

from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


lowerCAmelCase_			      =      logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase_			      =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=8			):
						snake_case_  =      h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						snake_case_  =      w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor





class       snake_case_  (						__A   ):







			'''simple docstring'''

			def __init__(	self					:							Dict				,  _UpperCamelCase					:							MultilingualCLIP				,  _UpperCamelCase					:							XLMRobertaTokenizer				,  _UpperCamelCase					:							UNetaDConditionModel				,  _UpperCamelCase					:							Union[DDIMScheduler, DDPMScheduler]				,  _UpperCamelCase					:							VQModel				,  ) ->List[Any]:
									super().__init__()

									self.register_modules(
									    text_encoder=_UpperCamelCase				,  tokenizer=_UpperCamelCase				,  unet=_UpperCamelCase				,  scheduler=_UpperCamelCase				,  movq=_UpperCamelCase				,  )
									snake_case_  =      2 ** (len(self.movq.config.block_out_channels				) - 1)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Union[str, Any]				) ->Union[str, Any]:
									if latents is None:
															snake_case_  =      randn_tensor(_UpperCamelCase				,  generator=_UpperCamelCase				,  device=_UpperCamelCase				,  dtype=_UpperCamelCase				)
									else:
															if latents.shape != shape:
																					raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
															snake_case_  =      latents.to(_UpperCamelCase				)

									snake_case_  =      latents * scheduler.init_noise_sigma
									return latents







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str=None				,  ) ->Tuple:
									snake_case_  =      len(_UpperCamelCase				) if isinstance(_UpperCamelCase				,  _UpperCamelCase				) else 1
									# get prompt text embeddings
									snake_case_  =      self.tokenizer(
									    _UpperCamelCase				,  padding='''max_length'''				,  truncation=_UpperCamelCase				,  max_length=7_7				,  return_attention_mask=_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors='''pt'''				,  )

									snake_case_  =      text_inputs.input_ids
									snake_case_  =      self.tokenizer(_UpperCamelCase				,  padding='''longest'''				,  return_tensors='''pt'''				).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
															logger.warning(
															    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
															    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''				)

									snake_case_  =      text_input_ids.to(_UpperCamelCase				)
									snake_case_  =      text_inputs.attention_mask.to(_UpperCamelCase				)

									snake_case_,       snake_case_  =      self.text_encoder(
									    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

									snake_case_  =      prompt_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)
									snake_case_  =      text_encoder_hidden_states.repeat_interleave(_UpperCamelCase				,  dim=0				)
									snake_case_  =      text_mask.repeat_interleave(_UpperCamelCase				,  dim=0				)

									if do_classifier_free_guidance:
															snake_case_  =      42
															if negative_prompt is None:
																					snake_case_  =      [''''''] * batch_size
															elif type(_UpperCamelCase				) is not type(_UpperCamelCase				):
																					raise TypeError(
																					    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_UpperCamelCase				)} !='''
																					    f''' {type(_UpperCamelCase				)}.'''				)
															elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
																					snake_case_  =      [negative_prompt]
															elif batch_size != len(_UpperCamelCase				):
																					raise ValueError(
																					    f'''`negative_prompt`: {negative_prompt} has batch size {len(_UpperCamelCase				)}, but `prompt`:'''
																					    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																					    ''' the batch size of `prompt`.'''				)
															else:
																					snake_case_  =      negative_prompt

															snake_case_  =      self.tokenizer(
															    _UpperCamelCase				,  padding='''max_length'''				,  max_length=7_7				,  truncation=_UpperCamelCase				,  return_attention_mask=_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors='''pt'''				,  )
															snake_case_  =      uncond_input.input_ids.to(_UpperCamelCase				)
															snake_case_  =      uncond_input.attention_mask.to(_UpperCamelCase				)

															snake_case_,       snake_case_  =      self.text_encoder(
															    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

															snake_case_  =      negative_prompt_embeds.shape[1]
															snake_case_  =      negative_prompt_embeds.repeat(1				,  _UpperCamelCase				)
															snake_case_  =      negative_prompt_embeds.view(batch_size * num_images_per_prompt				,  _UpperCamelCase				)

															snake_case_  =      uncond_text_encoder_hidden_states.shape[1]
															snake_case_  =      uncond_text_encoder_hidden_states.repeat(1				,  _UpperCamelCase				,  1				)
															snake_case_  =      uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt				,  _UpperCamelCase				,  -1				)
															snake_case_  =      uncond_text_mask.repeat_interleave(_UpperCamelCase				,  dim=0				)

