import numpy as np






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_=5							,     lowerCAmelCase_=5    )	->Any:
        a  :    Dict				=						depth
        a  :    List[str]				=						0
        a  :    Any				=						None
        a  :    List[str]				=						None
        a  :    str				=						min_leaf_size
        a  :    Optional[int]				=						None
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Union[str, Any]:

        if labels.ndim != 1:
             print('''Error: Input labels must be one dimensional'''    )

        return np.mean((labels - prediction) ** 2    )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Optional[Any]:

        if x.ndim != 1:
             print('''Error: Input data set must be one dimensional'''    )
             return
        if len(lowerCamelCase_    ) != len(lowerCamelCase_    ):
             print('''Error: X and y have different lengths'''    )
             return
        if y.ndim != 1:
             print('''Error: Data set labels must be one dimensional'''    )
             return

        if len(lowerCamelCase_    ) < 2 * self.min_leaf_size:
             a  :    Dict				=						np.mean(lowerCamelCase_    )
             return

        if self.depth == 1:
             a  :    str				=						np.mean(lowerCamelCase_    )
             return

        a  :    str				=						0
        a  :    str				=						self.mean_squared_error(lowerCamelCase_							,     np.mean(lowerCamelCase_    )    ) * 2
        for i in range(len(lowerCamelCase_    )    ):
             if len(x[:i]    ) < self.min_leaf_size:
                  continue
             elif len(x[i:]    ) < self.min_leaf_size:
                  continue
             else:
                  a  :    int				=						self.mean_squared_error(x[:i]							,     np.mean(y[:i]    )    )
                  a  :    Union[str, Any]				=						self.mean_squared_error(x[i:]							,     np.mean(y[i:]    )    )
                  a  :    List[str]				=						error_left + error_right
                  if error < min_error:
                       a  :    Dict				=						i
                       a  :    str				=						error

        if best_split != 0:
             a  :    Dict				=						x[:best_split]
             a  :    Optional[Any]				=						y[:best_split]
             a  :    Tuple				=						x[best_split:]
             a  :    List[str]				=						y[best_split:]

             a  :    List[str]				=						x[best_split]
             a  :    int				=						DecisionTree(
                 depth=self.depth - 1							,     min_leaf_size=self.min_leaf_size    )
             a  :    Tuple				=						DecisionTree(
                 depth=self.depth - 1							,     min_leaf_size=self.min_leaf_size    )
             self.left.train(lowerCamelCase_							,     lowerCamelCase_    )
             self.right.train(lowerCamelCase_							,     lowerCamelCase_    )
        else:
             a  :    List[Any]				=						np.mean(lowerCamelCase_    )

        return


   def _lowercase(    self							,     lowerCAmelCase_    )	->Any:

        if self.prediction is not None:
             return self.prediction
        elif self.left or self.right is not None:
             if x >= self.decision_boundary:
                  return self.right.predict(lowerCamelCase_    )
             else:
                  return self.left.predict(lowerCamelCase_    )
        else:
             print('''Error: Decision tree not yet trained'''    )
             return None






class 							_SCREAMING_SNAKE_CASE:
   @staticmethod
   def _lowercase(    lowerCAmelCase_							,     lowerCAmelCase_    )	->int:
        a  :    str				=						float(0    )
        for label in labels:
             squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size    )







def 	UpperCAmelCase       (		)	->	Dict:
     a  :    List[Any]				=						np.arange(-1.0						,      1.0						,      0.0_05       )
     a  :    Optional[Any]				=						np.sin(a_       )

     a  :    int				=						DecisionTree(depth=1_0						,      min_leaf_size=1_0       )
     tree.train(a_						,      a_       )

     a  :    List[Any]				=						(np.random.rand(1_0       ) * 2) - 1
     a  :    Union[str, Any]				=						np.array([tree.predict(a_       ) for x in test_cases]       )
     a  :    List[str]				=						np.mean((predictions - test_cases) ** 2       )

     print('''Test values: ''' + str(a_       )       )
     print('''Predictions: ''' + str(a_       )       )
     print('''Average error: ''' + str(a_       )       )


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod(name="""mean_squarred_error""", verbose=True)
import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

UpperCAmelCase							   =      """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""






@dataclass
class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->int:
        super().__init__()

        self.register_modules(
            prior=lowerCAmelCase_							,     text_encoder=lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     scheduler=lowerCAmelCase_							,     renderer=lowerCAmelCase_							,     )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->str:
        if latents is None:
             a  :    List[str]				=						randn_tensor(lowerCAmelCase_							,     generator=lowerCAmelCase_							,     device=lowerCAmelCase_							,     dtype=lowerCAmelCase_    )
        else:
             if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
             a  :    str				=						latents.to(lowerCAmelCase_    )

        a  :    Tuple				=						latents * scheduler.init_noise_sigma
        return latents
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->List[Any]:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

        a  :    Dict				=						torch.device(F"""cuda:{gpu_id}"""    )

        a  :    Union[str, Any]				=						[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(lowerCAmelCase_							,     lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->str:
        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.'''    )

        a  :    Union[str, Any]				=						torch.device(F"""cuda:{gpu_id}"""    )

