
"""simple docstring"""
from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_pt_objects import *  # noqa F403
else:
			from .consistency_models import ConsistencyModelPipeline
			from .dance_diffusion import DanceDiffusionPipeline
			from .ddim import DDIMPipeline
			from .ddpm import DDPMPipeline
			from .dit import DiTPipeline
			from .latent_diffusion import LDMSuperResolutionPipeline
			from .latent_diffusion_uncond import LDMPipeline
			from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
			from .pndm import PNDMPipeline
			from .repaint import RePaintPipeline
			from .score_sde_ve import ScoreSdeVePipeline
			from .stochastic_karras_ve import KarrasVePipeline

try:
			if not (is_torch_available() and is_librosa_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
			from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
			if not (is_torch_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
			from .audioldm import AudioLDMPipeline
			from .controlnet import (
			    StableDiffusionControlNetImgaImgPipeline,
			    StableDiffusionControlNetInpaintPipeline,
			    StableDiffusionControlNetPipeline,
			)
			from .deepfloyd_if import (
			    IFImgaImgPipeline,
			    IFImgaImgSuperResolutionPipeline,
			    IFInpaintingPipeline,
			    IFInpaintingSuperResolutionPipeline,
			    IFPipeline,
			    IFSuperResolutionPipeline,
			)
			from .kandinsky import (
			    KandinskyImgaImgPipeline,
			    KandinskyInpaintPipeline,
			    KandinskyPipeline,
			    KandinskyPriorPipeline,
			)
			from .kandinskya_a import (
			    KandinskyVaaControlnetImgaImgPipeline,
			    KandinskyVaaControlnetPipeline,
			    KandinskyVaaImgaImgPipeline,
			    KandinskyVaaInpaintPipeline,
			    KandinskyVaaPipeline,
			    KandinskyVaaPriorEmbaEmbPipeline,
			    KandinskyVaaPriorPipeline,
			)
			from .latent_diffusion import LDMTextToImagePipeline
			from .paint_by_example import PaintByExamplePipeline
			from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
			from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
			from .stable_diffusion import (
			    CycleDiffusionPipeline,
			    StableDiffusionAttendAndExcitePipeline,
			    StableDiffusionDepthaImgPipeline,
			    StableDiffusionDiffEditPipeline,
			    StableDiffusionImageVariationPipeline,
			    StableDiffusionImgaImgPipeline,
			    StableDiffusionInpaintPipeline,
			    StableDiffusionInpaintPipelineLegacy,
			    StableDiffusionInstructPixaPixPipeline,
			    StableDiffusionLatentUpscalePipeline,
			    StableDiffusionLDMaDPipeline,
			    StableDiffusionModelEditingPipeline,
			    StableDiffusionPanoramaPipeline,
			    StableDiffusionParadigmsPipeline,
			    StableDiffusionPipeline,
			    StableDiffusionPixaPixZeroPipeline,
			    StableDiffusionSAGPipeline,
			    StableDiffusionUpscalePipeline,
			    StableUnCLIPImgaImgPipeline,
			    StableUnCLIPPipeline,
			)
			from .stable_diffusion_safe import StableDiffusionPipelineSafe
			from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
			from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
			from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
			from .versatile_diffusion import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
			from .vq_diffusion import VQDiffusionPipeline


try:
			if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
			from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
			if not is_onnx_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_onnx_objects import *  # noqa F403
else:
			from .onnx_utils import OnnxRuntimeModel

try:
			if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
			from .stable_diffusion import (
			    OnnxStableDiffusionImgaImgPipeline,
			    OnnxStableDiffusionInpaintPipeline,
			    OnnxStableDiffusionInpaintPipelineLegacy,
			    OnnxStableDiffusionPipeline,
			    OnnxStableDiffusionUpscalePipeline,
			    StableDiffusionOnnxPipeline,
			)

try:
			if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
			from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_objects import *  # noqa F403
else:
			from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
			if not (is_flax_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
			from .controlnet import FlaxStableDiffusionControlNetPipeline
			from .stable_diffusion import (
			    FlaxStableDiffusionImgaImgPipeline,
			    FlaxStableDiffusionInpaintPipeline,
			    FlaxStableDiffusionPipeline,
			)
try:
			if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
			from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline





"""simple docstring"""


import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
				print("""#################### AVAILABLE DEVICES ####################""")
				print(jax.devices())
				print("""###########################################################""")

				# setup for wandb sweep
				snake_case_								=			Args()
				snake_case_								=			wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
				snake_case_								=			dict(logger.config)
				del wandb_args["batch_size"]
				snake_case_								=			replace(args, **wandb_args)
				snake_case_								=			args.base_dir + """-""" + wandb.run.id
				snake_case_								=			replace(args, base_dir=base_dir)
				print(args)

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

				# drop extra batch for now
				snake_case_								=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				snake_case_								=			tr_dataset.shuffle().select(indices)
				snake_case_								=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
				snake_case_								=			val_dataset.shuffle().select(indices)

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """lr""": args.lr,
				    """init_lr""": args.init_lr,
				    """warmup_steps""": args.warmup_steps,
				    """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
				    """weight_decay""": args.weight_decay,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			Trainer(
				    args=args,
				    data_collator=data_collator,
				    model_save_fn=model.save_pretrained,
				    train_step_fn=train_step,
				    val_step_fn=val_step,
				    logger=logger,
				    scheduler_fn=lr,
				)

				snake_case_								=			None
				snake_case_								=			trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
				try:
								trainer.train(state, tr_dataset, val_dataset)
				except KeyboardInterrupt:
								print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

				print("""SAVING WEIGHTS IN `final-weights`""")
				snake_case_								=			jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





