import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   (    _snake_case	, unittest.TestCase							):
		SCREAMING_SNAKE_CASE_	:	str						=KandinskyInpaintPipeline
		SCREAMING_SNAKE_CASE_	:	Any						=["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
		SCREAMING_SNAKE_CASE_	:	Tuple						=[
		    "prompt",
		    "negative_prompt",
		    "image_embeds",
		    "negative_image_embeds",
		    "image",
		    "mask_image",
		]
		SCREAMING_SNAKE_CASE_	:	str						=[
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "negative_prompt",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		SCREAMING_SNAKE_CASE_	:	List[str]						=False






		@property
		def 		_a					(self	)		->      Tuple:





							'''simple docstring'''





							return 32






		@property
		def 		_a					(self	)		->      List[Any]:





							'''simple docstring'''





							return 32






		@property
		def 		_a					(self	)		->      List[Any]:





							'''simple docstring'''





							return self.time_input_dim






		@property
		def 		_a					(self	)		->      Tuple:





							'''simple docstring'''





							return self.time_input_dim * 4






		@property
		def 		_a					(self	)		->      Optional[int]:





							'''simple docstring'''





							return 100






		@property
		def 		_a					(self	)		->      Optional[int]:





							'''simple docstring'''





							UpperCamelCase__										=      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''	)
							return tokenizer






		@property
		def 		_a					(self	)		->      Dict:





							'''simple docstring'''





							torch.manual_seed(0	)
							UpperCamelCase__										=      MCLIPConfig(
							    numDims=self.cross_attention_dim   ,				transformerDimensions=self.text_embedder_hidden_size   ,				hidden_size=self.text_embedder_hidden_size   ,				intermediate_size=37   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				vocab_size=1005   ,				)

							UpperCamelCase__										=      MultilingualCLIP(lowerCAmelCase__	)
							UpperCamelCase__										=      text_encoder.eval()

							return text_encoder






		@property
		def 		_a					(self	)		->      Optional[int]:





							'''simple docstring'''





							torch.manual_seed(0	)

							UpperCamelCase__										=      {
							    '''in_channels''': 9,
							    # Out channels is double in channels because predicts mean and variance
							    '''out_channels''': 8,
							    '''addition_embed_type''': '''text_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''': '''text_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(**lowerCAmelCase__	)
							return model






		@property
		def 		_a					(self	)		->      Optional[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	)		->      List[Any]:





							'''simple docstring'''





							torch.manual_seed(0	)
							UpperCamelCase__										=      VQModel(**self.dummy_movq_kwargs	)
							return model






		def 		_a					(self	)		->      Any:





							'''simple docstring'''





							UpperCamelCase__										=      self.dummy_text_encoder
							UpperCamelCase__										=      self.dummy_tokenizer
							UpperCamelCase__										=      self.dummy_unet
							UpperCamelCase__										=      self.dummy_movq

							UpperCamelCase__										=      DDIMScheduler(
							    num_train_timesteps=1000   ,				beta_schedule='''linear'''   ,				beta_start=0.00085   ,				beta_end=0.012   ,				clip_sample=lowerCAmelCase__   ,				set_alpha_to_one=lowerCAmelCase__   ,				steps_offset=1   ,				prediction_type='''epsilon'''   ,				thresholding=lowerCAmelCase__   ,				)

							UpperCamelCase__										=      {
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components






		def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=0	)		->      str:





							'''simple docstring'''





							UpperCamelCase__										=      floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(lowerCAmelCase__	)	).to(lowerCAmelCase__	)
							UpperCamelCase__										=      floats_tensor((1, self.cross_attention_dim)   ,				rng=random.Random(seed + 1	)	).to(lowerCAmelCase__	)
							# create init_image
							UpperCamelCase__										=      floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(lowerCAmelCase__	)	).to(lowerCAmelCase__	)
							UpperCamelCase__										=      image.cpu().permute(0   ,				2   ,				3   ,				1	)[0]
							UpperCamelCase__										=      Image.fromarray(np.uinta(lowerCAmelCase__	)	).convert('''RGB'''	).resize((256, 256)	)
							# create mask
							UpperCamelCase__										=      np.ones((64, 64)   ,				dtype=np.floataa	)
							UpperCamelCase__										=      0

							if str(lowerCAmelCase__	).startswith('''mps'''	):
												UpperCamelCase__										=      torch.manual_seed(lowerCAmelCase__	)
							else:
												UpperCamelCase__										=      torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
							UpperCamelCase__										=      {
							    '''prompt''': '''horse''',
							    '''image''': init_image,
							    '''mask_image''': mask,
							    '''image_embeds''': image_embeds,
							    '''negative_image_embeds''': negative_image_embeds,
							    '''generator''': generator,
							    '''height''': 64,
							    '''width''': 64,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 4.0,
							    '''output_type''': '''np''',
							}
							return inputs






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





							'''simple docstring'''





							UpperCamelCase__										=      '''cpu'''

							UpperCamelCase__										=      self.get_dummy_components()

							UpperCamelCase__										=      self.pipeline_class(**lowerCAmelCase__	)
							UpperCamelCase__										=      pipe.to(lowerCAmelCase__	)

							pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

							UpperCamelCase__										=      pipe(**self.get_dummy_inputs(lowerCAmelCase__	)	)
							UpperCamelCase__										=      output.images

							UpperCamelCase__										=      pipe(
							    **self.get_dummy_inputs(lowerCAmelCase__	)   ,				return_dict=lowerCAmelCase__   ,				)[0]

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

							print(F"image.shape {image.shape}"	)

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

							UpperCamelCase__										=      np.array(
							    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]	)

							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()}"






		def 		_a					(self	)		->      Tuple:





							'''simple docstring'''





							super().test_inference_batch_single_identical(expected_max_diff=3E-3	)



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






		def 		_a					(self	)		->      str:





							'''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'''
							    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''	)

							UpperCamelCase__										=      load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''	)
							UpperCamelCase__										=      np.ones((768, 768)   ,				dtype=np.floataa	)
							UpperCamelCase__										=      0

							UpperCamelCase__										=      '''a hat'''

							UpperCamelCase__										=      KandinskyPriorPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-1-prior'''   ,				torch_dtype=torch.floataa	)
							pipe_prior.to(lowerCAmelCase__	)

							UpperCamelCase__										=      KandinskyInpaintPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-1-inpaint'''   ,				torch_dtype=torch.floataa	)
							UpperCamelCase__										=      pipeline.to(lowerCAmelCase__	)
							pipeline.set_progress_bar_config(disable=lowerCAmelCase__	)

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

							UpperCamelCase__										=      pipeline(
							    lowerCAmelCase__   ,				image=lowerCAmelCase__   ,				mask_image=lowerCAmelCase__   ,				image_embeds=lowerCAmelCase__   ,				negative_image_embeds=lowerCAmelCase__   ,				generator=lowerCAmelCase__   ,				num_inference_steps=100   ,				height=768   ,				width=768   ,				output_type='''np'''   ,				)

							UpperCamelCase__										=      output.images[0]

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

							assert_mean_pixel_difference(lowerCAmelCase__   ,				lowerCAmelCase__	)




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)



