

"""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


SCREAMING_SNAKE_CASE_     :			List[str]	  =			{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 6_5536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8000,
        '''sample_size''': 13_1072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6000,
        '''sample_size''': 6_5536,
    },
}



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   return torch.atana(A__	,    A__   ) / math.pi * 2



def 	UpperCAmelCase__   (				A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			torch.sin(t * math.pi / 2   ) ** 2
   lowerCamelCase__						=			(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(A__	,    A__   )


class 		_A       (			__a					):
      pass






class 		_A       (			nn.Module					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         super().__init__()

         lowerCamelCase__						=			DiffusionAttnUnetaD(SCREAMING_SNAKE_CASE__    ,						n_attn_layers=4		)
         lowerCamelCase__						=			deepcopy(self.diffusion		)
         lowerCamelCase__						=			torch.quasirandom.SobolEngine(1    ,						scramble=SCREAMING_SNAKE_CASE__		)






def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			MODELS_MAP[model_name]["url"]
   os.system(f'wget {url} ./'   )

   return f'./{model_name}.ckpt'


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

SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}



def 	UpperCAmelCase__   (				A__   )      ->      List[Any]:




   """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 	UpperCAmelCase__   (				A__   )      ->      List[str]:




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



def 	UpperCAmelCase__   (				A__	,    A__=13   )      ->      Tuple:




   """simple docstring"""
   lowerCamelCase__						=			input_string

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

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

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

   if string.startswith("main."   ):
      lowerCamelCase__						=			string[5:]

   # mid block
   if string[:2].isdigit():
      lowerCamelCase__						=			string[:2]
      lowerCamelCase__						=			string[2:]
   else:
      lowerCamelCase__						=			string[0]
      lowerCamelCase__						=			string[1:]

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

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

   lowerCamelCase__						=			string_left[1:]

   if "resnets" in new_layer:
      lowerCamelCase__						=			convert_resconv_naming(A__   )
   elif "attentions" in new_layer:
      lowerCamelCase__						=			convert_attn_naming(A__   )
      lowerCamelCase__						=			new_string_left

   if not isinstance(A__	,    A__   ):
      lowerCamelCase__						=			prefix + "." + new_layer + "." + string_left
   else:
      lowerCamelCase__						=			[prefix + "." + new_layer + "." + s for s in string_left]
   return new_string



def 	UpperCAmelCase__   (				A__   )      ->      Tuple:




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

      lowerCamelCase__						=			rename(A__   )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(A__	,    A__   ):
         lowerCamelCase__						=			transform_conv_attns(A__	,    A__	,    A__   )
      else:
         lowerCamelCase__						=			v

   return new_state_dict



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      int:




   """simple docstring"""
   if len(A__   ) == 1:
      if len(v.shape   ) == 3:
         # weight
         lowerCamelCase__						=			v[:, :, 0]
      else:
         # bias
         lowerCamelCase__						=			v
   else:
      # qkv matrices
      lowerCamelCase__						=			v.shape[0]
      lowerCamelCase__						=			trippled_shape // 3
      for i in range(3   ):
         if len(v.shape   ) == 3:
            lowerCamelCase__						=			v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            lowerCamelCase__						=			v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict



def 	UpperCAmelCase__   (				A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			torch.device("cuda" if torch.cuda.is_available() else "cpu"   )

   lowerCamelCase__						=			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()}'
      lowerCamelCase__						=			download(A__   )

   lowerCamelCase__						=			MODELS_MAP[model_name]["sample_rate"]
   lowerCamelCase__						=			MODELS_MAP[model_name]["sample_size"]

   lowerCamelCase__						=			Object()
   lowerCamelCase__						=			sample_size
   lowerCamelCase__						=			sample_rate
   lowerCamelCase__						=			0

   lowerCamelCase__						=			UNetaDModel(sample_size=A__	,    sample_rate=A__   )
   lowerCamelCase__						=			diffusers_model.state_dict()

   lowerCamelCase__						=			DiffusionUncond(A__   )
   orig_model.load_state_dict(torch.load(args.model_path	,    map_location=A__   )["state_dict"]   )
   lowerCamelCase__						=			orig_model.diffusion_ema.eval()
   lowerCamelCase__						=			orig_model.state_dict()
   lowerCamelCase__						=			rename_orig_weights(A__   )

   lowerCamelCase__						=			set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
   lowerCamelCase__						=			set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

   assert len(A__   ) == 0, f'Problem with {renamed_minus_diffusers}'
   assert all(k.endswith("kernel"   ) for k in list(A__   )   ), 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":
         lowerCamelCase__						=			value.squeeze()

      lowerCamelCase__						=			value

   diffusers_model.load_state_dict(A__   )

   lowerCamelCase__						=			100
   lowerCamelCase__						=			33

   lowerCamelCase__						=			IPNDMScheduler(num_train_timesteps=A__   )

   lowerCamelCase__						=			torch.manual_seed(A__   )
   lowerCamelCase__						=			torch.randn([1, 2, config.sample_size]	,    generator=A__   ).to(A__   )

   lowerCamelCase__						=			torch.linspace(1	,    0	,    steps + 1	,    device=A__   )[:-1]
   lowerCamelCase__						=			get_crash_schedule(A__   )

   lowerCamelCase__						=			DanceDiffusionPipeline(unet=A__	,    scheduler=A__   )

   lowerCamelCase__						=			torch.manual_seed(33   )
   lowerCamelCase__						=			pipe(num_inference_steps=A__	,    generator=A__   ).audios

   lowerCamelCase__						=			sampling.iplms_sample(A__	,    A__	,    A__	,    {}   )
   lowerCamelCase__						=			generated.clamp(-1	,    1   )

   lowerCamelCase__						=			(generated - audio).abs().sum()
   lowerCamelCase__						=			(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			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.''')
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			parser.parse_args()

     main(args)







"""simple docstring"""




import argparse
import gc
import json
import os

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


SCREAMING_SNAKE_CASE_     :			int	  =			16
SCREAMING_SNAKE_CASE_     :			int	  =			32



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   return int(x / 2**20   )


class 		_A       :





      def __enter__(  self		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         return self





      def __exit__(  self    ,						*SCREAMING_SNAKE_CASE__		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         lowerCamelCase__						=			bamb(self.end - self.begin		)
         lowerCamelCase__						=			bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCAmelCase__   (				A__	,    A__ = 16	,    A__ = "bert-base-cased"	,    A__ = 320	,    A__ = 160	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			AutoTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			load_dataset(
       "glue"	,    "mrpc"	,    split={"train": f'train[:{n_train}]', "validation": f'validation[:{n_val}]'}   )

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

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

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

   def collate_fn(A__   ):
      # 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(A__	,    padding="max_length"	,    max_length=128	,    return_tensors="pt"   )
      return tokenizer.pad(A__	,    padding="longest"	,    return_tensors="pt"   )

   # Instantiate dataloaders.
   lowerCamelCase__						=			DataLoader(
       tokenized_datasets["train"]	,    shuffle=A__	,    collate_fn=A__	,    batch_size=A__   )
   lowerCamelCase__						=			DataLoader(
       tokenized_datasets["validation"]	,    shuffle=A__	,    collate_fn=A__	,    batch_size=A__   )

   return train_dataloader, eval_dataloader



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   # Initialize accelerator
   lowerCamelCase__						=			Accelerator()

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

   set_seed(A__   )
   lowerCamelCase__					,       lowerCamelCase__						=			get_dataloaders(A__	,    A__	,    A__	,    args.n_train	,    args.n_val   )

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

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

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase__						=			accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCamelCase__						=			1
   lowerCamelCase__						=			(len(A__   ) * num_epochs) // gradient_accumulation_steps

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

   # We need to keep track of how many total steps we have iterated over
   lowerCamelCase__						=			0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCamelCase__						=			0

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

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
      accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
      accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
      accelerator.print(
          "Total Peak Memory consumed during the train (max): {}".format(
              tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
      lowerCamelCase__						=			tracemalloc.peaked + bamb(tracemalloc.begin   )
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir	,    "peak_memory_utilization.json"   )	,    "w"   ) as f:
         json.dump(A__	,    A__   )



def 	UpperCAmelCase__   (				)      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
   parser.add_argument(
       "--model_name_or_path"	,    type=A__	,    default="bert-base-cased"	,    help="Path to pretrained model or model identifier from huggingface.co/models."	,    required=A__	,    )
   parser.add_argument(
       "--output_dir"	,    type=A__	,    default="."	,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,    )
   parser.add_argument(
       "--peak_memory_upper_bound"	,    type=A__	,    default=A__	,    help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."	,    )
   parser.add_argument(
       "--n_train"	,    type=A__	,    default=320	,    help="Number of training examples to use."	,    )
   parser.add_argument(
       "--n_val"	,    type=A__	,    default=160	,    help="Number of validation examples to use."	,    )
   parser.add_argument(
       "--num_epochs"	,    type=A__	,    default=1	,    help="Number of train epochs."	,    )
   lowerCamelCase__						=			parser.parse_args()
   lowerCamelCase__						=			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(A__	,    A__   )


if __name__ == "__main__":
     main()




