



"""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


_lowerCAmelCase       :      Union[str, Any]							=							logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase       :      Any							=							"\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 			UpperCAmelCase_						(       _UpperCamelCase		):
						__SCREAMING_SNAKE_CASE		:     Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class 			UpperCAmelCase_						(       _UpperCamelCase		):



						def __init__(  self  :			List[Any]  ,			A  :			PriorTransformer  ,			A  :			CLIPTextModelWithProjection  ,			A  :			CLIPTokenizer  ,			A  :			HeunDiscreteScheduler  ,			A  :			ShapERenderer  ,			):
											super().__init__()

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



						def 		snake_case_      (  self  :			int  ,			A  :			List[str]  ,			A  :			Tuple  ,			A  :			str  ,			A  :			Any  ,			A  :			str  ,			A  :			int		):
											if latents is None:
																_UpperCAmelCase    :			Dict              =					randn_tensor(A  ,			generator=A  ,			device=A  ,			dtype=A		)
											else:
																if latents.shape != shape:
																					raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
																_UpperCAmelCase    :			List[Any]              =					latents.to(A		)

											_UpperCAmelCase    :			int              =					latents * scheduler.init_noise_sigma
											return latents



						def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			str=0		):
											if is_accelerate_available():
																from accelerate import cpu_offload
											else:
																raise ImportError("Please install accelerate via `pip install accelerate`"		)

											_UpperCAmelCase    :			int              =					torch.device(f'cuda:{gpu_id}'		)

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



						def 		snake_case_      (  self  :			Tuple  ,			A  :			List[Any]=0		):
											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    :			Any              =					torch.device(f'cuda:{gpu_id}'		)

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

											_UpperCAmelCase    :			List[Any]              =					None
											for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCAmelCase    :			Dict              =					cpu_offload_with_hook(A  ,			A  ,			prev_module_hook=A		)

											if self.safety_checker is not None:
																_UpperCAmelCase    :			Optional[int]              =					cpu_offload_with_hook(self.safety_checker  ,			A  ,			prev_module_hook=A		)

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



						@property
						def 		snake_case_      (  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(A  ,			"_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  :			List[Any]  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			Optional[Any]  ,			A  :			Optional[Any]  ,			):
											len(A		) if isinstance(A  ,			A		) else 1

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

											if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A  ,			A		):
																_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    :			List[str]              =					self.text_encoder(text_input_ids.to(A		)		)
											_UpperCAmelCase    :			Optional[Any]              =					text_encoder_output.text_embeds

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

											if do_classifier_free_guidance:
																_UpperCAmelCase    :			Dict              =					torch.zeros_like(A		)

																# 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[int]              =					torch.cat([negative_prompt_embeds, prompt_embeds]		)

											# Rescale the features to have unit variance
											_UpperCAmelCase    :			int              =					math.sqrt(prompt_embeds.shape[1]		) * prompt_embeds

											return prompt_embeds



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

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

											_UpperCAmelCase    :			List[str]              =					self._execution_device

											_UpperCAmelCase    :			Optional[int]              =					batch_size * num_images_per_prompt

											_UpperCAmelCase    :			str              =					guidance_scale > 1.0
											_UpperCAmelCase    :			Dict              =					self._encode_prompt(A  ,			A  ,			A  ,			A		)

											# prior

											self.scheduler.set_timesteps(A  ,			device=A		)
											_UpperCAmelCase    :			Tuple              =					self.scheduler.timesteps

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

											_UpperCAmelCase    :			Optional[Any]              =					self.prepare_latents(
											    (batch_size, num_embeddings * embedding_dim)  ,			prompt_embeds.dtype  ,			A  ,			A  ,			A  ,			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    :			Optional[int]              =					latents.reshape(latents.shape[0]  ,			A  ,			A		)

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

																_UpperCAmelCase    :			str              =					self.prior(
																    A  ,			timestep=A  ,			proj_embedding=A  ,			).predicted_image_embedding

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

																_UpperCAmelCase    :			int              =					self.scheduler.step(
																    A  ,			timestep=A  ,			sample=A  ,			).prev_sample

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

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

											_UpperCAmelCase    :			Optional[Any]              =					torch.stack(A		)

											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    :			str              =					images.cpu().numpy()

											if output_type == "pil":
																_UpperCAmelCase    :			Optional[Any]              =					[self.numpy_to_pil(A		) 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=A		)





"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
     _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
     _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )

     # Load weights from numpy
     load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

     # Save pytorch-model
     _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
     torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
     print(f'Save configuration file to {pytorch_config_dump_path}'       )
     with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
          f.write(config.to_json_string()       )


if __name__ == "__main__":
 _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--imagegpt_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Path to the TensorFlow checkpoint path.",
 )
 parser.add_argument(
     "--model_size",
     default=None,
     type=str,
     required=True,
     help="Size of the model (can be either 'small', 'medium' or 'large').",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )
