






"""simple docstring"""



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							fname.split(os.path.sep				)[-1]
     return re.search(r"^(.*)_\d+\.jpg$"			,					snake_case				).groups()[0]




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Tuple							,			snake_case__      :	List[str]							,			snake_case__      :	Tuple=None							,			snake_case__      :	Optional[Any]=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							file_names
        UpperCAmelCase__						:    str							=							image_transform
        UpperCAmelCase__						:    List[Any]							=							label_to_id
   def __len__(		self      :	List[Any]							):


        '''simple docstring'''
        return len(self.file_names							)




   def __getitem__(		self      :	List[str]							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.file_names[idx]
        UpperCAmelCase__						:    Dict							=							PIL.Image.open(snake_case__							)
        UpperCAmelCase__						:    Dict							=							raw_image.convert("RGB"							)
        if self.image_transform is not None:
             UpperCAmelCase__						:    str							=							self.image_transform(snake_case__							)
        UpperCAmelCase__						:    str							=							extract_label(snake_case__							)
        if self.label_to_id is not None:
             UpperCAmelCase__						:    str							=							self.label_to_id[label]
        return {"image": image, "label": label}




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple			,					snake_case       : List[Any]				)->		Optional[int]:






     '''simple docstring'''



     if args.with_tracking:
          UpperCAmelCase__						:    Union[str, Any]							=							Accelerator(
              cpu=args.cpu			,					mixed_precision=args.mixed_precision			,					log_with="all"			,					project_dir=args.project_dir				)
     else:
          UpperCAmelCase__						:    Union[str, Any]							=							Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision				)

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase__						:    str							=							config["lr"]
     UpperCAmelCase__						:    int							=							int(config["num_epochs"]				)
     UpperCAmelCase__						:    Any							=							int(config["seed"]				)
     UpperCAmelCase__						:    Dict							=							int(config["batch_size"]				)
     UpperCAmelCase__						:    Optional[int]							=							config["image_size"]
     if not isinstance(snake_case			,					(list, tuple)				):
          UpperCAmelCase__						:    List[Any]							=							(image_size, image_size)

     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps			,					"isdigit"				):
          if args.checkpointing_steps == "epoch":
               UpperCAmelCase__						:    Optional[int]							=							args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               UpperCAmelCase__						:    int							=							int(args.checkpointing_steps				)
          else:
               raise ValueError(
                   f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'				)
     else:
          UpperCAmelCase__						:    Tuple							=							None

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

     # Grab all the image filenames
     UpperCAmelCase__						:    str							=							[os.path.join(args.data_dir			,					snake_case				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]

     # Build the label correspondences
     UpperCAmelCase__						:    Any							=							[extract_label(snake_case				) for fname in file_names]
     UpperCAmelCase__						:    str							=							list(set(snake_case				)				)
     id_to_label.sort()
     UpperCAmelCase__						:    int							=							{lbl: i for i, lbl in enumerate(snake_case				)}

     # Set the seed before splitting the data.
     np.random.seed(snake_case				)
     torch.manual_seed(snake_case				)
     torch.cuda.manual_seed_all(snake_case				)

     # Split our filenames between train and validation
     UpperCAmelCase__						:    Union[str, Any]							=							np.random.permutation(len(snake_case				)				)
     UpperCAmelCase__						:    Union[str, Any]							=							int(0.8 * len(snake_case				)				)
     UpperCAmelCase__						:    Any							=							random_perm[:cut]
     UpperCAmelCase__						:    List[Any]							=							random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     UpperCAmelCase__						:    Union[str, Any]							=							Compose([RandomResizedCrop(snake_case			,					scale=(0.5, 1.0)				), ToTensor()]				)
     UpperCAmelCase__						:    Optional[Any]							=							PetsDataset(
         [file_names[i] for i in train_split]			,					image_transform=snake_case			,					label_to_id=snake_case				)

     # For evaluation, we use a deterministic Resize
     UpperCAmelCase__						:    List[Any]							=							Compose([Resize(snake_case				), ToTensor()]				)
     UpperCAmelCase__						:    List[Any]							=							PetsDataset([file_names[i] for i in eval_split]			,					image_transform=snake_case			,					label_to_id=snake_case				)

     # Instantiate dataloaders.
     UpperCAmelCase__						:    Dict							=							DataLoader(snake_case			,					shuffle=snake_case			,					batch_size=snake_case			,					num_workers=4				)
     UpperCAmelCase__						:    Tuple							=							DataLoader(snake_case			,					shuffle=snake_case			,					batch_size=snake_case			,					num_workers=4				)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase__						:    List[str]							=							create_model("resnet50d"			,					pretrained=snake_case			,					num_classes=len(snake_case				)				)

     # 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).
     UpperCAmelCase__						:    Tuple							=							model.to(accelerator.device				)

