





"""simple docstring"""




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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       42




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       int,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       HeunDiscreteScheduler,					lowerCAmelCase__						:       ShapERenderer,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__()

										self.register_modules(
										    prior=lowerCAmelCase__,					text_encoder=lowerCAmelCase__,					tokenizer=lowerCAmelCase__,					scheduler=lowerCAmelCase__,					renderer=lowerCAmelCase__,					)






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Any      =       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}"""				)
																_UpperCamelCase     :   List[Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       str=0				)      -> Dict:




										'''simple docstring'''



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

										_UpperCamelCase     :   List[str]      =       torch.device(f"""cuda:{gpu_id}"""				)

										_UpperCamelCase     :   List[str]      =       [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Tuple=0				)      -> List[Any]:




										'''simple docstring'''



										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.'''				)

										_UpperCamelCase     :   str      =       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)

										_UpperCamelCase     :   Tuple      =       None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(lowerCAmelCase__,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										if self.safety_checker is not None:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(self.safety_checker,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

										# We'll offload the last model manually.
										_UpperCamelCase     :   Union[str, Any]      =       hook






				@property
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''simple docstring'''



										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
										_UpperCamelCase     :   Optional[Any]      =       0
										# get prompt text embeddings
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(
										    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
										_UpperCamelCase     :   Union[str, Any]      =       text_inputs.input_ids
										_UpperCamelCase     :   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__				):
																_UpperCamelCase     :   Tuple      =       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}"""				)

										_UpperCamelCase     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)
										_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds

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

										if do_classifier_free_guidance:
																_UpperCamelCase     :   Any      =       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
																_UpperCamelCase     :   Optional[Any]      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

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

										return prompt_embeds






				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__				)
				def __call__(					self						:       Optional[int],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       int = 1,					lowerCAmelCase__						:       int = 2_5,					lowerCAmelCase__						:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,					lowerCAmelCase__						:       Optional[torch.FloatTensor] = None,					lowerCAmelCase__						:       float = 4.0,					lowerCAmelCase__						:       int = 6_4,					lowerCAmelCase__						:       Optional[str] = "pil",					lowerCAmelCase__						:       bool = True,					)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       self._execution_device

										_UpperCamelCase     :   List[Any]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[str]      =       guidance_scale > 1.0
										_UpperCamelCase     :   str      =       self._encode_prompt(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       self.scheduler.timesteps

										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.num_embeddings
										_UpperCamelCase     :   Optional[Any]      =       self.prior.config.embedding_dim

										_UpperCamelCase     :   Tuple      =       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
										_UpperCamelCase     :   Dict      =       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
																_UpperCamelCase     :   Optional[int]      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																_UpperCamelCase     :   Dict      =       self.scheduler.scale_model_input(lowerCAmelCase__,					lowerCAmelCase__				)

																_UpperCamelCase     :   Tuple      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					).predicted_image_embedding

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

																if do_classifier_free_guidance is not None:
																						_UpperCamelCase  ,				_UpperCamelCase     :   List[str]      =       noise_pred.chunk(2				)
																						_UpperCamelCase     :   Optional[Any]      =       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																_UpperCamelCase     :   Dict      =       self.scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					).prev_sample

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

										_UpperCamelCase     :   List[Any]      =       []
										for i, latent in enumerate(lowerCAmelCase__				):
																_UpperCamelCase     :   Union[str, Any]      =       self.renderer.decode(
																    latent[None, :],					lowerCAmelCase__,					size=lowerCAmelCase__,					ray_batch_size=4_0_9_6,					n_coarse_samples=6_4,					n_fine_samples=1_2_8,					)
																images.append(lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       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}"""				)