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

        a  :    Dict				=						None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
             a			,							a  :    str				=						cpu_offload_with_hook(lowerCAmelCase_							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        if self.safety_checker is not None:
             a			,							a  :    Optional[Any]				=						cpu_offload_with_hook(self.safety_checker							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        # We'll offload the last model manually.
        a  :    Any				=						hook
   @property
   def _lowercase(    self    )	->Any:
        if self.device != torch.device('''meta'''    ) or not hasattr(self.text_encoder							,     '''_hf_hook'''    ):
             return self.device
        for module in self.text_encoder.modules():
             if (
                 hasattr(lowerCAmelCase_							,     '''_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
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->Optional[Any]:
        len(lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        a  :    Dict				=						0
        # get prompt text embeddings
        a  :    Any				=						self.tokenizer(
            lowerCAmelCase_							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )
        a  :    List[str]				=						text_inputs.input_ids
        a  :    Union[str, Any]				=						self.tokenizer(lowerCAmelCase_							,     padding='''longest'''							,     return_tensors='''pt'''    ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Union[str, Any]				=						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}"""    )

        a  :    Optional[int]				=						self.text_encoder(text_input_ids.to(lowerCAmelCase_    )    )
        a  :    Optional[Any]				=						text_encoder_output.text_embeds

        a  :    Any				=						prompt_embeds.repeat_interleave(lowerCAmelCase_							,     dim=0    )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        a  :    List[str]				=						prompt_embeds / torch.linalg.norm(lowerCAmelCase_							,     dim=-1							,     keepdim=lowerCAmelCase_    )

        if do_classifier_free_guidance:
             a  :    Optional[int]				=						torch.zeros_like(lowerCAmelCase_    )

             # 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
             a  :    Dict				=						torch.cat([negative_prompt_embeds, prompt_embeds]    )

        # Rescale the features to have unit variance
        a  :    Union[str, Any]				=						math.sqrt(prompt_embeds.shape[1]    ) * prompt_embeds

        return prompt_embeds


   @torch.no_grad()
   @replace_example_docstring(lowerCAmelCase_    )
   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 25							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 4.0							,     lowerCAmelCase_ = 64							,     lowerCAmelCase_ = "pil"							,     lowerCAmelCase_ = True							,     )	->Dict:

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Tuple				=						1
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Dict				=						len(lowerCAmelCase_    )
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_    )}"""    )

        a  :    Union[str, Any]				=						self._execution_device

        a  :    List[str]				=						batch_size * num_images_per_prompt

        a  :    Optional[int]				=						guidance_scale > 1.0
        a  :    Union[str, Any]				=						self._encode_prompt(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # prior

        self.scheduler.set_timesteps(lowerCAmelCase_							,     device=lowerCAmelCase_    )
        a  :    str				=						self.scheduler.timesteps

        a  :    Any				=						self.prior.config.num_embeddings
        a  :    Dict				=						self.prior.config.embedding_dim

        a  :    Any				=						self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)							,     prompt_embeds.dtype							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     self.scheduler							,     )

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        a  :    Any				=						latents.reshape(latents.shape[0]							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        for i, t in enumerate(self.progress_bar(lowerCAmelCase_    )    ):
             # expand the latents if we are doing classifier free guidance
             a  :    int				=						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
             a  :    List[str]				=						self.scheduler.scale_model_input(lowerCAmelCase_							,     lowerCAmelCase_    )

             a  :    Union[str, Any]				=						self.prior(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     proj_embedding=lowerCAmelCase_							,     ).predicted_image_embedding

             # remove the variance
             a			,							a  :    str				=						noise_pred.split(
                 scaled_model_input.shape[2]							,     dim=2    )  # batch_size, num_embeddings, embedding_dim

             if do_classifier_free_guidance is not None:
                  a			,							a  :    Tuple				=						noise_pred.chunk(2    )
                  a  :    Tuple				=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             a  :    List[Any]				=						self.scheduler.step(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     sample=lowerCAmelCase_							,     ).prev_sample

        if output_type == "latent":
             return ShapEPipelineOutput(images=lowerCAmelCase_    )

        a  :    int				=						[]
        for i, latent in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						self.renderer.decode(
                 latent[None, :]							,     lowerCAmelCase_							,     size=lowerCAmelCase_							,     ray_batch_size=40_96							,     n_coarse_samples=64							,     n_fine_samples=1_28							,     )
             images.append(lowerCAmelCase_    )

        a  :    Optional[Any]				=						torch.stack(lowerCAmelCase_    )

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

        a  :    Any				=						images.cpu().numpy()

        if output_type == "pil":
             a  :    Dict				=						[self.numpy_to_pil(lowerCAmelCase_    ) for image in images]

        # Offload last model to CPU
        if hasattr(self							,     '''final_offload_hook'''    ) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

        if not return_dict:
             return (images,)

        return ShapEPipelineOutput(images=lowerCAmelCase_    )