




import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowerCamelCase							:						str					       =  logging.getLogger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]=2      ,				lowercase      :			int=3      ,				lowercase      :			Union[str, Any]=16      ,				lowercase      :			int = 10      ,				lowercase      :			int = 2	):


      '''simple docstring'''



      def get_dataset(lowercase      :			List[str]	):
            lowerCamelCase_							=       torch.randn(batch_size * n_batches      ,				1	)
            return TensorDataset(lowercase      ,				a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,				1	)	)

      lowerCamelCase_							=       get_dataset(lowercase	)
      lowerCamelCase_							=       get_dataset(lowercase	)
      lowerCamelCase_							=       DataLoader(lowercase      ,				shuffle=lowercase      ,				batch_size=lowercase      ,				num_workers=4	)
      lowerCamelCase_							=       DataLoader(lowercase      ,				shuffle=lowercase      ,				batch_size=lowercase      ,				num_workers=4	)
      return (train_dataloader, valid_dataloader)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int      ,				lowercase      :			int      ,				lowercase      :			List[Any]      ,				lowercase      :			Any      ,				lowercase      :			Tuple      ,				lowercase      :			Tuple=None	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      for epoch in range(lowercase	):
            # Train quickly
            model.train()
            for batch in dataloader:
                  lowerCamelCase_							=       batch
                  lowerCamelCase_							=       model(lowercase	)
                  lowerCamelCase_							=       torch.nn.functional.mse_loss(lowercase      ,				lowercase	)
                  accelerator.backward(lowercase	)
                  optimizer.step()
                  optimizer.zero_grad()
            rands.append(random.random()	)  # Introduce some randomness
            if scheduler is not None:
                  scheduler.step()
      return rands







class     A(		nn.Module  ):



 '''simple docstring'''


 def __init__(							self   :    Dict    )  ->      Any:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_							=       nn.Parameter(torch.randn(1    )    )
       lowerCamelCase_							=       nn.Parameter(torch.randn(1    )    )





 def 							a__					(							self   :    List[str]		,				A_   :    Any    )  ->      Optional[int]:

       """simple docstring"""






       return x * self.a + self.b







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       dummy_dataloaders()
             lowerCamelCase_							=       ProjectConfiguration(total_limit=1		,				project_dir=snake_case__		,				automatic_checkpoint_naming=snake_case__    )
             # Train baseline
             lowerCamelCase_							=       Accelerator(project_config=snake_case__    )
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir    )    )		,				1    )

 def 							a__					(							self   :    int    )  ->      List[str]:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       dummy_dataloaders()
             # Train baseline
             lowerCamelCase_							=       Accelerator()
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save initial
             lowerCamelCase_							=       os.path.join(snake_case__		,				'initial'    )
             accelerator.save_state(snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             lowerCamelCase_							=       train(3		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()

             # Train partially
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       dummy_dataloaders()
             lowerCamelCase_							=       Accelerator()
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             accelerator.load_state(snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )

             lowerCamelCase_							=       train(2		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save everything
             lowerCamelCase_							=       os.path.join(snake_case__		,				'checkpoint'    )
             accelerator.save_state(snake_case__    )

             # Load everything back in and make sure all states work
             accelerator.load_state(snake_case__    )
             test_rands += train(1		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )

 def 							a__					(							self   :    str    )  ->      str:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       dummy_dataloaders()
             lowerCamelCase_							=       ProjectConfiguration(automatic_checkpoint_naming=snake_case__    )

             # Train baseline
             lowerCamelCase_							=       Accelerator(project_dir=snake_case__		,				project_config=snake_case__    )
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save initial
             accelerator.save_state()
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             lowerCamelCase_							=       train(3		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()

