


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


lowerCAmelCase__ :			Optional[int]         =logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__ :			Dict         ='\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'







@dataclass
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	4_2







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	):





										"""simple docstring"""




										super().__init__()

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:    str 		=				latents * scheduler.init_noise_sigma
										return latents






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__=0					):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.device(F'''cuda:{gpu_id}'''					)

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__=0					):





										"""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.'					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				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)

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

										if self.safety_checker is not None:
													SCREAMING_SNAKE_CASE_		:    Any 		=				cpu_offload_with_hook(self.safety_checker ,	lowerCAmelCase__ ,	prev_module_hook=lowerCAmelCase__					)

										# We'll offload the last model manually.
										SCREAMING_SNAKE_CASE_		:    Any 		=				hook






							@property
							def 				UpperCamelCase__    (			self					):





										"""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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	):





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

										SCREAMING_SNAKE_CASE_		:    Any 		=				self.text_encoder(text_input_ids.to(lowerCAmelCase__					)					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				text_encoder_output.text_embeds

										SCREAMING_SNAKE_CASE_		:    int 		=				prompt_embeds.repeat_interleave(lowerCAmelCase__ ,	dim=0					)
										# in Shap-E it normalize the prompt_embeds and then later rescale it
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				prompt_embeds / torch.linalg.norm(lowerCAmelCase__ ,	dim=-1 ,	keepdim=lowerCAmelCase__					)

										if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				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
													SCREAMING_SNAKE_CASE_		:    Dict 		=				torch.cat([negative_prompt_embeds, prompt_embeds]					)

										# Rescale the features to have unit variance
										SCREAMING_SNAKE_CASE_		:    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__ = 2_5 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = 4.0 ,	lowerCAmelCase__ = 6_4 ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	):





										"""simple docstring"""




										if isinstance(lowerCAmelCase__ ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				1
										elif isinstance(lowerCAmelCase__ ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				len(lowerCAmelCase__					)
										else:
													raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__					)}'''					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				self._execution_device

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				batch_size * num_images_per_prompt

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

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__ ,	device=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.scheduler.timesteps

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.prior.config.num_embeddings
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.prior.config.embedding_dim

										SCREAMING_SNAKE_CASE_		:    Union[str, 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
										SCREAMING_SNAKE_CASE_		:    List[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
													SCREAMING_SNAKE_CASE_		:    str 		=				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				self.scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		:    int 		=				self.prior(
													    lowerCAmelCase__ ,	timestep=lowerCAmelCase__ ,	proj_embedding=lowerCAmelCase__ ,	).predicted_image_embedding

													# remove the variance
													SCREAMING_SNAKE_CASE_		:    int 		=				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_		:    Tuple 		=				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

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

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

										SCREAMING_SNAKE_CASE_		:    Dict 		=				[]
										for i, latent in enumerate(lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    str 		=				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__					)

										SCREAMING_SNAKE_CASE_		:    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}'''					)

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

										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				[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 collections import Counter






def 	a__ (							A__							):
			return sum(socks_by_color // 2 for socks_by_color in Counter(A__							).values()							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__ :			Optional[int]         =[int(x) for x in input('Enter socks by color :').rstrip().split()]
				print(F"""sock_merchant({colors}) = {sock_merchant(colors)}""")





