



SCREAMING_SNAKE_CASE__   			=   '''0.18.2'''

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


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 .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     filter(lambda __UpperCamelCase       : p.requires_grad			,						model.parameters()       )
			A__	  =     sum([np.prod(p.size()       ) for p in model_parameters]       )
			return params


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			if metric == "rouge2":
						A__	  =     '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						A__	  =     '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						A__	  =     '{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						A__	  =     '{val_avg_loss:.4f}-{step_count}'
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    ' function.'       )

			A__	  =     ModelCheckpoint(
			    dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=f'''val_{metric}'''			,						mode='max'			,						save_top_k=1			,						every_n_epochs=1			,						)

			return checkpoint_callback


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
								pl_module.logger.log_metrics(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=True       ):






								"""simple docstring"""


								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
								A__	  =     trainer.callback_metrics
								trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}       )
								# Log results
								A__	  =     Path(pl_module.hparams.output_dir       )
								if type_path == "test":
											A__	  =     od / 'test_results.txt'
											A__	  =     od / 'test_generations.txt'
								else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											A__	  =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											A__	  =     od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

								if "preds" in metrics:
											A__	  =     '\n'.join(metrics['preds']       )
											generations_file.open('w+'       ).write(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								try:
											A__	  =     pl_module.model.model.num_parameters()
								except AttributeError:
											A__	  =     pl_module.model.num_parameters()

								A__	  =     count_trainable_parameters(_snake_case       )
								# mp stands for million parameters
								trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}       )



					@rank_zero_only
					def      _a				(  self       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



					@rank_zero_only
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")