             # Train partially
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       dummy_dataloaders()
             lowerCamelCase_							=       ProjectConfiguration(iteration=1		,				automatic_checkpoint_naming=snake_case__    )
             lowerCamelCase_							=       Accelerator(project_dir=snake_case__		,				project_config=snake_case__    )
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             accelerator.load_state(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_0'    )    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )

             lowerCamelCase_							=       train(2		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_1'    )    )
             test_rands += train(1		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             (lowerCamelCase_)							=       model.a.item(), model.b.item()
             lowerCamelCase_							=       optimizer.state_dict()
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )
             self.assertEqual(snake_case__		,				snake_case__    )

 def 							a__					(							self   :    Optional[Any]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       torch.tensor([1, 2, 3]    )
       lowerCamelCase_							=       torch.tensor([2, 3, 4]    )
       lowerCamelCase_							=       DummyModel()
       lowerCamelCase_							=       torch.optim.Adam(net.parameters()    )
       lowerCamelCase_							=       Accelerator()
       with self.assertRaises(snake_case__    ) as ve:
             accelerator.register_for_checkpointing(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
       lowerCamelCase_							=       str(ve.exception    )
       self.assertTrue('Item at index 0' in message    )
       self.assertTrue('Item at index 1' in message    )
       self.assertFalse('Item at index 2' in message    )
       self.assertFalse('Item at index 3' in message    )

 def 							a__					(							self   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       torch.optim.Adam(params=model.parameters()		,				lr=1E-3    )
             lowerCamelCase_							=       torch.optim.lr_scheduler.StepLR(snake_case__		,				step_size=1		,				gamma=0.99    )
             lowerCamelCase_							=       dummy_dataloaders()
             lowerCamelCase_							=       ProjectConfiguration(automatic_checkpoint_naming=snake_case__    )
             # Train baseline
             lowerCamelCase_							=       Accelerator(project_dir=snake_case__		,				project_config=snake_case__    )
             lowerCamelCase_							=       accelerator.prepare(
                 snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             # Save initial
             accelerator.save_state()
             lowerCamelCase_							=       scheduler.state_dict()
             train(3		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )
             self.assertNotEqual(snake_case__		,				scheduler.state_dict()    )

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_0'    )    )
             self.assertEqual(snake_case__		,				scheduler.state_dict()    )

 def 							a__					(							self   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42    )
             lowerCamelCase_							=       DummyModel()
             lowerCamelCase_							=       ProjectConfiguration(automatic_checkpoint_naming=snake_case__		,				total_limit=2    )
             # Train baseline
             lowerCamelCase_							=       Accelerator(project_dir=snake_case__		,				project_config=snake_case__    )
             lowerCamelCase_							=       accelerator.prepare(snake_case__    )
             # Save 3 states:
             for _ in range(11    ):
                   accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_0'    )    )    )
             self.assertTrue(os.path.exists(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_9'    )    )    )
             self.assertTrue(os.path.exists(os.path.join(snake_case__		,				'checkpoints'		,				'checkpoint_10'    )    )    )





 @require_cuda
 def 							a__					(							self   :    List[Any]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
       execute_subprocess_async(snake_case__		,				env=os.environ.copy()    )


if __name__ == "__main__":
 lowerCamelCase							:						Optional[Any]					       =  """/tmp/accelerate/state_checkpointing"""
 lowerCamelCase							:						List[Any]					       =  DummyModel()
 lowerCamelCase							:						Dict					       =  torch.optim.Adam(params=model.parameters(), lr=1e-3)
 lowerCamelCase							:						Optional[int]					       =  torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 lowerCamelCase							:						int					       =  dummy_dataloaders()
 lowerCamelCase							:						Optional[int]					       =  ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 lowerCamelCase							:						int					       =  Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 lowerCamelCase							:						Tuple					       =  accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 lowerCamelCase							:						Optional[int]					       =  accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  lowerCamelCase							:						List[str]					       =  group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 lowerCamelCase							:						Dict					       =  model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
 for group in optimizer.param_groups:
  lowerCamelCase							:						Optional[Any]					       =  group["""params"""][0].device
  break
 assert (
     param_device.type == torch.device("cpu").type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
 for group in optimizer.param_groups:
  lowerCamelCase							:						Optional[int]					       =  group["""params"""][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
  accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()











"""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)


