
import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase     :					List[Any]       =16
_UpperCAmelCase     :					Dict       =32



def      lowerCAmelCase	(						lowerCAmelCase_	)->	List[Any]:
				return int(x / 2**20	)







class 			snake_case__:







						'''simple docstring'''




						def __enter__(			self							)   ->      int:
										gc.collect()
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
										lowerCAmelCase_	: List[str]      			=			torch.cuda.memory_allocated()
										lowerCAmelCase_	: Tuple      			=			psutil.Process()

										lowerCAmelCase_	: Tuple      			=			self.cpu_mem_used()
										lowerCAmelCase_	: Tuple      			=			True
										lowerCAmelCase_	: List[Any]      			=			threading.Thread(target=self.peak_monitor_func							)
										lowerCAmelCase_	: Optional[Any]      			=			True
										peak_monitor_thread.start()
										return self




						def   lowercase_	(			self							)   ->      int:
										return self.process.memory_info().rss




						def   lowercase_	(			self							)   ->      int:
										lowerCAmelCase_	: Optional[int]      			=			-1

										while True:
														lowerCAmelCase_	: int      			=			max(self.cpu_mem_used()				,  self.cpu_peak							)

														# can't sleep or will not catch the peak right (this comment is here on purpose)
														# time.sleep(0.001) # 1msec

														if not self.peak_monitoring:
																		break




						def __exit__(			self				,  *__lowercase							)   ->      int:
										lowerCAmelCase_	: Union[str, Any]      			=			False

										gc.collect()
										torch.cuda.empty_cache()
										lowerCAmelCase_	: Dict      			=			torch.cuda.memory_allocated()
										lowerCAmelCase_	: Tuple      			=			torch.cuda.max_memory_allocated()
										lowerCAmelCase_	: Optional[Any]      			=			bamb(self.end - self.begin							)
										lowerCAmelCase_	: str      			=			bamb(self.peak - self.begin							)

										lowerCAmelCase_	: Union[str, Any]      			=			self.cpu_mem_used()
										lowerCAmelCase_	: Optional[int]      			=			bamb(self.cpu_end - self.cpu_begin							)
										lowerCAmelCase_	: Optional[Any]      			=			bamb(self.cpu_peak - self.cpu_begin							)
										# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						_UpperCAmelCase     :					Union[str, Any]       =mocked_dataloaders  # noqa: F811



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Tuple:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,				lowerCAmelCase_	) == "1":
								lowerCAmelCase_	: List[Any]      			=			2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				lowerCAmelCase_	: Optional[int]      			=			FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase_	,				ranka_only=lowerCAmelCase_	)	,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase_	,				ranka_only=lowerCAmelCase_	)	,				)

				# Initialize accelerator
				if args.with_tracking:
								lowerCAmelCase_	: List[str]      			=			Accelerator(
								    cpu=args.cpu	,				mixed_precision=args.mixed_precision	,				log_with='''wandb'''	,				project_dir=args.logging_dir	,				fsdp_plugin=lowerCAmelCase_	,				)
				else:
								lowerCAmelCase_	: Dict      			=			Accelerator(fsdp_plugin=lowerCAmelCase_	)
				accelerator.print(accelerator.distributed_type	)

				if hasattr(args.checkpointing_steps	,				'''isdigit'''	):
								if args.checkpointing_steps == "epoch":
												lowerCAmelCase_	: int      			=			args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												lowerCAmelCase_	: Dict      			=			int(args.checkpointing_steps	)
								else:
												raise ValueError(
												    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
				else:
								lowerCAmelCase_	: int      			=			None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase_	: List[str]      			=			config['''lr''']
				lowerCAmelCase_	: Union[str, Any]      			=			int(config['''num_epochs''']	)
				lowerCAmelCase_	: Union[str, Any]      			=			int(config['''seed''']	)
				lowerCAmelCase_	: Any      			=			int(config['''batch_size''']	)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								lowerCAmelCase_	: Tuple      			=			vars(lowerCAmelCase_	)
								accelerator.init_trackers('''fsdp_glue_no_trainer'''	,				lowerCAmelCase_	)

