




import argparse
import os

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


########################################################################
# 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)
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       16
SCREAMING_SNAKE_CASE_						:					Tuple			   =       32
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    # Initialize accelerator
    if args.with_tracking:
        a__      =       Accelerator(
            cpu=args.cpu   ,    mixed_precision=args.mixed_precision   ,    log_with="""all"""   ,    project_dir=args.project_dir		)
    else:
        a__      =       Accelerator(cpu=args.cpu   ,    mixed_precision=args.mixed_precision		)

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

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        a__      =       os.path.split(UpperCAmelCase_		)[-1].split("""."""		)[0]
        accelerator.init_trackers(UpperCAmelCase_   ,    UpperCAmelCase_		)

    a__      =       AutoTokenizer.from_pretrained("""bert-base-cased"""		)
    a__      =       load_dataset("""glue"""   ,    """mrpc"""		)
    a__      =       evaluate.load("""glue"""   ,    """mrpc"""		)

    def tokenize_function(UpperCAmelCase_		):
        # max_length=None => use the model max length (it's actually the default)
        a__      =       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():
        a__      =       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
    a__      =       tokenized_datasets.rename_column("""label"""   ,    """labels"""		)

    # If the batch size is too big we use gradient accumulation
    a__      =       1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        a__      =       batch_size // MAX_GPU_BATCH_SIZE
        a__      =       MAX_GPU_BATCH_SIZE

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

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

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

    set_seed(UpperCAmelCase_		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    a__      =       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).
    a__      =       model.to(accelerator.device		)

    # Instantiate optimizer
    a__      =       AdamW(params=model.parameters()   ,    lr=UpperCAmelCase_		)

    # Instantiate scheduler
    a__      =       get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase_   ,    num_warmup_steps=100   ,    num_training_steps=(len(UpperCAmelCase_		) * num_epochs) // gradient_accumulation_steps   ,    )

    # 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.
    a__ ,		a__ ,		a__ ,		a__ ,		a__      =       accelerator.prepare(
        UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		)

    # We need to keep track of how many total steps we have iterated over
    a__      =       0
    # We also need to keep track of the stating epoch so files are named properly
    a__      =       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		)
            a__      =       os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            a__      =       [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            a__      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        a__      =       os.path.splitext(UpperCAmelCase_		)[0]

        if "epoch" in training_difference:
            a__      =       int(training_difference.replace("""epoch_"""   ,    """"""		)		) + 1
            a__      =       None
        else:
            a__      =       int(training_difference.replace("""step_"""   ,    """"""		)		)
            a__      =       resume_step // len(UpperCAmelCase_		)
            resume_step -= starting_epoch * len(UpperCAmelCase_		)

    # Now we train the model
    for epoch in range(UpperCAmelCase_   ,    UpperCAmelCase_		):
        model.train()
        if args.with_tracking:
            a__      =       0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            a__      =       accelerator.skip_first_batches(UpperCAmelCase_   ,    UpperCAmelCase_		)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            a__      =       train_dataloader
        for step, batch in enumerate(UpperCAmelCase_		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            a__      =       model(**UpperCAmelCase_		)
            a__      =       outputs.loss
            a__      =       loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(UpperCAmelCase_		)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_		):
                a__      =       F"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        a__      =       os.path.join(args.output_dir   ,    UpperCAmelCase_		)
                    accelerator.save_state(UpperCAmelCase_		)

        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():
                a__      =       model(**UpperCAmelCase_		)
            a__      =       outputs.logits.argmax(dim=-1		)
            a__ ,		a__      =       accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
            metric.add_batch(
                predictions=UpperCAmelCase_   ,    references=UpperCAmelCase_   ,    )