     # Freezing the base model
     for param in model.parameters():
          UpperCAmelCase__						:    List[str]							=							False
     for param in model.get_classifier().parameters():
          UpperCAmelCase__						:    List[Any]							=							True

     # We normalize the batches of images to be a bit faster.
     UpperCAmelCase__						:    Union[str, Any]							=							torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
     UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)

     # Instantiate optimizer
     UpperCAmelCase__						:    List[Any]							=							torch.optim.Adam(params=model.parameters()			,					lr=lr / 25				)

     # Instantiate learning rate scheduler
     UpperCAmelCase__						:    Optional[int]							=							OneCycleLR(optimizer=snake_case			,					max_lr=snake_case			,					epochs=snake_case			,					steps_per_epoch=len(snake_case				)				)

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

          if "epoch" in training_difference:
               UpperCAmelCase__						:    Tuple							=							int(training_difference.replace("epoch_"			,					""				)				) + 1
               UpperCAmelCase__						:    Any							=							None
          else:
               UpperCAmelCase__						:    Optional[int]							=							int(training_difference.replace("step_"			,					""				)				)
               UpperCAmelCase__						:    List[str]							=							resume_step // len(snake_case				)
               resume_step -= starting_epoch * len(snake_case				)

    # Now we train the model
     for epoch in range(snake_case			,					snake_case				):
          model.train()
          if args.with_tracking:
               UpperCAmelCase__						:    int							=							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
               UpperCAmelCase__						:    Union[str, Any]							=							accelerator.skip_first_batches(snake_case			,					snake_case				)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               UpperCAmelCase__						:    Any							=							train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               UpperCAmelCase__						:    int							=							{k: v.to(accelerator.device				) for k, v in batch.items()}
               UpperCAmelCase__						:    Optional[Any]							=							(batch["image"] - mean) / std
               UpperCAmelCase__						:    Union[str, Any]							=							model(snake_case				)
               UpperCAmelCase__						:    Tuple							=							torch.nn.functional.cross_entropy(snake_case			,					batch["label"]				)
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(snake_case				)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(snake_case			,					snake_case				):
                    UpperCAmelCase__						:    Dict							=							f'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              UpperCAmelCase__						:    List[str]							=							os.path.join(args.output_dir			,					snake_case				)
                         accelerator.save_state(snake_case				)
          model.eval()
          UpperCAmelCase__						:    List[str]							=							0
          UpperCAmelCase__						:    Union[str, Any]							=							0
          for step, batch in enumerate(snake_case				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               UpperCAmelCase__						:    Any							=							{k: v.to(accelerator.device				) for k, v in batch.items()}
               UpperCAmelCase__						:    Optional[Any]							=							(batch["image"] - mean) / std
               with torch.no_grad():
                    UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
               UpperCAmelCase__						:    Tuple							=							outputs.argmax(dim=-1				)
               UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							accelerator.gather_for_metrics((predictions, batch["label"])				)
               UpperCAmelCase__						:    Optional[Any]							=							predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          UpperCAmelCase__						:    Any							=							accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}'				)
          if args.with_tracking:
               accelerator.log(
                   {
                       "accuracy": 100 * eval_metric,
                       "train_loss": total_loss.item() / len(snake_case				),
                       "epoch": epoch,
                   }			,					step=snake_case			,					)
          if checkpointing_steps == "epoch":
               UpperCAmelCase__						:    Optional[Any]							=							f'epoch_{epoch}'
               if args.output_dir is not None:
                    UpperCAmelCase__						:    int							=							os.path.join(args.output_dir			,					snake_case				)
               accelerator.save_state(snake_case				)

     if args.with_tracking:
          accelerator.end_training()


def 	SCREAMING_SNAKE_CASE__       (  )->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument("--data_dir"			,					required=snake_case			,					help="The data folder on disk."				)
     parser.add_argument("--fp16"			,					action="store_true"			,					help="If passed, will use FP16 training."				)
     parser.add_argument(
         "--mixed_precision"			,					type=snake_case			,					default=snake_case			,					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=snake_case			,					default=snake_case			,					help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."			,					)
     parser.add_argument(
         "--output_dir"			,					type=snake_case			,					default="."			,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,					)
     parser.add_argument(
         "--resume_from_checkpoint"			,					type=snake_case			,					default=snake_case			,					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(
         "--project_dir"			,					type=snake_case			,					default="logs"			,					help="Location on where to store experiment tracking logs` and relevent project information"			,					)
     UpperCAmelCase__						:    Any							=							parser.parse_args()
     UpperCAmelCase__						:    str							=							{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
     training_function(snake_case			,					snake_case				)


if __name__ == "__main__":
 main()










"""simple docstring"""



import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_lowerCAmelCase	:	Tuple				=      {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 131_072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Optional[int]:






     '''simple docstring'''



     return torch.atana(snake_case			,					snake_case				) / math.pi * 2


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							torch.sin(t * math.pi / 2				) ** 2
     UpperCAmelCase__						:    Optional[int]							=							(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(snake_case			,					snake_case				)




class 			lowerCAmelCase__			(    __magic_name__   ):
   pass




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Any							,			snake_case__      :	str							):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Any							=							DiffusionAttnUnetaD(snake_case__							,			n_attn_layers=4							)
        UpperCAmelCase__						:    Any							=							deepcopy(self.diffusion							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.quasirandom.SobolEngine(1							,			scramble=snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							MODELS_MAP[model_name]["url"]
     os.system(f'wget {url} ./'				)

     return f'./{model_name}.ckpt'


_lowerCAmelCase	:	List[Any]				=      {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_lowerCAmelCase	:	Optional[Any]				=      {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_lowerCAmelCase	:	List[str]				=      {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
_lowerCAmelCase	:	List[str]				=      {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_lowerCAmelCase	:	Dict				=      {
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

_lowerCAmelCase	:	List[Any]				=      {
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		Dict:






     '''simple docstring'''



     if name.startswith("skip"				):
          return name.replace("skip"			,					RES_CONV_MAP["skip"]				)

     # name has to be of format main.{digit}
     if not name.startswith("main."				):
          raise ValueError(f'ResConvBlock error with {name}'				)

     return name.replace(name[:6]			,					RES_CONV_MAP[name[:6]]				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		int:






     '''simple docstring'''



     for key, value in ATTN_MAP.items():
          if name.startswith(snake_case				) and not isinstance(snake_case			,					snake_case				):
               return name.replace(snake_case			,					snake_case				)
          elif name.startswith(snake_case				):
               return [name.replace(snake_case			,					snake_case				) for v in value]
     raise ValueError(f'Attn error with {name}'				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]=13				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							input_string

     if string.split("."				)[0] == "timestep_embed":
          return string.replace("timestep_embed"			,					"time_proj"				)

     UpperCAmelCase__						:    Tuple							=							0
     if string.startswith("net.3."				):
          depth += 1
          UpperCAmelCase__						:    Dict							=							string[6:]
     elif string.startswith("net."				):
          UpperCAmelCase__						:    Tuple							=							string[4:]

     while string.startswith("main.7."				):
          depth += 1
          UpperCAmelCase__						:    Any							=							string[7:]

     if string.startswith("main."				):
          UpperCAmelCase__						:    Union[str, Any]							=							string[5:]

     # mid block
     if string[:2].isdigit():
          UpperCAmelCase__						:    Optional[Any]							=							string[:2]
          UpperCAmelCase__						:    int							=							string[2:]
     else:
          UpperCAmelCase__						:    Optional[int]							=							string[0]
          UpperCAmelCase__						:    Any							=							string[1:]

     if depth == max_depth:
          UpperCAmelCase__						:    Any							=							MID_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							"mid_block"
     elif depth > 0 and int(snake_case				) < 7:
          UpperCAmelCase__						:    Optional[Any]							=							DOWN_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							f'down_blocks.{depth}'
     elif depth > 0 and int(snake_case				) > 7:
          UpperCAmelCase__						:    Optional[int]							=							UP_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Tuple							=							f'up_blocks.{max_depth - depth - 1}'
     elif depth == 0:
          UpperCAmelCase__						:    Tuple							=							DEPTH_0_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{max_depth - 1}' if int(snake_case				) > 3 else "down_blocks.0"

     if not string_left.startswith("."				):
          raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'				)

     UpperCAmelCase__						:    List[Any]							=							string_left[1:]

     if "resnets" in new_layer:
          UpperCAmelCase__						:    Optional[int]							=							convert_resconv_naming(snake_case				)
     elif "attentions" in new_layer:
          UpperCAmelCase__						:    Optional[int]							=							convert_attn_naming(snake_case				)
          UpperCAmelCase__						:    Any							=							new_string_left

     if not isinstance(snake_case			,					snake_case				):
          UpperCAmelCase__						:    Tuple							=							prefix + "." + new_layer + "." + string_left
     else:
          UpperCAmelCase__						:    str							=							[prefix + "." + new_layer + "." + s for s in string_left]
     return new_string


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[str]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							{}
     for k, v in state_dict.items():
          if k.endswith("kernel"				):
               # up- and downsample layers, don't have trainable weights
               continue

          UpperCAmelCase__						:    Optional[int]							=							rename(snake_case				)

          # check if we need to transform from Conv => Linear for attention
          if isinstance(snake_case			,					snake_case				):
               UpperCAmelCase__						:    Optional[Any]							=							transform_conv_attns(snake_case			,					snake_case			,					snake_case				)
          else:
               UpperCAmelCase__						:    List[Any]							=							v

