_name__								=df.iloc[:, 1:2]
     __magic_name__								=actual_data.values.reshape(len_data, 1)
     __magic_name__								=MinMaxScaler().fit_transform(actual_data)
     __magic_name__								=10
     __magic_name__								=5
     __magic_name__								=20
     __magic_name__								=len_data - periods * look_back
     __magic_name__								=actual_data[:division]
     __magic_name__								=actual_data[division - look_back :]
     __magic_name__		,  __magic_name__								=[], []
     __magic_name__		,  __magic_name__								=[], []

     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])
     __magic_name__								=np.array(train_x)
     __magic_name__								=np.array(test_x)
     __magic_name__								=np.array([list(i.ravel()) for i in train_y])
     __magic_name__								=np.array([list(i.ravel()) for i in test_y])

     __magic_name__								=Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     __magic_name__								=model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     __magic_name__								=model.predict(x_test)



