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

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

					__UpperCamelCase	: Optional[int]    		=  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')
					__UpperCamelCase	: str    		=  model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					__UpperCamelCase	: Optional[int]    		=  model.predict(x_test)






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	IFPipeline
						UpperCamelCase_  =	TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
						UpperCamelCase_  =	TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase_  =	PipelineTesterMixin.required_optional_params - {"""latents"""}


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												return self._get_dummy_components()


						def  __A ( self			:       int					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       int=0		):





												'''simple docstring'''





												if str(UpperCamelCase__		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							torch.manual_seed(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					Dict           =							torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					int           =							{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_save_load_optional_components()


						@unittest.skipIf(torch_device != '''cuda'''					,	reason='''float16 requires CUDA'''		)
						def  __A ( self			:       Any		):





												'''simple docstring'''





												super().test_save_load_floataa(expected_max_diff=1E-1		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												self._test_save_load_local()


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2					,	)



						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()					,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,	)
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)






@slow
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa		)

												SCREAMING_SNAKE_CASE							:					str           =							IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa					,	text_encoder=UpperCamelCase__					,	tokenizer=UpperCamelCase__		)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda'''		)

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a.encode_prompt('''anime turtle'''					,	device='''cuda'''		)

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												SCREAMING_SNAKE_CASE							:					str           =							None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_imgaimg(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												SCREAMING_SNAKE_CASE							:					Tuple           =							IFInpaintingPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_inpainting(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 13 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Tuple           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					int           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					int           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)



						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Any           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					List[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)






def    A			(					):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()