     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[Any]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if len(snake_case				) == 1:
          if len(v.shape				) == 3:
               # weight
               UpperCAmelCase__						:    Optional[int]							=							v[:, :, 0]
          else:
               # bias
               UpperCAmelCase__						:    Any							=							v
     else:
          # qkv matrices
          UpperCAmelCase__						:    Any							=							v.shape[0]
          UpperCAmelCase__						:    Optional[Any]							=							trippled_shape // 3
          for i in range(3				):
               if len(v.shape				) == 3:
                    UpperCAmelCase__						:    List[Any]							=							v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    UpperCAmelCase__						:    Any							=							v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

     UpperCAmelCase__						:    List[str]							=							args.model_path.split("/"				)[-1].split("."				)[0]
     if not os.path.isfile(args.model_path				):
          assert (
              model_name == args.model_path
          ), f'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
          UpperCAmelCase__						:    Optional[int]							=							download(snake_case				)

     UpperCAmelCase__						:    List[str]							=							MODELS_MAP[model_name]["sample_rate"]
     UpperCAmelCase__						:    Dict							=							MODELS_MAP[model_name]["sample_size"]

     UpperCAmelCase__						:    Optional[int]							=							Object()
     UpperCAmelCase__						:    int							=							sample_size
     UpperCAmelCase__						:    List[Any]							=							sample_rate
     UpperCAmelCase__						:    int							=							0

     UpperCAmelCase__						:    int							=							UNetaDModel(sample_size=snake_case			,					sample_rate=snake_case				)
     UpperCAmelCase__						:    int							=							diffusers_model.state_dict()

     UpperCAmelCase__						:    Optional[Any]							=							DiffusionUncond(snake_case				)
     orig_model.load_state_dict(torch.load(args.model_path			,					map_location=snake_case				)["state_dict"]				)
     UpperCAmelCase__						:    List[str]							=							orig_model.diffusion_ema.eval()
     UpperCAmelCase__						:    Optional[int]							=							orig_model.state_dict()
     UpperCAmelCase__						:    Any							=							rename_orig_weights(snake_case				)

     UpperCAmelCase__						:    List[Any]							=							set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
     UpperCAmelCase__						:    Optional[int]							=							set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

     assert len(snake_case				) == 0, f'Problem with {renamed_minus_diffusers}'
     assert all(k.endswith("kernel"				) for k in list(snake_case				)				), f'Problem with {diffusers_minus_renamed}'

     for key, value in renamed_state_dict.items():
          assert (
              diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
          ), f'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
          if key == "time_proj.weight":
               UpperCAmelCase__						:    Union[str, Any]							=							value.squeeze()

          UpperCAmelCase__						:    List[str]							=							value

     diffusers_model.load_state_dict(snake_case				)

     UpperCAmelCase__						:    Any							=							100
     UpperCAmelCase__						:    str							=							33

     UpperCAmelCase__						:    Optional[int]							=							IPNDMScheduler(num_train_timesteps=snake_case				)

     UpperCAmelCase__						:    Any							=							torch.manual_seed(snake_case				)
     UpperCAmelCase__						:    List[str]							=							torch.randn([1, 2, config.sample_size]			,					generator=snake_case				).to(snake_case				)

     UpperCAmelCase__						:    Dict							=							torch.linspace(1			,					0			,					steps + 1			,					device=snake_case				)[:-1]
     UpperCAmelCase__						:    List[str]							=							get_crash_schedule(snake_case				)

     UpperCAmelCase__						:    List[str]							=							DanceDiffusionPipeline(unet=snake_case			,					scheduler=snake_case				)

     UpperCAmelCase__						:    Optional[Any]							=							torch.manual_seed(33				)
     UpperCAmelCase__						:    int							=							pipe(num_inference_steps=snake_case			,					generator=snake_case				).audios

     UpperCAmelCase__						:    Optional[Any]							=							sampling.iplms_sample(snake_case			,					snake_case			,					snake_case			,					{}				)
     UpperCAmelCase__						:    Tuple							=							generated.clamp(-1			,					1				)

     UpperCAmelCase__						:    Optional[int]							=							(generated - audio).abs().sum()
     UpperCAmelCase__						:    List[Any]							=							(generated - audio).abs().max()

     if args.save:
          pipe.save_pretrained(args.checkpoint_path				)

     print("Diff sum"			,					snake_case				)
     print("Diff max"			,					snake_case				)

     assert diff_max < 1E-3, f'Diff max: {diff_max} is too much :-/'

     print(f'Conversion for {model_name} successful!'				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Dict				=      argparse.ArgumentParser()

 parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
 parser.add_argument(
     """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
 )
 parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
 _lowerCAmelCase	:	Tuple				=      parser.parse_args()

 main(args)


