





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A							:			Optional[int]         =			16
__A							:			Optional[Any]         =			32





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE = 16							,     _SCREAMING_SNAKE_CASE = "bert-base-cased"				)							-> int:




    """simple docstring"""




    _A     =							AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
    _A     =							load_dataset('glue'							,     'mrpc'				)

    def tokenize_function(_SCREAMING_SNAKE_CASE				):
        # max_length=None => use the model max length (it's actually the default)
        _A     =							tokenizer(examples['sentence1']							,     examples['sentence2']							,     truncation=_SCREAMING_SNAKE_CASE							,     max_length=_SCREAMING_SNAKE_CASE				)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    _A     =							datasets.map(
        _SCREAMING_SNAKE_CASE							,     batched=_SCREAMING_SNAKE_CASE							,     remove_columns=['idx', 'sentence1', 'sentence2']							,     load_from_cache_file=_SCREAMING_SNAKE_CASE				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _A     =							tokenized_datasets.rename_column('label'							,     'labels'				)

    def collate_fn(_SCREAMING_SNAKE_CASE				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_SCREAMING_SNAKE_CASE							,     padding='max_length'							,     max_length=128							,     return_tensors='pt'				)
        return tokenizer.pad(_SCREAMING_SNAKE_CASE							,     padding='longest'							,     return_tensors='pt'				)

    # Instantiate dataloaders.
    _A     =							DataLoader(
        tokenized_datasets['train']							,     shuffle=_SCREAMING_SNAKE_CASE							,     collate_fn=_SCREAMING_SNAKE_CASE							,     batch_size=_SCREAMING_SNAKE_CASE				)
    _A     =							DataLoader(
        tokenized_datasets['validation']							,     shuffle=_SCREAMING_SNAKE_CASE							,     collate_fn=_SCREAMING_SNAKE_CASE							,     batch_size=_SCREAMING_SNAKE_CASE				)

    return train_dataloader, eval_dataloader





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Union[str, Any]:




    """simple docstring"""




    model.eval()
    _A     =							0
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device				)
        with torch.no_grad():
            _A     =							model(**_SCREAMING_SNAKE_CASE				)
        _A     =							outputs.logits.argmax(dim=-1				)
        # It is slightly faster to call this once, than multiple times
        _A,			_A     =							accelerator.gather(
            (predictions, batch['labels'])				)  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_SCREAMING_SNAKE_CASE				) - 1:
                _A     =							predictions[: len(eval_dataloader.dataset				) - samples_seen]
                _A     =							references[: len(eval_dataloader.dataset				) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_SCREAMING_SNAKE_CASE							,     references=_SCREAMING_SNAKE_CASE							,     )

    _A     =							metric.compute()
    return eval_metric["accuracy"]





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _A     =							config['lr']
    _A     =							int(config['num_epochs']				)
    _A     =							int(config['seed']				)
    _A     =							int(config['batch_size']				)
    _A     =							args.model_name_or_path

    set_seed(_SCREAMING_SNAKE_CASE				)
    _A,			_A     =							get_dataloaders(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _A     =							AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE							,     return_dict=_SCREAMING_SNAKE_CASE				)

    # Instantiate optimizer
    _A     =							(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _A     =							optimizer_cls(params=model.parameters()							,     lr=_SCREAMING_SNAKE_CASE				)

    if accelerator.state.deepspeed_plugin is not None:
        _A     =							accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        _A     =							1
    _A     =							(len(_SCREAMING_SNAKE_CASE				) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _A     =							get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE							,     num_warmup_steps=0							,     num_training_steps=_SCREAMING_SNAKE_CASE							,     )
    else:
        _A     =							DummyScheduler(_SCREAMING_SNAKE_CASE							,     total_num_steps=_SCREAMING_SNAKE_CASE							,     warmup_num_steps=0				)

    # 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.
    _A,			_A,			_A,			_A,			_A     =							accelerator.prepare(
        _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    # We need to keep track of how many total steps we have iterated over
    _A     =							0
    # We also need to keep track of the stating epoch so files are named properly
    _A     =							0
    _A     =							evaluate.load('glue'							,     'mrpc'				)
    _A     =							num_epochs

    if args.partial_train_epoch is not None:
        _A     =							args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint				)
        _A     =							args.resume_from_checkpoint.split('epoch_'				)[1]
        _A     =							''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        _A     =							int(_SCREAMING_SNAKE_CASE				) + 1
        _A     =							evaluation_loop(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
        accelerator.print('resumed checkpoint performance:'							,     _SCREAMING_SNAKE_CASE				)
        accelerator.print('resumed checkpoint\'s scheduler\'s lr:'							,     lr_scheduler.get_lr()[0]				)
        accelerator.print('resumed optimizers\'s lr:'							,     optimizer.param_groups[0]['lr']				)
        with open(os.path.join(args.output_dir							,     F"state_{starting_epoch-1}.json"				)							,     'r'				) as f:
            _A     =							json.load(_SCREAMING_SNAKE_CASE				)
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    _A     =							{}
    for epoch in range(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        model.train()
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
            _A     =							model(**_SCREAMING_SNAKE_CASE				)
            _A     =							outputs.loss
            _A     =							loss / gradient_accumulation_steps
            accelerator.backward(_SCREAMING_SNAKE_CASE				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        _A     =							F"epoch_{epoch}"
        _A     =							os.path.join(args.output_dir							,     _SCREAMING_SNAKE_CASE				)
        accelerator.save_state(_SCREAMING_SNAKE_CASE				)
        _A     =							evaluation_loop(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
        _A     =							accuracy
        _A     =							lr_scheduler.get_lr()[0]
        _A     =							optimizer.param_groups[0]['lr']
        _A     =							epoch
        _A     =							overall_step
        accelerator.print(F"epoch {epoch}:"							,     _SCREAMING_SNAKE_CASE				)

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir							,     F"state_{epoch}.json"				)							,     'w'				) as f:
                json.dump(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       )							-> Optional[Any]:




    """simple docstring"""




    _A     =							argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'				)
    parser.add_argument(
        '--model_name_or_path'							,     type=_SCREAMING_SNAKE_CASE							,     default='bert-base-cased'							,     help='Path to pretrained model or model identifier from huggingface.co/models.'							,     required=_SCREAMING_SNAKE_CASE							,     )
    parser.add_argument(
        '--output_dir'							,     type=_SCREAMING_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=_SCREAMING_SNAKE_CASE							,     default=_SCREAMING_SNAKE_CASE							,     help='If the training should continue from a checkpoint folder.'							,     )
    parser.add_argument(
        '--partial_train_epoch'							,     type=_SCREAMING_SNAKE_CASE							,     default=_SCREAMING_SNAKE_CASE							,     help='If passed, the training will stop after this number of epochs.'							,     )
    parser.add_argument(
        '--num_epochs'							,     type=_SCREAMING_SNAKE_CASE							,     default=2							,     help='Number of train epochs.'							,     )
    _A     =							parser.parse_args()
    _A     =							{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

    training_function(_SCREAMING_SNAKE_CASE							,     _SCREAMING_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)


