"""simple docstring"""




import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCAmelCase       :    Union[str, Any]									=			{
    "gpt-j-6b": {"is_causal": True, "model": "sgugger/sharded-gpt-j-6B", "tokenizer": "EleutherAI/gpt-j-6B"},
    "gpt-neox": {"is_causal": True, "model": "EleutherAI/gpt-neox-20b"},
    "opt": {"is_causal": True, "model": "facebook/opt-30b"},
    "T0pp": {"is_causal": False, "model": "bigscience/T0pp", "model_revision": "sharded"},
}

UpperCAmelCase       :    Any									=			[
    "Hello, my name is",
    "Are unicorns real? Unicorns are",
    "For the first time in several years,",
    "My name is Julien and I am",
    "The goal of life is",
    "Whenever I'm sad, I like to",
]



def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   int  				=					argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''						)
					parser.add_argument('''model_name'''							, type=_lowercase							, default=_lowercase							, help='''The name of the model to try.'''						)
					parser.add_argument(
					    '''--tokenizer_name'''							, type=_lowercase							, default=_lowercase							, help='''The name of the tokenizer (if different from the model.'''						)
					parser.add_argument('''--is_causal'''							, type=_lowercase							, default=_lowercase							, help='''Whether or not the model is causal.'''						)
					parser.add_argument(
					    '''--model_revision'''							, type=_lowercase							, default=_lowercase							, help='''The revision to use for the model checkpoint.'''						)
					parser.add_argument('''--torch_dtype'''							, type=_lowercase							, default=_lowercase							, help='''The dtype for the model.'''						)
					parser.add_argument('''--disk_offload'''							, action='''store_true'''						)

					lowerCamelCase__		:   Tuple  				=					parser.parse_args()

					# Sanitize args
					if args.model_name in DEFAULT_MODELS:
										lowerCamelCase__		:   List[str]  				=					DEFAULT_MODELS[args.model_name]
										lowerCamelCase__		:   Optional[Any]  				=					defaults['''model''']
										if args.tokenizer_name is None:
															lowerCamelCase__		:   Any  				=					defaults.get('''tokenizer'''							, args.model_name						)
										if args.is_causal is None:
															lowerCamelCase__		:   Optional[int]  				=					defaults['''is_causal''']
										if args.model_revision is None:
															lowerCamelCase__		:   List[Any]  				=					defaults.get('''model_revision'''							, '''main'''						)

					if args.is_causal is None:
										raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.'''						)
					if args.tokenizer_name is None:
										lowerCamelCase__		:   Any  				=					args.model_name
					if args.model_revision is None:
										lowerCamelCase__		:   List[Any]  				=					'''main'''

					return args



def 				__a       (			):







					"""simple docstring"""





					transformers.utils.logging.set_verbosity_error()
					lowerCamelCase__		:   List[str]  				=					parse_args()

					if args.torch_dtype is None:
										lowerCamelCase__		:   Dict  				=					AutoConfig.from_pretrained(args.model_name						)
										lowerCamelCase__		:   Tuple  				=					getattr(_lowercase							, '''torch_dtype'''							, torch.floataa						)
					else:
										lowerCamelCase__		:   Dict  				=					getattr(_lowercase							, args.torch_dtype						)
					lowerCamelCase__		:   str  				=					AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
					lowerCamelCase__		:   Union[str, Any]  				=					{
					    '''torch_dtype''': torch_dtype,
					    '''revision''': args.model_revision,
					}
					if args.disk_offload:
										lowerCamelCase__		:   str  				=					'''tmp_offload'''
										lowerCamelCase__		:   Optional[int]  				=					True

					lowerCamelCase__		:   List[str]  				=					start_measure()
					lowerCamelCase__		:   str  				=					model_cls.from_pretrained(args.model_name							, device_map='''auto'''							, **_lowercase						)
					lowerCamelCase__		:   int  				=					end_measure(_lowercase						)
					log_measures(_lowercase							, '''Model loading'''						)