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

        if checkpointing_steps == "epoch":
            a__      =       F"""epoch_{epoch}"""
            if args.output_dir is not None:
                a__      =       os.path.join(args.output_dir   ,    UpperCAmelCase_		)
            accelerator.save_state(UpperCAmelCase_		)

    if args.with_tracking:
        accelerator.end_training()
def        __lowerCAmelCase ():
    a__      =       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."""		)
    parser.add_argument(
        """--checkpointing_steps"""   ,    type=UpperCAmelCase_   ,    default=UpperCAmelCase_   ,    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=UpperCAmelCase_   ,    default=UpperCAmelCase_   ,    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=UpperCAmelCase_   ,    default="""."""   ,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,    )
    parser.add_argument(
        """--project_dir"""   ,    type=UpperCAmelCase_   ,    default="""logs"""   ,    help="""Location on where to store experiment tracking logs` and relevent project information"""   ,    )
    a__      =       parser.parse_args()
    a__      =       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCAmelCase_   ,    UpperCAmelCase_		)


if __name__ == "__main__":
 main()












from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       getLogger(__name__)


SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       4
SCREAMING_SNAKE_CASE_						:					List[str]			   =       7

SCREAMING_SNAKE_CASE_						:					List[str]			   =       {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}
def        __lowerCAmelCase (UpperCAmelCase_		):
    if isinstance(UpperCAmelCase_   ,    torch.Tensor		):
        return image
    elif isinstance(UpperCAmelCase_   ,    PIL.Image.Image		):
        a__      =       [image]

    if isinstance(image[0]   ,    PIL.Image.Image		):
        a__ ,		a__      =       image[0].size
        a__ ,		a__      =       (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

        a__      =       [np.array(i.resize((w, h)		)		)[None, :] for i in image]
        a__      =       np.concatenate(UpperCAmelCase_   ,    axis=0		)
        a__      =       np.array(UpperCAmelCase_		).astype(np.floataa		) / 2_5_5.0
        a__      =       image.transpose(0   ,    3   ,    1   ,    2		)
        a__      =       2.0 * image - 1.0
        a__      =       torch.from_numpy(UpperCAmelCase_		)
    elif isinstance(image[0]   ,    torch.Tensor		):
        a__      =       torch.cat(UpperCAmelCase_   ,    dim=0		)

    return image







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       int						,				A :       OnnxRuntimeModel						,				A :       OnnxRuntimeModel						,				A :       Any						,				A :       OnnxRuntimeModel						,				A :       DDPMScheduler						,				A :       Any						,				A :       int = 3_50						,				)			->			List[Any]:






         """simple docstring"""
         super().__init__(
             vae=A						,				text_encoder=A						,				tokenizer=A						,				unet=A						,				low_res_scheduler=A						,				scheduler=A						,				safety_checker=A						,				feature_extractor=A						,				watermarker=A						,				max_noise_level=A						,				)

     def __call__(      self :       Optional[int]						,				A :       Union[str, List[str]]						,				A :       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						,				A :       int = 75						,				A :       float = 9.0						,				A :       int = 20						,				A :       Optional[Union[str, List[str]]] = None						,				A :       Optional[int] = 1						,				A :       float = 0.0						,				A :       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[str] = "pil"						,				A :       bool = True						,				A :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,				A :       Optional[int] = 1						,				)			->			Tuple:






         """simple docstring"""
         self.check_inputs(A						,				A						,				A						,				A    )

         # 2. Define call parameters
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

         a__      =       self._execution_device
         # 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.
         a__      =       guidance_scale > 1.0

         # 3. Encode input prompt
         a__      =       self._encode_prompt(
             A						,				A						,				A						,				A						,				A						,				prompt_embeds=A						,				negative_prompt_embeds=A						,				)

         a__      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

         # 4. Preprocess image
         a__      =       preprocess(A    )
         a__      =       image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(A						,				device=A    )
         a__      =       self.scheduler.timesteps

         # 5. Add noise to image
         a__      =       torch.tensor([noise_level]						,				dtype=torch.long						,				device=A    )
         a__      =       torch.randn(image.shape						,				generator=A						,				device=A						,				dtype=A    )
         a__      =       self.low_res_scheduler.add_noise(A						,				A						,				A    )

         a__      =       2 if do_classifier_free_guidance else 1
         a__      =       np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
         a__      =       np.concatenate([noise_level] * image.shape[0]    )