										_UpperCamelCase     :   Dict      =       images.cpu().numpy()

										if output_type == "pil":
																_UpperCamelCase     :   Any      =       [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__				)




from __future__ import annotations

from collections.abc import Iterable







class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     Optional[Any]			,			_lowerCAmelCase					:     Tuple			,			_lowerCAmelCase					:     Tuple			):
           __snake_case			, __snake_case       :				str 			=    float(_lowerCAmelCase			), float(_lowerCAmelCase			)

       def __eq__(							self					:     List[Any]			,			_lowerCAmelCase					:     List[str]			):
           return self.x == other.x and self.y == other.y

       def __ne__(							self					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return not self == other

       def __gt__(							self					:     Union[str, Any]			,			_lowerCAmelCase					:     int			):
           if self.x > other.x:
               return True
           elif self.x == other.x:
               return self.y > other.y
           return False

       def __lt__(							self					:     List[str]			,			_lowerCAmelCase					:     List[str]			):
           return not self > other

       def __ge__(							self					:     List[Any]			,			_lowerCAmelCase					:     List[Any]			):
           if self.x > other.x:
               return True
           elif self.x == other.x:
               return self.y >= other.y
           return False

       def __le__(							self					:     List[Any]			,			_lowerCAmelCase					:     List[Any]			):
           if self.x < other.x:
               return True
           elif self.x == other.x:
               return self.y <= other.y
           return False

       def __repr__(							self					:     str			):
           return f'''({self.x}, {self.y})'''





       def __hash__(							self					:     List[str]			):
           return hash(self.x			)







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point] | list[list[float]] | Iterable[list[float]]		,							):







    '''simple docstring'''
    __snake_case       :				list[Point] 			=    []
    if list_of_tuples:
        for p in list_of_tuples:
            if isinstance(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
                points.append(__SCREAMING_SNAKE_CASE   )
            else:
                try:
                    points.append(Point(p[0]		,							p[1]   )   )
                except (IndexError, TypeError):
                    print(
                        F'''Ignoring deformed point {p}. All points'''
                        """ must have at least 2 coordinates."""   )
    return points







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point] | list[list[float]]   ):







    '''simple docstring'''

    if not hasattr(__SCREAMING_SNAKE_CASE		,							"""__iter__"""   ):
        __snake_case       :				List[str] 			=    F'''Expecting an iterable object but got an non-iterable type {points}'''
        raise ValueError(__SCREAMING_SNAKE_CASE   )

    if not points:
        __snake_case       :				Tuple 			=    F'''Expecting a list of points but got {points}'''
        raise ValueError(__SCREAMING_SNAKE_CASE   )

    return _construct_points(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Point		,							__SCREAMING_SNAKE_CASE       :					Point		,							__SCREAMING_SNAKE_CASE       :					Point   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
    return det







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point]   ):







    '''simple docstring'''
    __snake_case       :				List[Any] 			=    sorted(_validate_input(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				Optional[Any] 			=    len(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Dict 			=    set()

    for i in range(n - 1   ):
        for j in range(i + 1		,							__SCREAMING_SNAKE_CASE   ):
            __snake_case       :				str 			=    False
            __snake_case       :				Any 			=    True
            for k in range(__SCREAMING_SNAKE_CASE   ):
                if k != i and k != j:
                    __snake_case       :				str 			=    _det(points[i]		,							points[j]		,							points[k]   )

                    if det_k > 0:
                        __snake_case       :				str 			=    True
                    elif det_k < 0:
                        __snake_case       :				List[Any] 			=    True
                    else:
                        # point[i], point[j], point[k] all lie on a straight line
                        # if point[k] is to the left of point[i] or it's to the
                        # right of point[j], then point[i], point[j] cannot be
                        # part of the convex hull of A
                        if points[k] < points[i] or points[k] > points[j]:
                            __snake_case       :				Any 			=    False
                            break

                if points_left_of_ij and points_right_of_ij:
                    __snake_case       :				int 			=    False
                    break

            if ij_part_of_convex_hull:
                convex_set.update([points[i], points[j]]   )

    return sorted(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point]   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    sorted(_validate_input(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				Any 			=    len(__SCREAMING_SNAKE_CASE   )

    # divide all the points into an upper hull and a lower hull
    # the left most point and the right most point are definitely
    # members of the convex hull by definition.
    # use these two anchors to divide all the points into two hulls,
    # an upper hull and a lower hull.

