




'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Any   				=					KandinskyVaaImgaImgPipeline
       __a				:     Optional[int]   				=					["""image_embeds""", """negative_image_embeds""", """image"""]
       __a				:     int   				=					[
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       __a				:     Any   				=					[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       __a				:     Union[str, Any]   				=					False
       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           return 32
       @property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return 32
       @property
       def     A      (	self						:	str				):



           '''simple docstring'''

           return self.time_input_dim
       @property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return self.time_input_dim * 4
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return 100
       @property
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           torch.manual_seed(0				)

           UpperCAmelCase			       =		{
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           UpperCAmelCase			       =		UNetaDConditionModel(**lowercase				)
           return model
       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }
       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           torch.manual_seed(0				)
           UpperCAmelCase			       =		VQModel(**self.dummy_movq_kwargs				)
           return model
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.dummy_unet
           UpperCAmelCase			       =		self.dummy_movq

           UpperCAmelCase			       =		{
               '''num_train_timesteps''': 1_000,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.0_0085,
               '''beta_end''': 0.012,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           UpperCAmelCase			       =		DDIMScheduler(**lowercase				)

           UpperCAmelCase			       =		{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components
       def     A      (	self						:	Optional[int]   ,				lowercase						:	int   ,				lowercase						:	List[Any]=0				):



           '''simple docstring'''

           UpperCAmelCase			       =		floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(lowercase				)				).to(lowercase				)
           UpperCAmelCase			       =		floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1				)				).to(
               lowercase				)
           # create init_image
           UpperCAmelCase			       =		floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(lowercase				)				).to(lowercase				)
           UpperCAmelCase			       =		image.cpu().permute(0   ,				2   ,				3   ,				1				)[0]
           UpperCAmelCase			       =		Image.fromarray(np.uinta(lowercase				)				).convert('''RGB'''				).resize((256, 256)				)

           if str(lowercase				).startswith('''mps'''				):
               UpperCAmelCase			       =		torch.manual_seed(lowercase				)
           else:
               UpperCAmelCase			       =		torch.Generator(device=lowercase				).manual_seed(lowercase				)
           UpperCAmelCase			       =		{
               '''image''': init_image,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''num_inference_steps''': 10,
               '''guidance_scale''': 7.0,
               '''strength''': 0.2,
               '''output_type''': '''np''',
           }
           return inputs







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		'''cpu'''

           UpperCAmelCase			       =		self.get_dummy_components()

           UpperCAmelCase			       =		self.pipeline_class(**lowercase				)
           UpperCAmelCase			       =		pipe.to(lowercase				)

           pipe.set_progress_bar_config(disable=lowercase				)

           UpperCAmelCase			       =		pipe(**self.get_dummy_inputs(lowercase				)				)
           UpperCAmelCase			       =		output.images

           UpperCAmelCase			       =		pipe(
               **self.get_dummy_inputs(lowercase				)   ,				return_dict=lowercase   ,				)[0]

           UpperCAmelCase			       =		image[0, -3:, -3:, -1]
           UpperCAmelCase			       =		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           UpperCAmelCase			       =		np.array(
               [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]				)
           assert (
               np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
           ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
           ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''				)

           UpperCAmelCase			       =		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
           UpperCAmelCase			       =		'''A red cartoon frog, 4k'''

           UpperCAmelCase			       =		KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''   ,				torch_dtype=torch.floataa				)
           pipe_prior.to(lowercase				)

           UpperCAmelCase			       =		KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''   ,				torch_dtype=torch.floataa				)
           UpperCAmelCase			       =		pipeline.to(lowercase				)

           pipeline.set_progress_bar_config(disable=lowercase				)

           UpperCAmelCase			       =		torch.Generator(device='''cpu'''				).manual_seed(0				)
           UpperCAmelCase       ,     UpperCAmelCase			       =		pipe_prior(
               lowercase   ,				generator=lowercase   ,				num_inference_steps=5   ,				negative_prompt=''''''   ,				).to_tuple()

           UpperCAmelCase			       =		pipeline(
               image=lowercase   ,				image_embeds=lowercase   ,				negative_image_embeds=lowercase   ,				generator=lowercase   ,				num_inference_steps=100   ,				height=768   ,				width=768   ,				strength=0.2   ,				output_type='''np'''   ,				)

           UpperCAmelCase			       =		output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(lowercase   ,				lowercase				)











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

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

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)