
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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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						      =				16
_A						      =				32



def 		lowercase_    (     A__				,	A__ = 16	)							->							Optional[int]:
    """simple docstring"""

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

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

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

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

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

    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						      =				mocked_dataloaders  # noqa: F811



def 		lowercase_    (     A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

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

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

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

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

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

    # Instantiate optimizer
    snake_case				  =      AdamW(params=model.parameters()				,	lr=A__	)

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

    # Now we train the model
    for epoch in range(A__	):
        model.train()
        for step, batch in enumerate(A__	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            snake_case				  =      model(**A__	)
            snake_case				  =      outputs.loss
            snake_case				  =      loss / gradient_accumulation_steps
            accelerator.backward(A__	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        snake_case				  =      0
        for step, batch in enumerate(A__	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            with torch.no_grad():
                snake_case				  =      model(**A__	)
            snake_case				  =      outputs.logits.argmax(dim=-1	)
            snake_case  ,							snake_case				  =      accelerator.gather((predictions, batch["labels"])	)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(A__	) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    snake_case				  =      predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    snake_case				  =      references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=A__				,	references=A__				,	)

        snake_case				  =      metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'				,	A__	)



def 		lowercase_    (     )							->							Any:
    """simple docstring"""

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


if __name__ == "__main__":
   main()



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	KandinskyVaaControlnetImgaImgPipeline
     UpperCAmelCase__       :							Optional[Any]     =	["image_embeds", "negative_image_embeds", "image", "hint"]
     UpperCAmelCase__       :							List[Any]     =	["image_embeds", "negative_image_embeds", "image", "hint"]
     UpperCAmelCase__       :							List[str]     =	[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__       :							List[str]     =	False





     @property
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Dict:
         return 3_2





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[int]:
         return 3_2





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Union[str, Any]:
         return self.time_input_dim





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
         return self.time_input_dim * 4





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		int:
         return 1_0_0





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Optional[Any]:
         torch.manual_seed(0     )

         snake_case				  =      {
             "in_channels": 8,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image_hint",
             "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
             "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
             "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "layers_per_block": 1,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         snake_case				  =      UNetaDConditionModel(**_A     )
         return model





     @property
     def 	UpperCAmelCase(self   :					Any     )		->		List[str]:
         return {
             "block_out_channels": [3_2, 3_2, 6_4, 6_4],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "AttnDownEncoderBlock2D",
             ],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 1_2,
             "out_channels": 3,
             "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         torch.manual_seed(0     )
         snake_case				  =      VQModel(**self.dummy_movq_kwargs     )
         return model





     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         snake_case				  =      self.dummy_unet
         snake_case				  =      self.dummy_movq

         snake_case				  =      {
             "num_train_timesteps": 1_0_0_0,
             "beta_schedule": "linear",
             "beta_start": 0.0_00_85,
             "beta_end": 0.0_12,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         snake_case				  =      DDIMScheduler(**_A     )

         snake_case				  =      {
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components





     def 	UpperCAmelCase(self   :					Any       ,					_A   :					Any       ,					_A   :					Optional[Any]=0     )		->		List[Any]:
         snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(_A     )     ).to(_A     )
         snake_case				  =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1     )     ).to(
             _A     )
         # create init_image
         snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(_A     )     ).to(_A     )
         snake_case				  =      image.cpu().permute(0       ,					2       ,					3       ,					1     )[0]
         snake_case				  =      Image.fromarray(np.uinta(_A     )     ).convert("RGB"     ).resize((2_5_6, 2_5_6)     )
         # create hint
         snake_case				  =      floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(_A     )     ).to(_A     )

         if str(_A     ).startswith("mps"     ):
             snake_case				  =      torch.manual_seed(_A     )
         else:
             snake_case				  =      torch.Generator(device=_A     ).manual_seed(_A     )
         snake_case				  =      {
             "image": init_image,
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 6_4,
             "width": 6_4,
             "num_inference_steps": 1_0,
             "guidance_scale": 7.0,
             "strength": 0.2,
             "output_type": "np",
         }
         return inputs





     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         snake_case				  =      "cpu"

         snake_case				  =      self.get_dummy_components()

         snake_case				  =      self.pipeline_class(**_A     )
         snake_case				  =      pipe.to(_A     )

         pipe.set_progress_bar_config(disable=_A     )

         snake_case				  =      pipe(**self.get_dummy_inputs(_A     )     )
         snake_case				  =      output.images

         snake_case				  =      pipe(
             **self.get_dummy_inputs(_A     )       ,					return_dict=_A       ,					)[0]

         snake_case				  =      image[0, -3:, -3:, -1]
         snake_case				  =      image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case				  =      np.array(
             [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]     )
         assert (
             np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
         ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
         ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					int     )		->		Optional[int]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 	UpperCAmelCase(self   :					int     )		->		Tuple:
         snake_case				  =      load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"     )

         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
         snake_case				  =      init_image.resize((5_1_2, 5_1_2)     )

         snake_case				  =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png"     )
         snake_case				  =      torch.from_numpy(np.array(_A     )     ).float() / 2_55.0
         snake_case				  =      hint.permute(2       ,					0       ,					1     ).unsqueeze(0     )

         snake_case				  =      "A robot, 4k photo"

         snake_case				  =      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"       ,					torch_dtype=torch.floataa     )
         pipe_prior.to(_A     )

         snake_case				  =      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,					torch_dtype=torch.floataa     )
         snake_case				  =      pipeline.to(_A     )

         pipeline.set_progress_bar_config(disable=_A     )

         snake_case				  =      torch.Generator(device="cpu"     ).manual_seed(0     )

         snake_case  ,							snake_case				  =      pipe_prior(
             _A       ,					image=_A       ,					strength=0.85       ,					generator=_A       ,					negative_prompt=""       ,					).to_tuple()

         snake_case				  =      pipeline(
             image=_A       ,					image_embeds=_A       ,					negative_image_embeds=_A       ,					hint=_A       ,					generator=_A       ,					num_inference_steps=1_0_0       ,					height=5_1_2       ,					width=5_1_2       ,					strength=0.5       ,					output_type="np"       ,					)

         snake_case				  =      output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(_A       ,					_A     )