															# done duplicates

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															snake_case_  =      torch.cat([negative_prompt_embeds, prompt_embeds]				)
															snake_case_  =      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

															snake_case_  =      torch.cat([uncond_text_mask, text_mask]				)

									return prompt_embeds, text_encoder_hidden_states, text_mask







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							int=0				) ->Union[str, Any]:
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

									snake_case_  =      torch.device(f'''cuda:{gpu_id}'''				)

									snake_case_  =      [
									    self.unet,
									    self.text_encoder,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(_UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							str=0				) ->Tuple:
									if is_accelerate_available() and is_accelerate_version('''>='''				,  '''0.17.0.dev0'''				):
															from accelerate import cpu_offload_with_hook
									else:
															raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

									snake_case_  =      torch.device(f'''cuda:{gpu_id}'''				)

									if self.device.type != "cpu":
															self.to('''cpu'''				,  silence_dtype_warnings=_UpperCamelCase				)
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									snake_case_  =      None
									for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
															snake_case_,       snake_case_  =      cpu_offload_with_hook(_UpperCamelCase				,  _UpperCamelCase				,  prev_module_hook=_UpperCamelCase				)

									if self.safety_checker is not None:
															snake_case_,       snake_case_  =      cpu_offload_with_hook(self.safety_checker				,  _UpperCamelCase				,  prev_module_hook=_UpperCamelCase				)

									# We'll offload the last model manually.
									snake_case_  =      hook







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							snake_case__(	self					:							Optional[int]				) ->Optional[Any]:
									if not hasattr(self.unet				,  '''_hf_hook'''				):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(_UpperCamelCase				,  '''_hf_hook'''				)
															    and hasattr(module._hf_hook				,  '''execution_device'''				)
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device				)
									return self.device







			@torch.no_grad()
			@replace_example_docstring(_UpperCamelCase				)
			def __call__(	self					:							Tuple				,  _UpperCamelCase					:							Union[str, List[str]]				,  _UpperCamelCase					:							Union[torch.FloatTensor, List[torch.FloatTensor]]				,  _UpperCamelCase					:							Union[torch.FloatTensor, List[torch.FloatTensor]]				,  _UpperCamelCase					:							Optional[Union[str, List[str]]] = None				,  _UpperCamelCase					:							int = 5_1_2				,  _UpperCamelCase					:							int = 5_1_2				,  _UpperCamelCase					:							int = 1_0_0				,  _UpperCamelCase					:							float = 4.0				,  _UpperCamelCase					:							int = 1				,  _UpperCamelCase					:							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,  _UpperCamelCase					:							Optional[torch.FloatTensor] = None				,  _UpperCamelCase					:							Optional[str] = "pil"				,  _UpperCamelCase					:							bool = True				,  ) ->Optional[int]:

									if isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      1
									elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      len(_UpperCamelCase				)
									else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase				)}'''				)

									snake_case_  =      self._execution_device

									snake_case_  =      batch_size * num_images_per_prompt
									snake_case_  =      guidance_scale > 1.0

									snake_case_,       snake_case_,       snake_case_  =      self._encode_prompt(
									    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)

									if isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      torch.cat(_UpperCamelCase				,  dim=0				)
									if isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      torch.cat(_UpperCamelCase				,  dim=0				)

									if do_classifier_free_guidance:
															snake_case_  =      image_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)
															snake_case_  =      negative_image_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)

									snake_case_  =      torch.cat([negative_image_embeds, image_embeds]				,  dim=0				).to(
									    dtype=prompt_embeds.dtype				,  device=_UpperCamelCase				)

									self.scheduler.set_timesteps(_UpperCamelCase				,  device=_UpperCamelCase				)
									snake_case_  =      self.scheduler.timesteps

									snake_case_  =      self.unet.config.in_channels

									snake_case_,       snake_case_  =      get_new_h_w(_UpperCamelCase				,  _UpperCamelCase				,  self.movq_scale_factor				)

									# create initial latent
									snake_case_  =      self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)				,  text_encoder_hidden_states.dtype				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  self.scheduler				,  )

									for i, t in enumerate(self.progress_bar(_UpperCamelCase				)				):
															# expand the latents if we are doing classifier free guidance
															snake_case_  =      torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

															snake_case_  =      {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
															snake_case_  =      self.unet(
															    sample=_UpperCamelCase				,  timestep=_UpperCamelCase				,  encoder_hidden_states=_UpperCamelCase				,  added_cond_kwargs=_UpperCamelCase				,  return_dict=_UpperCamelCase				,  )[0]

															if do_classifier_free_guidance:
																					snake_case_,       snake_case_  =      noise_pred.split(latents.shape[1]				,  dim=1				)
																					snake_case_,       snake_case_  =      noise_pred.chunk(2				)
																					snake_case_,       snake_case_  =      variance_pred.chunk(2				)
																					snake_case_  =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					snake_case_  =      torch.cat([noise_pred, variance_pred_text]				,  dim=1				)

															if not (
															    hasattr(self.scheduler.config				,  '''variance_type'''				)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					snake_case_,       snake_case_  =      noise_pred.split(latents.shape[1]				,  dim=1				)

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_  =      self.scheduler.step(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  generator=_UpperCamelCase				,  ).prev_sample
									# post-processing
									snake_case_  =      self.movq.decode(_UpperCamelCase				,  force_not_quantize=_UpperCamelCase				)['''sample''']

									if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''				)

									if output_type in ["np", "pil"]:
															snake_case_  =      image * 0.5 + 0.5
															snake_case_  =      image.clamp(0				,  1				)
															snake_case_  =      image.cpu().permute(0				,  2				,  3				,  1				).float().numpy()

									if output_type == "pil":
															snake_case_  =      self.numpy_to_pil(_UpperCamelCase				)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase				)
"""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)