				lowerCAmelCase_	: str      			=			AutoTokenizer.from_pretrained(args.model_name_or_path	)
				lowerCAmelCase_	: List[str]      			=			load_dataset('''glue'''	,				'''mrpc'''	)
				lowerCAmelCase_	: int      			=			evaluate.load('''glue'''	,				'''mrpc'''	)

				def tokenize_function(lowerCAmelCase_	):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase_	: List[str]      			=			tokenizer(examples['''sentence1''']	,				examples['''sentence2''']	,				truncation=lowerCAmelCase_	,				max_length=lowerCAmelCase_	)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCAmelCase_	: str      			=			datasets.map(
								    lowerCAmelCase_	,				batched=lowerCAmelCase_	,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,				)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase_	: Union[str, Any]      			=			tokenized_datasets.rename_column('''label'''	,				'''labels'''	)

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase_	: List[str]      			=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase_	: Any      			=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase_	: Union[str, Any]      			=			MAX_GPU_BATCH_SIZE

				def collate_fn(lowerCAmelCase_	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase_	: Tuple      			=			128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase_	: str      			=			16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase_	: Optional[Any]      			=			8
								else:
												lowerCAmelCase_	: Optional[Any]      			=			None

								return tokenizer.pad(
								    lowerCAmelCase_	,				padding='''longest'''	,				max_length=lowerCAmelCase_	,				pad_to_multiple_of=lowerCAmelCase_	,				return_tensors='''pt'''	,				)

				# Instantiate dataloaders.
				lowerCAmelCase_	: List[str]      			=			DataLoader(
				    tokenized_datasets['''train''']	,				shuffle=lowerCAmelCase_	,				collate_fn=lowerCAmelCase_	,				batch_size=lowerCAmelCase_	)
				lowerCAmelCase_	: str      			=			DataLoader(
				    tokenized_datasets['''validation''']	,				shuffle=lowerCAmelCase_	,				collate_fn=lowerCAmelCase_	,				batch_size=lowerCAmelCase_	)

				set_seed(lowerCAmelCase_	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase_	: str      			=			AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path	,				return_dict=lowerCAmelCase_	,				low_cpu_mem_usage=lowerCAmelCase_	)

				# New Code #
				# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
				lowerCAmelCase_	: int      			=			accelerator.prepare(lowerCAmelCase_	)
				accelerator.print(lowerCAmelCase_	)

				# Instantiate optimizer
				# New Code #
				# For FSDP feature, at present it doesn't support multiple parameter groups,
				# so we need to create a single parameter group for the whole model
				lowerCAmelCase_	: Any      			=			torch.optim.AdamW(params=model.parameters()	,				lr=lowerCAmelCase_	,				weight_decay=2e-4	)

				# Instantiate scheduler
				lowerCAmelCase_	: int      			=			get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_	,				num_warmup_steps=10	,				num_training_steps=(len(lowerCAmelCase_	) * num_epochs) // gradient_accumulation_steps	,				)

				# New Code #
				# For FSDP feature, prepare everything except the model as we have already prepared the model
				# before creating the optimizer
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCAmelCase_	: str      			=			accelerator.prepare(
				    lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)

				lowerCAmelCase_	: Any      			=			0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
												accelerator.load_state(args.resume_from_checkpoint	)
												lowerCAmelCase_	: Any      			=			os.path.basename(args.resume_from_checkpoint	)
								else:
												# Get the most recent checkpoint
												lowerCAmelCase_	: str      			=			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
												dirs.sort(key=os.path.getctime	)
												lowerCAmelCase_	: Any      			=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								lowerCAmelCase_	: Any      			=			os.path.splitext(lowerCAmelCase_	)[0]

								if "epoch" in training_difference:
												num_epochs -= int(training_difference.replace('''epoch_'''	,				''''''	)	)
												lowerCAmelCase_	: List[Any]      			=			None
								else:
												lowerCAmelCase_	: str      			=			int(training_difference.replace('''step_'''	,				''''''	)	)
												num_epochs -= resume_step // len(lowerCAmelCase_	)
												# If resuming by step, we also need to know exactly how far into the DataLoader we went
												lowerCAmelCase_	: Union[str, Any]      			=			(num_epochs * len(lowerCAmelCase_	)) - resume_step