					lowerCamelCase__		:   str  				=					compute_module_sizes(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					{v: 0 for v in model.hf_device_map.values()}
					for module, device in model.hf_device_map.items():
										device_size[device] += module_sizes[module]
					lowerCamelCase__		:   List[str]  				=					'''\n'''.join([f"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
					print(f"""\nTheoretical use:\n{message}"""						)

					lowerCamelCase__		:   Optional[int]  				=					AutoTokenizer.from_pretrained(args.tokenizer_name						)

					lowerCamelCase__		:   List[str]  				=					start_measure()
					lowerCamelCase__		:   Dict  				=					[]
					lowerCamelCase__		:   int  				=					[]
					lowerCamelCase__		:   Optional[int]  				=					[]
					for prompt in PROMPTS:
										lowerCamelCase__		:   List[Any]  				=					tokenizer(_lowercase							, return_tensors='''pt'''						).to(0						)
										lowerCamelCase__		:   Tuple  				=					inputs['''input_ids'''][0].tolist()
										lowerCamelCase__		:   List[Any]  				=					time.time()
										lowerCamelCase__		:   List[str]  				=					model.generate(inputs['''input_ids''']						)
										lowerCamelCase__		:   Any  				=					time.time()
										lowerCamelCase__		:   List[Any]  				=					outputs[0].tolist()
										lowerCamelCase__		:   Dict  				=					len(_lowercase						) if outputs[: len(_lowercase						)] != tokens else len(_lowercase						) - len(_lowercase						)
										lowerCamelCase__		:   Optional[int]  				=					after_generate - before_generate

										lowerCamelCase__		:   List[Any]  				=					tokenizer.decode(_lowercase							, skip_special_tokens=_lowercase						)
										texts_outs.append(_lowercase						)
										generation_times.append(_lowercase						)
										gen_tokens.append(_lowercase						)
										print(f"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)

					lowerCamelCase__		:   List[Any]  				=					end_measure(_lowercase						)
					log_measures(_lowercase							, '''Model generation'''						)

					lowerCamelCase__		:   Tuple  				=					[gen / tok for gen, tok in zip(_lowercase							, _lowercase						)]
					lowerCamelCase__		:   Optional[Any]  				=					sum(_lowercase						) / len(_lowercase						)
					print(f"""Average time of generation per token: {avg_gen:.2f}s"""						)
					print(f"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""						)
					lowerCamelCase__		:   str  				=					sum(generation_times_per_token[1:]						) / (len(_lowercase						) - 1)
					print(f"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)


if __name__ == "__main__":
					main()







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

UpperCAmelCase       :    List[str]									=			"\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        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      42





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				def __init__(    self    :Optional[Any]      ,__UpperCAmelCase    :PriorTransformer      ,__UpperCAmelCase    :CLIPTextModelWithProjection      ,__UpperCAmelCase    :CLIPTokenizer      ,__UpperCAmelCase    :HeunDiscreteScheduler      ,__UpperCAmelCase    :ShapERenderer      ,)      ->					List[Any]:




									"""simple docstring"""



									super().__init__()

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







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									if latents is None:
														lowerCamelCase__		:   Optional[Any]  				=					randn_tensor(__UpperCAmelCase      ,generator=__UpperCAmelCase      ,device=__UpperCAmelCase      ,dtype=__UpperCAmelCase  )
									else:
														if latents.shape != shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
														lowerCamelCase__		:   Optional[Any]  				=					latents.to(__UpperCAmelCase  )

									lowerCamelCase__		:   Dict  				=					latents * scheduler.init_noise_sigma
									return latents







				def   lowercase_		(    self    :Dict      ,__UpperCAmelCase    :List[Any]=0  )      ->					str:




									"""simple docstring"""



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

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

									lowerCamelCase__		:   Optional[int]  				=					[self.text_encoder, self.prior]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :int      ,__UpperCAmelCase    :Dict=0  )      ->					Dict:




									"""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.'''  )

									lowerCamelCase__		:   List[Any]  				=					torch.device(F"""cuda:{gpu_id}"""  )

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

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

									if self.safety_checker is not None:
														lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					cpu_offload_with_hook(self.safety_checker      ,__UpperCAmelCase      ,prev_module_hook=__UpperCAmelCase  )

