


'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


UpperCAmelCase_		:   List[str]							   =		logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_		:   Optional[int]							   =		"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

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

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

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

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""





@dataclass
class     lowercase__  (						__UpperCAmelCase					):





							'''simple docstring'''


							A_							:      Union[PIL.Image.Image, np.ndarray]





class     lowercase__  (						__UpperCAmelCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					):
											super().__init__()

											self.register_modules(
											    prior=__snake_case    ,					image_encoder=__snake_case    ,					image_processor=__snake_case    ,					scheduler=__snake_case    ,					renderer=__snake_case    ,					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):

											if latents is None:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      randn_tensor(__snake_case    ,					generator=__snake_case    ,					device=__snake_case    ,					dtype=__snake_case  )
											else:
															if latents.shape != shape:
																			raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
															_SCREAMING_SNAKE_CASE :       List[str]     		=      latents.to(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      latents * scheduler.init_noise_sigma
											return latents






							def UpperCAmelCase_	(    self    ,					__snake_case=0  ):

											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

											_SCREAMING_SNAKE_CASE :       Any     		=      torch.device(f"""cuda:{gpu_id}"""  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [self.image_encoder, self.prior]
											for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																			cpu_offload(__snake_case    ,					__snake_case  )






							@property
							def UpperCAmelCase_	(    self  ):

											if self.device != torch.device("""meta"""  ) or not hasattr(self.image_encoder    ,					"""_hf_hook"""  ):
															return self.device
											for module in self.image_encoder.modules():
															if (
															    hasattr(__snake_case    ,					"""_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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					):

											if isinstance(__snake_case    ,					__snake_case  ) and isinstance(image[0]    ,					torch.Tensor  ):
															_SCREAMING_SNAKE_CASE :       Dict     		=      torch.cat(__snake_case    ,					axis=0  ) if image[0].ndim == 4 else torch.stack(__snake_case    ,					axis=0  )

											if not isinstance(__snake_case    ,					torch.Tensor  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processor(__snake_case    ,					return_tensors="""pt"""  ).pixel_values[0].unsqueeze(0  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image.to(dtype=self.image_encoder.dtype    ,					device=__snake_case  )

											_SCREAMING_SNAKE_CASE :       int     		=      self.image_encoder(__snake_case  )['last_hidden_state']
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_embeds.repeat_interleave(__snake_case    ,					dim=0  )

											if do_classifier_free_guidance:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.zeros_like(__snake_case  )

															# 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
															_SCREAMING_SNAKE_CASE :       int     		=      torch.cat([negative_image_embeds, image_embeds]  )

											return image_embeds






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

											if isinstance(__snake_case    ,					PIL.Image.Image  ):
															_SCREAMING_SNAKE_CASE :       List[str]     		=      1
											elif isinstance(__snake_case    ,					torch.Tensor  ):
															_SCREAMING_SNAKE_CASE :       List[str]     		=      image.shape[0]
											elif isinstance(__snake_case    ,					__snake_case  ) and isinstance(image[0]    ,					(torch.Tensor, PIL.Image.Image)  ):
															_SCREAMING_SNAKE_CASE :       List[str]     		=      len(__snake_case  )
											else:
															raise ValueError(
															    f"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__snake_case  )}"""  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self._execution_device

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      batch_size * num_images_per_prompt

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      guidance_scale > 1.0
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self._encode_image(__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  )

											# prior

											self.scheduler.set_timesteps(__snake_case    ,					device=__snake_case  )
											_SCREAMING_SNAKE_CASE :       str     		=      self.scheduler.timesteps

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.prior.config.num_embeddings
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.prior.config.embedding_dim

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.prepare_latents(
											    (batch_size, num_embeddings * embedding_dim)    ,					image_embeds.dtype    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					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
											_SCREAMING_SNAKE_CASE :       Tuple     		=      latents.reshape(latents.shape[0]    ,					__snake_case    ,					__snake_case  )

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

															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.prior(
															    __snake_case    ,					timestep=__snake_case    ,					proj_embedding=__snake_case    ,					).predicted_image_embedding

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

															if do_classifier_free_guidance is not None:
																			_SCREAMING_SNAKE_CASE :       Any     		=      noise_pred.chunk(2  )
																			_SCREAMING_SNAKE_CASE :       str     		=      noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

															_SCREAMING_SNAKE_CASE :       List[str]     		=      self.scheduler.step(
															    __snake_case    ,					timestep=__snake_case    ,					sample=__snake_case    ,					).prev_sample

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

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      []
											for i, latent in enumerate(__snake_case  ):
															print()
															_SCREAMING_SNAKE_CASE :       Dict     		=      self.renderer.decode(
															    latent[None, :]    ,					__snake_case    ,					size=__snake_case    ,					ray_batch_size=4096    ,					n_coarse_samples=64    ,					n_fine_samples=128    ,					)

															images.append(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.stack(__snake_case  )

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

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      images.cpu().numpy()

											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Any     		=      [self.numpy_to_pil(__snake_case  ) 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=__snake_case  )











from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