    # Now we train the model
				for epoch in range(lowerCAmelCase_	):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																lowerCAmelCase_	: List[Any]      			=			0
												for step, batch in enumerate(lowerCAmelCase_	):
																# We need to skip steps until we reach the resumed step
																if args.resume_from_checkpoint and epoch == 0:
																				if resume_step is not None and step < resume_step:
																								pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																lowerCAmelCase_	: Optional[Any]      			=			model(**lowerCAmelCase_	)
																lowerCAmelCase_	: Union[str, Any]      			=			outputs.loss
																lowerCAmelCase_	: Dict      			=			loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(lowerCAmelCase_	)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# accelerator.print(lr_scheduler.get_lr())

																overall_step += 1

																if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
																				lowerCAmelCase_	: Union[str, Any]      			=			f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												lowerCAmelCase_	: Union[str, Any]      			=			os.path.join(args.output_dir	,				lowerCAmelCase_	)
																								accelerator.save_state(lowerCAmelCase_	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin	)	)	)
								accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used	)	)
								accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked	)	)
								accelerator.print(
								    '''Total Peak Memory consumed during the train (max): {}'''.format(
								        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
												    }	,				step=lowerCAmelCase_	,				)

								# New Code #
								# context manager to track the peak memory usage during the evaluation
								with TorchTracemalloc() as tracemalloc:
												model.eval()
												for step, batch in enumerate(lowerCAmelCase_	):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device	)
																with torch.no_grad():
																				lowerCAmelCase_	: Union[str, Any]      			=			model(**lowerCAmelCase_	)
																lowerCAmelCase_	: Any      			=			outputs.logits.argmax(dim=-1	)
																lowerCAmelCase_	: Optional[Any]      			=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
																metric.add_batch(
																    predictions=lowerCAmelCase_	,				references=lowerCAmelCase_	,				)

												lowerCAmelCase_	: Union[str, Any]      			=			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""	,				lowerCAmelCase_	)
												if args.with_tracking:
																accelerator.log(
																    {
																        '''accuracy''': eval_metric['''accuracy'''],
																        '''f1''': eval_metric['''f1'''],
																        '''train_loss''': total_loss.item() / len(lowerCAmelCase_	),
																    }	,				step=lowerCAmelCase_	,				)

												if checkpointing_steps == "epoch":
																lowerCAmelCase_	: int      			=			f"""epoch_{epoch}"""
																if args.output_dir is not None:
																				lowerCAmelCase_	: List[str]      			=			os.path.join(args.output_dir	,				lowerCAmelCase_	)
																accelerator.save_state(lowerCAmelCase_	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin	)	)	)
								accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used	)	)
								accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked	)	)
								accelerator.print(
								    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin	),
												    }	,				step=lowerCAmelCase_	,				)

				if args.with_tracking:
								accelerator.end_training()



def      lowerCAmelCase	(						)->	Tuple:
				lowerCAmelCase_	: Union[str, Any]      			=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
				parser.add_argument(
				    '''--mixed_precision'''	,				type=lowerCAmelCase_	,				default=lowerCAmelCase_	,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,				help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''	,				)
				parser.add_argument('''--cpu'''	,				action='''store_true'''	,				help='''If passed, will train on the CPU.'''	)
				parser.add_argument(
				    '''--checkpointing_steps'''	,				type=lowerCAmelCase_	,				default=lowerCAmelCase_	,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,				)
				parser.add_argument(
				    '''--resume_from_checkpoint'''	,				type=lowerCAmelCase_	,				default=lowerCAmelCase_	,				help='''If the training should continue from a checkpoint folder.'''	,				)
				parser.add_argument(
				    '''--with_tracking'''	,				action='''store_true'''	,				help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,				)
				parser.add_argument(
				    '''--output_dir'''	,				type=lowerCAmelCase_	,				default='''.'''	,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,				)
				parser.add_argument(
				    '''--logging_dir'''	,				type=lowerCAmelCase_	,				default='''logs'''	,				help='''Location on where to store experiment tracking logs`'''	,				)
				parser.add_argument(
				    '''--model_name_or_path'''	,				type=lowerCAmelCase_	,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,				required=lowerCAmelCase_	,				)
				lowerCAmelCase_	: List[str]      			=			parser.parse_args()
				lowerCAmelCase_	: int      			=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCAmelCase_	,				lowerCAmelCase_	)


if __name__ == "__main__":
						main()