									# We'll offload the last model manually.
									lowerCamelCase__		:   Dict  				=					hook







				@property
				def   lowercase_		(    self    :Dict  )      ->					Optional[Any]:




									"""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(__UpperCAmelCase      ,'''_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   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Optional[Any]      ,)      ->					str:




									"""simple docstring"""



									len(__UpperCAmelCase  ) if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ) else 1

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

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__UpperCAmelCase      ,__UpperCAmelCase  ):
														lowerCamelCase__		:   List[str]  				=					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}"""  )

									lowerCamelCase__		:   Optional[int]  				=					self.text_encoder(text_input_ids.to(__UpperCAmelCase  )  )
									lowerCamelCase__		:   List[Any]  				=					text_encoder_output.text_embeds

									lowerCamelCase__		:   Optional[Any]  				=					prompt_embeds.repeat_interleave(__UpperCAmelCase      ,dim=0  )
									# in Shap-E it normalize the prompt_embeds and then later rescale it
									lowerCamelCase__		:   Union[str, Any]  				=					prompt_embeds / torch.linalg.norm(__UpperCAmelCase      ,dim=-1      ,keepdim=__UpperCAmelCase  )

									if do_classifier_free_guidance:
														lowerCamelCase__		:   Any  				=					torch.zeros_like(__UpperCAmelCase  )

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

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

									return prompt_embeds







				@torch.no_grad()
				@replace_example_docstring(__UpperCAmelCase  )
				def __call__(    self    :Any      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :int = 1      ,__UpperCAmelCase    :int = 25      ,__UpperCAmelCase    :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__UpperCAmelCase    :Optional[torch.FloatTensor] = None      ,__UpperCAmelCase    :float = 4.0      ,__UpperCAmelCase    :int = 64      ,__UpperCAmelCase    :Optional[str] = "pil"      ,__UpperCAmelCase    :bool = True      ,)      ->					Tuple:




									"""simple docstring"""



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

									lowerCamelCase__		:   Optional[Any]  				=					self._execution_device

									lowerCamelCase__		:   List[Any]  				=					batch_size * num_images_per_prompt

									lowerCamelCase__		:   Optional[int]  				=					guidance_scale > 1.0
									lowerCamelCase__		:   Tuple  				=					self._encode_prompt(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )

									# prior

									self.scheduler.set_timesteps(__UpperCAmelCase      ,device=__UpperCAmelCase  )
									lowerCamelCase__		:   str  				=					self.scheduler.timesteps

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

									lowerCamelCase__		:   int  				=					self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)      ,prompt_embeds.dtype      ,__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,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
									lowerCamelCase__		:   Tuple  				=					latents.reshape(latents.shape[0]      ,__UpperCAmelCase      ,__UpperCAmelCase  )

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

														lowerCamelCase__		:   Dict  				=					self.prior(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,proj_embedding=__UpperCAmelCase      ,).predicted_image_embedding

														# remove the variance
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[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:
																			lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					noise_pred.chunk(2  )
																			lowerCamelCase__		:   List[str]  				=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														lowerCamelCase__		:   Union[str, Any]  				=					self.scheduler.step(
														    __UpperCAmelCase      ,timestep=__UpperCAmelCase      ,sample=__UpperCAmelCase      ,).prev_sample

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

									lowerCamelCase__		:   List[Any]  				=					[]
									for i, latent in enumerate(__UpperCAmelCase  ):
														lowerCamelCase__		:   Tuple  				=					self.renderer.decode(
														    latent[None, :]      ,__UpperCAmelCase      ,size=__UpperCAmelCase      ,ray_batch_size=40_96      ,n_coarse_samples=64      ,n_fine_samples=1_28      ,)
														images.append(__UpperCAmelCase  )

									lowerCamelCase__		:   Union[str, Any]  				=					torch.stack(__UpperCAmelCase  )

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

									lowerCamelCase__		:   Optional[int]  				=					images.cpu().numpy()

									if output_type == "pil":
														lowerCamelCase__		:   Optional[Any]  				=					[self.numpy_to_pil(__UpperCAmelCase  ) 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=__UpperCAmelCase  )






