


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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       (   snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: List[str]													=						KandinskyInpaintPipeline
   __UpperCAmelCase				: Any													=						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   __UpperCAmelCase				: Optional[int]													=						[
       '''prompt''',
       '''negative_prompt''',
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   __UpperCAmelCase				: Union[str, Any]													=						[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''negative_prompt''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase				: str													=						False







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       return 32







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       return self.time_input_dim







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       return self.time_input_dim * 4







   @property
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       return 100







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
       return tokenizer







   @property
   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Optional[Any]         =					MCLIPConfig(
           numDims=self.cross_attention_dim		,			transformerDimensions=self.text_embedder_hidden_size		,			hidden_size=self.text_embedder_hidden_size		,			intermediate_size=37		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=1005		,			)

       snake_case					:   List[str]         =					MultilingualCLIP(UpperCamelCase__		)
       snake_case					:   Dict         =					text_encoder.eval()

       return text_encoder







   @property
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       torch.manual_seed(0		)

       snake_case					:   List[str]         =					{
           "in_channels": 9,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "text_image",
           "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": "text_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					:   Dict         =					UNetaDConditionModel(**UpperCamelCase__		)
       return model







   @property
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }







   @property
   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					VQModel(**self.dummy_movq_kwargs		)
       return model







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   int         =					self.dummy_text_encoder
       snake_case					:   Dict         =					self.dummy_tokenizer
       snake_case					:   List[str]         =					self.dummy_unet
       snake_case					:   Union[str, Any]         =					self.dummy_movq

       snake_case					:   int         =					DDIMScheduler(
           num_train_timesteps=1000		,			beta_schedule="linear"		,			beta_start=0.00085		,			beta_end=0.012		,			clip_sample=UpperCamelCase__		,			set_alpha_to_one=UpperCamelCase__		,			steps_offset=1		,			prediction_type="epsilon"		,			thresholding=UpperCamelCase__		,			)

       snake_case					:   List[str]         =					{
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0		)			->	Any:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1		)		).to(UpperCamelCase__		)
       # create init_image
       snake_case					:   Any         =					floats_tensor((1, 3, 64, 64)		,			rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					image.cpu().permute(0		,			2		,			3		,			1		)[0]
       snake_case					:   str         =					Image.fromarray(np.uinta(UpperCamelCase__		)		).convert("RGB"		).resize((256, 256)		)
       # create mask
       snake_case					:   Union[str, Any]         =					np.ones((64, 64)		,			dtype=np.floataa		)
       snake_case					:   Union[str, Any]         =					0

       if str(UpperCamelCase__		).startswith("mps"		):
           snake_case					:   int         =					torch.manual_seed(UpperCamelCase__		)
       else:
           snake_case					:   str         =					torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
       snake_case					:   str         =					{
           "prompt": "horse",
           "image": init_image,
           "mask_image": mask,
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "num_inference_steps": 2,
           "guidance_scale": 4.0,
           "output_type": "np",
       }
       return inputs







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   List[str]         =					"cpu"

       snake_case					:   str         =					self.get_dummy_components()

       snake_case					:   str         =					self.pipeline_class(**UpperCamelCase__		)
       snake_case					:   str         =					pipe.to(UpperCamelCase__		)

       pipe.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					pipe(**self.get_dummy_inputs(UpperCamelCase__		)		)
       snake_case					:   int         =					output.images

       snake_case					:   List[Any]         =					pipe(
           **self.get_dummy_inputs(UpperCamelCase__		)		,			return_dict=UpperCamelCase__		,			)[0]

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

       print(F'image.shape {image.shape}'		)

       assert image.shape == (1, 64, 64, 3)

       snake_case					:   int         =					np.array(
           [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]		)

       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()}'







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"		)

       snake_case					:   List[str]         =					load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
       snake_case					:   Tuple         =					np.ones((768, 768)		,			dtype=np.floataa		)
       snake_case					:   Dict         =					0

       snake_case					:   Any         =					"a hat"

       snake_case					:   int         =					KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"		,			torch_dtype=torch.floataa		)
       pipe_prior.to(UpperCamelCase__		)

       snake_case					:   Dict         =					KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"		,			torch_dtype=torch.floataa		)
       snake_case					:   str         =					pipeline.to(UpperCamelCase__		)
       pipeline.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   int         =					torch.Generator(device="cpu"		).manual_seed(0		)
       snake_case      ,snake_case					:   Tuple         =					pipe_prior(
           UpperCamelCase__		,			generator=UpperCamelCase__		,			num_inference_steps=5		,			negative_prompt=""		,			).to_tuple()

       snake_case					:   Any         =					pipeline(
           UpperCamelCase__		,			image=UpperCamelCase__		,			mask_image=UpperCamelCase__		,			image_embeds=UpperCamelCase__		,			negative_image_embeds=UpperCamelCase__		,			generator=UpperCamelCase__		,			num_inference_steps=100		,			height=768		,			width=768		,			output_type="np"		,			)

       snake_case					:   List[str]         =					output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(UpperCamelCase__		,			UpperCamelCase__		)









"""simple docstring"""



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 the experiment tracking capability,
# 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
#
########################################################################

__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Union[str, Any]:


    """simple docstring"""





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

    def tokenize_function(lowercase						:						Tuple				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					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():
        snake_case					:   Any         =					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
    snake_case					:   Tuple         =					tokenized_datasets.rename_column("label"		,							"labels"				)

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

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

    # Instantiate dataloaders.
    snake_case					:   Any         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   List[str]         =					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

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						int				)			->		Tuple:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Union[str, Any]         =					2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        snake_case					:   str         =					Accelerator(
            cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with="all"		,							project_dir=args.project_dir				)
    else:
        snake_case					:   Optional[int]         =					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					:   str         =					config["lr"]
    snake_case					:   Dict         =					int(config["num_epochs"]				)
    snake_case					:   int         =					int(config["seed"]				)
    snake_case					:   Tuple         =					int(config["batch_size"]				)
    set_seed(lowercase				)

    snake_case      ,snake_case					:   List[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    snake_case					:   Optional[Any]         =					evaluate.load("glue"		,							"mrpc"				)

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   List[Any]         =					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).
    snake_case					:   str         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Optional[Any]         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * 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					:   Union[str, Any]         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        snake_case					:   str         =					os.path.split(lowercase				)[-1].split("."				)[0]
        accelerator.init_trackers(lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            snake_case					:   Any         =					0
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            snake_case					:   Dict         =					model(**lowercase				)
            snake_case					:   int         =					outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            snake_case					:   Union[str, Any]         =					loss / gradient_accumulation_steps
            accelerator.backward(lowercase				)
            if step % gradient_accumulation_steps == 0:
                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` (the default).
            batch.to(accelerator.device				)
            with torch.no_grad():
                snake_case					:   Optional[int]         =					model(**lowercase				)
            snake_case					:   Tuple         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   List[str]         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

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

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(lowercase				),
                    "epoch": epoch,
                }		,							step=lowercase		,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()







def 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					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."				)
    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(
        "--project_dir"		,							type=lowercase		,							default="logs"		,							help="Location on where to store experiment tracking logs` and relevent project information"		,							)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[str]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





