


"""simple docstring"""


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A  :     List[str]    					=    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 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(
										    speech_model=__a					,     speech_processor=__a					,     vae=__a					,     text_encoder=__a					,     tokenizer=__a					,     unet=__a					,     scheduler=__a					,     feature_extractor=__a					,     )

							def     snake_case       (  self					,     __a = "auto"			):
										if slice_size == "auto":
													__lowerCAmelCase       					=    self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(__a			)

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



							@torch.no_grad()
							def __call__(  self					,     __a					,     __a=1_60_00					,     __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.speech_processor.feature_extractor(
										    __a					,     return_tensors="pt"					,     sampling_rate=__a			).input_features.to(self.device			)
										__lowerCAmelCase       					=    self.speech_model.generate(__a					,     max_length=48_00_00			)

										__lowerCAmelCase       					=    self.speech_processor.tokenizer.batch_decode(__a					,     skip_special_tokens=__a					,     normalize=__a			)[
										    0
										]

										if isinstance(__a					,     __a			):
													__lowerCAmelCase       					=    1
										elif isinstance(__a					,     __a			):
													__lowerCAmelCase       					=    len(__a			)
										else:
													raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__a			)}"			)

										if height % 8 != 0 or width % 8 != 0:
													raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."			)

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(__a					,     __a			) or callback_steps <= 0)
										):
													raise ValueError(
													    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
													    f" {type(__a			)}."			)

										# get prompt text embeddings
										__lowerCAmelCase       					=    self.tokenizer(
										    __a					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     return_tensors="pt"					,     )
										__lowerCAmelCase       					=    text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													__lowerCAmelCase       					=    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
													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       					=    text_input_ids[:, : self.tokenizer.model_max_length]
										__lowerCAmelCase       					=    self.text_encoder(text_input_ids.to(self.device			)			)[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    text_embeddings.shape
										__lowerCAmelCase       					=    text_embeddings.repeat(1					,     __a					,     1			)
										__lowerCAmelCase       					=    text_embeddings.view(bs_embed * num_images_per_prompt					,     __a					,     -1			)

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										__lowerCAmelCase       					=    guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
													__lowerCAmelCase       					=    42
													if negative_prompt is None:
																__lowerCAmelCase       					=    [""] * batch_size
													elif type(__a			) is not type(__a			):
																raise TypeError(
																    f"`negative_prompt` should be the same type to `prompt`, but got {type(__a			)} !="
																    f" {type(__a			)}."			)
													elif isinstance(__a					,     __a			):
																__lowerCAmelCase       					=    [negative_prompt]
													elif batch_size != len(__a			):
																raise ValueError(
																    f"`negative_prompt`: {negative_prompt} has batch size {len(__a			)}, but `prompt`:"
																    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
																    " the batch size of `prompt`."			)
													else:
																__lowerCAmelCase       					=    negative_prompt

													__lowerCAmelCase       					=    text_input_ids.shape[-1]
													__lowerCAmelCase       					=    self.tokenizer(
													    __a					,     padding="max_length"					,     max_length=__a					,     truncation=__a					,     return_tensors="pt"					,     )
													__lowerCAmelCase       					=    self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													__lowerCAmelCase       					=    uncond_embeddings.shape[1]
													__lowerCAmelCase       					=    uncond_embeddings.repeat(1					,     __a					,     1			)
													__lowerCAmelCase       					=    uncond_embeddings.view(batch_size * num_images_per_prompt					,     __a					,     -1			)

													# 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       					=    torch.cat([uncond_embeddings, text_embeddings]			)

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										__lowerCAmelCase       					=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										__lowerCAmelCase       					=    text_embeddings.dtype
										if latents is None:
													if self.device.type == "mps":
																# randn does not exist on mps
																__lowerCAmelCase       					=    torch.randn(__a					,     generator=__a					,     device="cpu"					,     dtype=__a			).to(
																    self.device			)
													else:
																__lowerCAmelCase       					=    torch.randn(__a					,     generator=__a					,     device=self.device					,     dtype=__a			)
										else:
													if latents.shape != latents_shape:
																raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"			)
													__lowerCAmelCase       					=    latents.to(self.device			)

										# set timesteps
										self.scheduler.set_timesteps(__a			)

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										__lowerCAmelCase       					=    self.scheduler.timesteps.to(self.device			)

										# scale the initial noise by the standard deviation required by the scheduler
										__lowerCAmelCase       					=    latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										__lowerCAmelCase       					=    "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
										__lowerCAmelCase       					=    {}
										if accepts_eta:
													__lowerCAmelCase       					=    eta

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

													# predict the noise residual
													__lowerCAmelCase       					=    self.unet(__a					,     __a					,     encoder_hidden_states=__a			).sample

													# perform guidance
													if do_classifier_free_guidance:
																__lowerCAmelCase					,			__lowerCAmelCase       					=    noise_pred.chunk(2			)
																__lowerCAmelCase       					=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													__lowerCAmelCase       					=    self.scheduler.step(__a					,     __a					,     __a					,     **__a			).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																callback(__a					,     __a					,     __a			)

										__lowerCAmelCase       					=    1 / 0.1_8_2_1_5 * latents
										__lowerCAmelCase       					=    self.vae.decode(__a			).sample

										__lowerCAmelCase       					=    (image / 2 + 0.5).clamp(0					,     1			)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase       					=    image.cpu().permute(0					,     2					,     3					,     1			).float().numpy()

										if output_type == "pil":
													__lowerCAmelCase       					=    self.numpy_to_pil(__a			)

										if not return_dict:
													return image

										return StableDiffusionPipelineOutput(images=__a					,     nsfw_content_detected=__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()




