
import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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[str]    =			32


def  A			(   lowercase       ,			lowercase = 16					)  ->    int:
		'''simple docstring'''

		UpperCamelCase            =	AutoTokenizer.from_pretrained('bert-base-cased'					)
		UpperCamelCase            =	load_dataset('glue'       ,			'mrpc'					)

		def tokenize_function(lowercase					):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase            =	tokenizer(examples['sentence1']       ,			examples['sentence2']       ,			truncation=_lowercase       ,			max_length=_lowercase					)
				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():
				UpperCamelCase            =	datasets.map(
				    _lowercase       ,			batched=_lowercase       ,			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
		UpperCamelCase            =	tokenized_datasets.rename_column('label'       ,			'labels'					)

		def collate_fn(lowercase					):
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						UpperCamelCase            =	16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase            =	8
				else:
						UpperCamelCase            =	None

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

		# Instantiate dataloaders.
		UpperCamelCase            =	DataLoader(
		    tokenized_datasets['train']       ,			shuffle=_lowercase       ,			collate_fn=_lowercase       ,			batch_size=_lowercase					)
		UpperCamelCase            =	DataLoader(
		    tokenized_datasets['validation']       ,			shuffle=_lowercase       ,			collate_fn=_lowercase       ,			batch_size=_lowercase					)

		return train_dataloader, eval_dataloader


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

					_UpperCAmelCase   :					List[Any]    =			mocked_dataloaders  # noqa: F811


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

		if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,			_lowercase					) == "1":
				UpperCamelCase            =	2
		# Initialize accelerator
		UpperCamelCase            =	Accelerator(cpu=args.cpu       ,			mixed_precision=args.mixed_precision					)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase            =	config['''lr''']
		UpperCamelCase            =	int(config['num_epochs']					)
		UpperCamelCase            =	int(config['seed']					)
		UpperCamelCase            =	int(config['batch_size']					)

		UpperCamelCase            =	evaluate.load('glue'       ,			'mrpc'					)

		# New Code #
		# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
		# half each time. From this, we can calculate the number of gradient accumulation steps needed
		# and modify the Accelerator object as a result
		@find_executable_batch_size(starting_batch_size=int(_lowercase					)					)
		def inner_training_loop(lowercase					):
				# Since we need to modify the outside accelerator object, we need to bring it
				# to the local scope
				nonlocal accelerator

				# We can calculate the number of gradient accumulation steps based on the current
				# batch size vs the starting batch size
				UpperCamelCase            =	observed_batch_size // batch_size

				# And then set it in the Accelerator directly:
				UpperCamelCase            =	num_gradient_accumulation_steps

				# Next we need to free all of the stored model references in the Accelerator each time
				accelerator.free_memory()

				# And set the seed so our results are reproducable each reset
				set_seed(_lowercase					)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase            =	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,			return_dict=_lowercase					)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				UpperCamelCase            =	model.to(accelerator.device					)

				# Instantiate optimizer
				UpperCamelCase            =	AdamW(params=model.parameters()       ,			lr=_lowercase					)
				UpperCamelCase            =	get_dataloaders(_lowercase       ,			_lowercase					)

				# Instantiate scheduler
				UpperCamelCase            =	get_linear_schedule_with_warmup(
				    optimizer=_lowercase       ,			num_warmup_steps=100       ,			num_training_steps=(len(_lowercase					) * num_epochs)       ,			)

				# Prepare everything
				# 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.
				UpperCamelCase            =	accelerator.prepare(
				    _lowercase       ,			_lowercase       ,			_lowercase       ,			_lowercase       ,			_lowercase					)

				# Now we train the model
				for epoch in range(_lowercase					):
						model.train()
						for step, batch in enumerate(_lowercase					):
								# And perform gradient accumulation
								with accelerator.accumulate(_lowercase					):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device					)
										UpperCamelCase            =	model(**_lowercase					)
										UpperCamelCase            =	outputs.loss
										accelerator.backward(_lowercase					)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(_lowercase					):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device					)
								with torch.no_grad():
										UpperCamelCase            =	model(**_lowercase					)
								UpperCamelCase            =	outputs.logits.argmax(dim=-1					)
								UpperCamelCase            =	accelerator.gather_for_metrics((predictions, batch['labels'])					)
								metric.add_batch(
								    predictions=_lowercase       ,			references=_lowercase       ,			)

						UpperCamelCase            =	metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''       ,			_lowercase					)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()


def  A			(   )  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	argparse.ArgumentParser(description='Simple example of training script.'					)
		parser.add_argument(
		    '--mixed_precision'       ,			type=_lowercase       ,			default=_lowercase       ,			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.'					)
		UpperCamelCase            =	parser.parse_args()
		# New Code #
		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
		UpperCamelCase            =	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
		training_function(_lowercase       ,			_lowercase					)


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  )