         # 6. Prepare latent variables
         a__ ,		a__      =       image.shape[2:]
         a__      =       self.prepare_latents(
             batch_size * num_images_per_prompt						,				A						,				A						,				A						,				A						,				A						,				A						,				A						,				)

         # 7. Check that sizes of image and latents match
         a__      =       image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                 f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
                 f""" `num_channels_image`: {num_channels_image} """
                 f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
                 """ `pipeline.unet` or your `image` input."""    )

         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         a__      =       self.prepare_extra_step_kwargs(A						,				A    )

         a__      =       next(
             (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")						,				"""tensor(float)"""    )
         a__      =       ORT_TO_NP_TYPE[timestep_dtype]

         # 9. Denoising loop
         a__      =       len(A    ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=A    ) as progress_bar:
             for i, t in enumerate(A    ):
                 # expand the latents if we are doing classifier free guidance
                 a__      =       np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

                 # concat latents, mask, masked_image_latents in the channel dimension
                 a__      =       self.scheduler.scale_model_input(A						,				A    )
                 a__      =       np.concatenate([latent_model_input, image]						,				axis=1    )

                 # timestep to tensor
                 a__      =       np.array([t]						,				dtype=A    )

                 # predict the noise residual
                 a__      =       self.unet(
                     sample=A						,				timestep=A						,				encoder_hidden_states=A						,				class_labels=noise_level.astype(np.intaa    )						,				)[0]

                 # perform guidance
                 if do_classifier_free_guidance:
                     a__ ,		a__      =       np.split(A						,				2    )
                 a__      =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 a__      =       self.scheduler.step(
                     torch.from_numpy(A    )						,				A						,				A						,				**A    ).prev_sample

                 # call the callback, if provided
                 if i == len(A    ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(A						,				A						,				A    )

        # 10. Post-processing
         a__      =       self.decode_latents(latents.float()    )

         # 11. Convert to PIL
         if output_type == "pil":
             a__      =       self.numpy_to_pil(A    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Dict    )			->			List[str]:






         """simple docstring"""
         a__      =       1 / 0.0_8_3_3_3 * latents
         a__      =       self.vae(latent_sample=A    )[0]
         a__      =       np.clip(image / 2 + 0.5						,				0						,				1    )
         a__      =       image.transpose((0, 2, 3, 1)    )
         return image






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       Union[str, List[str]]						,				A :       str						,				A :       Optional[int]						,				A :       bool						,				A :       Optional[str]						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[torch.FloatTensor] = None						,				)			->			Tuple:






         """simple docstring"""
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

         if prompt_embeds is None:
             a__      =       self.tokenizer(
                 A						,				padding="""max_length"""						,				max_length=self.tokenizer.model_max_length						,				truncation=A						,				return_tensors="""pt"""						,				)
             a__      =       text_inputs.input_ids
             a__      =       self.tokenizer(A						,				padding="""longest"""						,				return_tensors="""pt"""    ).input_ids

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

             # no positional arguments to text_encoder
             a__      =       self.text_encoder(
                 input_ids=text_input_ids.int().to(A    )						,				)
             a__      =       prompt_embeds[0]

         a__ ,		a__ ,		a__      =       prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         a__      =       prompt_embeds.repeat(1						,				A    )
         a__      =       prompt_embeds.reshape(bs_embed * num_images_per_prompt						,				A						,				-1    )

         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             a__      =       42
             if negative_prompt is None:
                 a__      =       [""""""] * 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    ):
                 a__      =       [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:
                 a__      =       negative_prompt

             a__      =       text_input_ids.shape[-1]
             a__      =       self.tokenizer(
                 A						,				padding="""max_length"""						,				max_length=A						,				truncation=A						,				return_tensors="""pt"""						,				)

             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None

             a__      =       self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(A    )						,				)
             a__      =       uncond_embeddings[0]

         if do_classifier_free_guidance:
             a__      =       uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             a__      =       uncond_embeddings.repeat(1						,				A    )
             a__      =       uncond_embeddings.reshape(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
             a__      =       np.concatenate([uncond_embeddings, prompt_embeds]    )

         return prompt_embeds