    # all points to the left (above) the line joining the extreme points belong to the
    # upper hull
    # all points to the right (below) the line joining the extreme points below to the
    # lower hull
    # ignore all points on the line joining the extreme points since they cannot be
    # part of the convex hull

    __snake_case       :				Tuple 			=    points[0]
    __snake_case       :				Optional[Any] 			=    points[n - 1]

    __snake_case       :				Optional[Any] 			=    {left_most_point, right_most_point}
    __snake_case       :				Optional[int] 			=    []
    __snake_case       :				List[str] 			=    []

    for i in range(1		,							n - 1   ):
        __snake_case       :				Tuple 			=    _det(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							points[i]   )

        if det > 0:
            upper_hull.append(points[i]   )
        elif det < 0:
            lower_hull.append(points[i]   )

    _construct_hull(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
    _construct_hull(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

    return sorted(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point]		,							__SCREAMING_SNAKE_CASE       :					Point		,							__SCREAMING_SNAKE_CASE       :					Point		,							__SCREAMING_SNAKE_CASE       :					set[Point]   ):







    '''simple docstring'''
    if points:
        __snake_case       :				List[str] 			=    None
        __snake_case       :				Tuple 			=    float("""-inf"""   )
        __snake_case       :				List[Any] 			=    []

        for p in points:
            __snake_case       :				Union[str, Any] 			=    _det(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

            if det > 0:
                candidate_points.append(__SCREAMING_SNAKE_CASE   )

                if det > extreme_point_distance:
                    __snake_case       :				List[str] 			=    det
                    __snake_case       :				List[str] 			=    p

        if extreme_point:
            _construct_hull(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
            convex_set.add(__SCREAMING_SNAKE_CASE   )
            _construct_hull(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					list[Point]   ):







    '''simple docstring'''
    __snake_case       :				Tuple 			=    sorted(_validate_input(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				str 			=    len(__SCREAMING_SNAKE_CASE   )

    __snake_case       :				Optional[int] 			=    points[:2]
    for i in range(2		,							__SCREAMING_SNAKE_CASE   ):
        __snake_case       :				List[str] 			=    _det(convex_hull[1]		,							convex_hull[0]		,							points[i]   )
        if det > 0:
            convex_hull.insert(0		,							points[i]   )
            break
        elif det < 0:
            convex_hull.append(points[i]   )
            break
        else:
            __snake_case       :				Union[str, Any] 			=    points[i]
    i += 1

    for j in range(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ):
        if (
            _det(convex_hull[0]		,							convex_hull[-1]		,							points[j]   ) > 0
            and _det(convex_hull[-1]		,							convex_hull[0]		,							points[1]   ) < 0
        ):
            # The point lies within the convex hull
            continue

        convex_hull.insert(0		,							points[j]   )
        convex_hull.append(points[j]   )
        while _det(convex_hull[0]		,							convex_hull[1]		,							convex_hull[2]   ) >= 0:
            del convex_hull[1]
        while _det(convex_hull[-1]		,							convex_hull[-2]		,							convex_hull[-3]   ) <= 0:
            del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
    return sorted(convex_hull[1:] if len(__SCREAMING_SNAKE_CASE   ) > 3 else convex_hull   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    __snake_case       :				str 			=    [
        (0, 3),
        (2, 2),
        (1, 1),
        (2, 1),
        (3, 0),
        (0, 0),
        (3, 3),
        (2, -1),
        (2, -4),
        (1, -3),
    ]
    # the convex set of points is
    # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
    __snake_case       :				Optional[Any] 			=    convex_hull_bf(__SCREAMING_SNAKE_CASE   )

    __snake_case       :				List[Any] 			=    convex_hull_recursive(__SCREAMING_SNAKE_CASE   )
    assert results_bf == results_recursive

    __snake_case       :				Tuple 			=    convex_hull_melkman(__SCREAMING_SNAKE_CASE   )
    assert results_bf == results_melkman

    print(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
     main()



