



"""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


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

a_			   =					'''
    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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											super().__init__()

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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											if latents is None:
																snake_case_					:		Any 					=					randn_tensor(lowercase__   ,		generator=lowercase__   ,		device=lowercase__   ,		dtype=lowercase__       )
											else:
																if latents.shape != shape:
																					raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
																snake_case_					:		Tuple 					=					latents.to(lowercase__       )

											snake_case_					:		Any 					=					latents * scheduler.init_noise_sigma
											return latents







						def 						__UpperCamelCase							(self   ,		lowercase__=0       ):
											if is_accelerate_available():
																from accelerate import cpu_offload
											else:
																raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

											snake_case_					:		List[Any] 					=					torch.device(f'cuda:{gpu_id}'       )

											snake_case_					:		List[str] 					=					[self.image_encoder, self.prior]
											for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																					cpu_offload(lowercase__   ,		lowercase__       )







						@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(lowercase__   ,		"""_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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											if isinstance(lowercase__   ,		lowercase__       ) and isinstance(image[0]   ,		torch.Tensor       ):
																snake_case_					:		Tuple 					=					torch.cat(lowercase__   ,		axis=0       ) if image[0].ndim == 4 else torch.stack(lowercase__   ,		axis=0       )

											if not isinstance(lowercase__   ,		torch.Tensor       ):
																snake_case_					:		List[str] 					=					self.image_processor(lowercase__   ,		return_tensors="""pt"""       ).pixel_values[0].unsqueeze(0       )

											snake_case_					:		List[Any] 					=					image.to(dtype=self.image_encoder.dtype   ,		device=lowercase__       )

											snake_case_					:		int 					=					self.image_encoder(lowercase__       )["""last_hidden_state"""]
											snake_case_					:		Any 					=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

											snake_case_					:		List[str] 					=					image_embeds.repeat_interleave(lowercase__   ,		dim=0       )

											if do_classifier_free_guidance:
																snake_case_					:		Any 					=					torch.zeros_like(lowercase__       )

																# 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_					:		List[str] 					=					torch.cat([negative_image_embeds, image_embeds]       )

											return image_embeds







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

											if isinstance(lowercase__   ,		PIL.Image.Image       ):
																snake_case_					:		Dict 					=					1
											elif isinstance(lowercase__   ,		torch.Tensor       ):
																snake_case_					:		str 					=					image.shape[0]
											elif isinstance(lowercase__   ,		lowercase__       ) and isinstance(image[0]   ,		(torch.Tensor, PIL.Image.Image)       ):
																snake_case_					:		Union[str, Any] 					=					len(lowercase__       )
											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(lowercase__       )}'       )

											snake_case_					:		Tuple 					=					self._execution_device

											snake_case_					:		int 					=					batch_size * num_images_per_prompt

											snake_case_					:		Optional[int] 					=					guidance_scale > 1.0
											snake_case_					:		List[str] 					=					self._encode_image(lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       )

											# prior

											self.scheduler.set_timesteps(lowercase__   ,		device=lowercase__       )
											snake_case_					:		Any 					=					self.scheduler.timesteps

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

											snake_case_					:		Tuple 					=					self.prepare_latents(
											    (batch_size, num_embeddings * embedding_dim)   ,		image_embeds.dtype   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		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
											snake_case_					:		Optional[int] 					=					latents.reshape(latents.shape[0]   ,		lowercase__   ,		lowercase__       )

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

																snake_case_					:		List[Any] 					=					self.prior(
																    lowercase__   ,		timestep=lowercase__   ,		proj_embedding=lowercase__   ,		).predicted_image_embedding

																# remove the variance
																snake_case_					,			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:
																					snake_case_					,			snake_case_					:		List[Any] 					=					noise_pred.chunk(2       )
																					snake_case_					:		Tuple 					=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																snake_case_					:		int 					=					self.scheduler.step(
																    lowercase__   ,		timestep=lowercase__   ,		sample=lowercase__   ,		).prev_sample

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

											snake_case_					:		List[str] 					=					[]
											for i, latent in enumerate(lowercase__       ):
																print()
																snake_case_					:		int 					=					self.renderer.decode(
																    latent[None, :]   ,		lowercase__   ,		size=lowercase__   ,		ray_batch_size=40_96   ,		n_coarse_samples=64   ,		n_fine_samples=1_28   ,		)

																images.append(lowercase__       )

											snake_case_					:		Any 					=					torch.stack(lowercase__       )

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

											snake_case_					:		Optional[Any] 					=					images.cpu().numpy()

											if output_type == "pil":
																snake_case_					:		List[str] 					=					[self.numpy_to_pil(lowercase__       ) 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=lowercase__       )









"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser(
			    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
			)
			parser.add_argument(
			    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
			)
			parser.add_argument(
			    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
			)
			parser.add_argument('''--vocab_size''', default=30522, type=int)
			a_			   =					parser.parse_args()

			logger.info(F'''Loading data from {args.data_file}''')
			with open(args.data_file, '''rb''') as fp:
						a_			   =					pickle.load(fp)

			logger.info('''Counting occurrences for MLM.''')
			a_			   =					Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a_			   =					[0] * args.vocab_size
			for k, v in counter.items():
						a_			   =					v

			logger.info(F'''Dump to {args.token_counts_dump}''')
			with open(args.token_counts_dump, '''wb''') as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