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

_UpperCAmelCase     :					Optional[Any]       ="""
    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 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      Optional[Any]:
										super().__init__()

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




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										if latents is None:
														lowerCAmelCase_	: Dict      			=			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}"""							)
														lowerCAmelCase_	: Dict      			=			latents.to(__lowercase							)

										lowerCAmelCase_	: Tuple      			=			latents * scheduler.init_noise_sigma
										return latents




						def   lowercase_	(			self				,  __lowercase=0							)   ->      List[Any]:
										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

										lowerCAmelCase_	: Tuple      			=			torch.device(f"""cuda:{gpu_id}"""							)

										lowerCAmelCase_	: Tuple      			=			[self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(__lowercase				,  __lowercase							)




						def   lowercase_	(			self				,  __lowercase=0							)   ->      Optional[int]:
										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_	: Union[str, Any]      			=			torch.device(f"""cuda:{gpu_id}"""							)

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

										lowerCAmelCase_	: str      			=			None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
														lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			cpu_offload_with_hook(__lowercase				,  __lowercase				,  prev_module_hook=__lowercase							)

										if self.safety_checker is not None:
														lowerCAmelCase_			,					lowerCAmelCase_	: Optional[Any]      			=			cpu_offload_with_hook(self.safety_checker				,  __lowercase				,  prev_module_hook=__lowercase							)

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




						@property
						def   lowercase_	(			self							)   ->      Optional[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(__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   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      Dict:
										len(__lowercase							) if isinstance(__lowercase				,  __lowercase							) else 1

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

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: 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}"""							)

										lowerCAmelCase_	: Any      			=			self.text_encoder(text_input_ids.to(__lowercase							)							)
										lowerCAmelCase_	: Dict      			=			text_encoder_output.text_embeds

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

										if do_classifier_free_guidance:
														lowerCAmelCase_	: Optional[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
														lowerCAmelCase_	: str      			=			torch.cat([negative_prompt_embeds, prompt_embeds]							)

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

										return prompt_embeds




						@torch.no_grad()
						@replace_example_docstring(__lowercase							)
						def __call__(			self				,  __lowercase				,  __lowercase = 1				,  __lowercase = 2_5				,  __lowercase = None				,  __lowercase = None				,  __lowercase = 4.0				,  __lowercase = 6_4				,  __lowercase = "pil"				,  __lowercase = True				,  )   ->      Tuple:

										if isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: List[str]      			=			1
										elif isinstance(__lowercase				,  __lowercase							):
														lowerCAmelCase_	: Optional[int]      			=			len(__lowercase							)
										else:
														raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__lowercase							)}"""							)

										lowerCAmelCase_	: Union[str, Any]      			=			self._execution_device

										lowerCAmelCase_	: Dict      			=			batch_size * num_images_per_prompt

										lowerCAmelCase_	: List[str]      			=			guidance_scale > 1.0
										lowerCAmelCase_	: List[Any]      			=			self._encode_prompt(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)

										# prior

										self.scheduler.set_timesteps(__lowercase				,  device=__lowercase							)
										lowerCAmelCase_	: Any      			=			self.scheduler.timesteps

										lowerCAmelCase_	: Union[str, Any]      			=			self.prior.config.num_embeddings
										lowerCAmelCase_	: Dict      			=			self.prior.config.embedding_dim

										lowerCAmelCase_	: List[str]      			=			self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim)				,  prompt_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
										lowerCAmelCase_	: Tuple      			=			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
														lowerCAmelCase_	: int      			=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
														lowerCAmelCase_	: str      			=			self.scheduler.scale_model_input(__lowercase				,  __lowercase							)

														lowerCAmelCase_	: List[str]      			=			self.prior(
														    __lowercase				,  timestep=__lowercase				,  proj_embedding=__lowercase				,  ).predicted_image_embedding

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

														lowerCAmelCase_	: Optional[int]      			=			self.scheduler.step(
														    __lowercase				,  timestep=__lowercase				,  sample=__lowercase				,  ).prev_sample

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

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

										lowerCAmelCase_	: List[str]      			=			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}"""							)

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

										if output_type == "pil":
														lowerCAmelCase_	: Tuple      			=			[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							)