


"""simple docstring"""


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


A  :     Any									=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     ):
										super().__init__()

										if hasattr(scheduler.config					,     "steps_offset"			) and scheduler.config.steps_offset != 1:
													__lowerCAmelCase       					=    (
													    f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
													    f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
													    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
													    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
													    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
													    " file"
													)
													deprecate("steps_offset!=1"					,     "1.0.0"					,     __a					,     standard_warn=__a			)
													__lowerCAmelCase       					=    dict(scheduler.config			)
													__lowerCAmelCase       					=    1
													__lowerCAmelCase       					=    FrozenDict(__a			)

										if hasattr(scheduler.config					,     "skip_prk_steps"			) and scheduler.config.skip_prk_steps is False:
													__lowerCAmelCase       					=    (
													    f"The configuration file of this scheduler: {scheduler} has not set the configuration"
													    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
													    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
													    " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
													    " Hub, it would be very nice if you could open a Pull request for the"
													    " `scheduler/scheduler_config.json` file"
													)
													deprecate("skip_prk_steps not set"					,     "1.0.0"					,     __a					,     standard_warn=__a			)
													__lowerCAmelCase       					=    dict(scheduler.config			)
													__lowerCAmelCase       					=    True
													__lowerCAmelCase       					=    FrozenDict(__a			)

										if safety_checker is None:
													logger.warning(
													    f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
													    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
													    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
													    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
													    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
													    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."			)

										self.register_modules(
										    segmentation_model=__a					,     segmentation_processor=__a					,     vae=__a					,     text_encoder=__a					,     tokenizer=__a					,     unet=__a					,     scheduler=__a					,     safety_checker=__a					,     feature_extractor=__a					,     )

							def     snake_case       (  self					,     __a = "auto"			):
										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													__lowerCAmelCase       					=    self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(__a			)

							def     snake_case       (  self			):
										self.enable_attention_slicing(__a			)

							def     snake_case       (  self			):
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError("Please install accelerate via `pip install accelerate`"			)

										__lowerCAmelCase       					=    torch.device("cuda"			)

										for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
													if cpu_offloaded_model is not None:
																cpu_offload(__a					,     __a			)

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def     snake_case       (  self			):
										if self.device != torch.device("meta"			) or not hasattr(self.unet					,     "_hf_hook"			):
													return self.device
										for module in self.unet.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



							@torch.no_grad()
							def __call__(  self					,     __a					,     __a					,     __a					,     __a = 5_12					,     __a = 5_12					,     __a = 50					,     __a = 7.5					,     __a = None					,     __a = 1					,     __a = 0.0					,     __a = None					,     __a = None					,     __a = "pil"					,     __a = True					,     __a = None					,     __a = 1					,     **__a					,     ):
										__lowerCAmelCase       					=    self.segmentation_processor(
										    text=[text]					,     images=[image]					,     padding="max_length"					,     return_tensors="pt"			).to(self.device			)
										__lowerCAmelCase       					=    self.segmentation_model(**__a			)
										__lowerCAmelCase       					=    torch.sigmoid(outputs.logits			).cpu().detach().unsqueeze(-1			).numpy()
										__lowerCAmelCase       					=    self.numpy_to_pil(__a			)[0].resize(image.size			)

										# Run inpainting pipeline with the generated mask
										__lowerCAmelCase       					=    StableDiffusionInpaintPipeline(
										    vae=self.vae					,     text_encoder=self.text_encoder					,     tokenizer=self.tokenizer					,     unet=self.unet					,     scheduler=self.scheduler					,     safety_checker=self.safety_checker					,     feature_extractor=self.feature_extractor					,     )
										return inpainting_pipeline(
										    prompt=__a					,     image=__a					,     mask_image=__a					,     height=__a					,     width=__a					,     num_inference_steps=__a					,     guidance_scale=__a					,     negative_prompt=__a					,     num_images_per_prompt=__a					,     eta=__a					,     generator=__a					,     latents=__a					,     output_type=__a					,     return_dict=__a					,     callback=__a					,     callback_steps=__a					,     )








"""simple docstring"""


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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


A  :     Dict    					=    1_6
A  :     Optional[int]    					=    3_2


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase = 16   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    AutoTokenizer.from_pretrained("bert-base-cased"   )
			__lowerCAmelCase       					=    load_dataset("glue"					,				"mrpc"   )

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

			def collate_fn(_UpperCamelCase   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__lowerCAmelCase       					=    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       					=    16
						elif accelerator.mixed_precision != "no":
									__lowerCAmelCase       					=    8
						else:
									__lowerCAmelCase       					=    None

						return tokenizer.pad(
						    _UpperCamelCase					,				padding="longest"					,				max_length=_UpperCamelCase					,				pad_to_multiple_of=_UpperCamelCase					,				return_tensors="pt"					,				)

			# Instantiate dataloaders.
			__lowerCAmelCase       					=    DataLoader(
			    tokenized_datasets["train"]					,				shuffle=_UpperCamelCase					,				collate_fn=_UpperCamelCase					,				batch_size=_UpperCamelCase   )
			__lowerCAmelCase       					=    DataLoader(
			    tokenized_datasets["validation"]					,				shuffle=_UpperCamelCase					,				collate_fn=_UpperCamelCase					,				batch_size=_UpperCamelCase   )

			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

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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




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

			__lowerCAmelCase       					=    evaluate.load("glue"					,				"mrpc"   )

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=_UpperCamelCase   )
			def inner_training_loop(_UpperCamelCase   ):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(_UpperCamelCase   )

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

						# 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).
						__lowerCAmelCase       					=    model.to(accelerator.device   )

						# Instantiate optimizer
						__lowerCAmelCase       					=    AdamW(params=model.parameters()					,				lr=_UpperCamelCase   )
						__lowerCAmelCase					,			__lowerCAmelCase       					=    get_dataloaders(_UpperCamelCase					,				_UpperCamelCase   )

						# Instantiate scheduler
						__lowerCAmelCase       					=    get_linear_schedule_with_warmup(
						    optimizer=_UpperCamelCase					,				num_warmup_steps=100					,				num_training_steps=(len(_UpperCamelCase   ) * 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.
						__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.prepare(
						    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )

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

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

									__lowerCAmelCase       					=    metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f"epoch {epoch}:"					,				_UpperCamelCase   )

    # 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 _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    argparse.ArgumentParser(description="Simple example of training script."   )
			parser.add_argument(
			    "--mixed_precision"					,				type=_UpperCamelCase					,				default=_UpperCamelCase					,				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."   )
			__lowerCAmelCase       					=    parser.parse_args()
			__lowerCAmelCase       					=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(_UpperCamelCase					,				_UpperCamelCase   )


if __name__ == "__main__":
	main()




