import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule


class     lowercase     (  A__  ):
							lowercase__    :							List[str]										=				42
							lowercase__    :							List[Any]										=				jnp.floataa
							lowercase__    :							str										=				True




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



												'''simple docstring'''

												super().setup()
												SCREAMING_SNAKE_CASE           =							nn.Dense(5						,				dtype=self.dtype   )





							def __call__(    self :       List[Any]						,				*_UpperCamelCase :       Dict						,				**_UpperCamelCase :       Optional[int]   )       ->					int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							super().__call__(*_lowerCamelCase						,				**_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							self.cls(outputs[2]   )
												return outputs[:2] + (cls_out,)



class     lowercase     (  A__  ):
							lowercase__    :							Dict										=				FlaxBigBirdForNaturalQuestionsModule





def 						__lowerCamelCase				(UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Union[str, Any]   ,			UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Union[str, Any]   ,			UpperCAmelCase__  :				Union[str, Any]	):
					def cross_entropy(UpperCAmelCase__  :				Any   ,			UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				List[str]=None	):
										SCREAMING_SNAKE_CASE           =							logits.shape[-1]
										SCREAMING_SNAKE_CASE           =							(labels[..., None] == jnp.arange(__lowercase	)[None]).astype("f4"	)
										SCREAMING_SNAKE_CASE           =							jax.nn.log_softmax(__lowercase   ,			axis=-1	)
										SCREAMING_SNAKE_CASE           =							-jnp.sum(labels * logits   ,			axis=-1	)
										if reduction is not None:
															SCREAMING_SNAKE_CASE           =							reduction(__lowercase	)
										return loss

					SCREAMING_SNAKE_CASE           =							partial(__lowercase   ,			reduction=jnp.mean	)
					SCREAMING_SNAKE_CASE           =							cross_entropy(__lowercase   ,			__lowercase	)
					SCREAMING_SNAKE_CASE           =							cross_entropy(__lowercase   ,			__lowercase	)
					SCREAMING_SNAKE_CASE           =							cross_entropy(__lowercase   ,			__lowercase	)
					return (start_loss + end_loss + pooled_loss) / 3


@dataclass
class     lowercase     :
							lowercase__    :							Any										=				"""google/bigbird-roberta-base"""
							lowercase__    :							Optional[int]										=				3_000
							lowercase__    :							Tuple										=				10_500

							lowercase__    :							Dict										=				128
							lowercase__    :							List[Any]										=				3

							lowercase__    :							Optional[int]										=				1
							lowercase__    :							int										=				5

							# tx_args
							lowercase__    :							int										=				3e-5
							lowercase__    :							str										=				0.0
							lowercase__    :							List[str]										=				20_000
							lowercase__    :							Union[str, Any]										=				0.0095

							lowercase__    :							List[str]										=				"""bigbird-roberta-natural-questions"""
							lowercase__    :							List[str]										=				"""training-expt"""
							lowercase__    :							str										=				"""data/nq-training.jsonl"""
							lowercase__    :							List[Any]										=				"""data/nq-validation.jsonl"""




							def 						__snake_case(    self :       Dict   )       ->					List[str]:



												'''simple docstring'''

												os.makedirs(self.base_dir						,				exist_ok=_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							os.path.join(self.base_dir						,				self.save_dir   )
												SCREAMING_SNAKE_CASE           =							self.batch_size_per_device * jax.device_count()



@dataclass
class     lowercase     :
							lowercase__    :							List[str]										=				42
							lowercase__    :							Union[str, Any]										=				4_096  # no dynamic padding on TPUs




							def __call__(    self :       Union[str, Any]						,				_UpperCamelCase :       List[str]   )       ->					Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.collate_fn(_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							jax.tree_util.tree_map(_lowerCamelCase						,				_lowerCamelCase   )
												return batch




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Tuple   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.fetch_inputs(features["input_ids"]   )
												SCREAMING_SNAKE_CASE           =							{
												    'input_ids': jnp.array(_lowerCamelCase						,				dtype=jnp.intaa   ),
												    'attention_mask': jnp.array(_lowerCamelCase						,				dtype=jnp.intaa   ),
												    'start_labels': jnp.array(features["start_token"]						,				dtype=jnp.intaa   ),
												    'end_labels': jnp.array(features["end_token"]						,				dtype=jnp.intaa   ),
												    'pooled_labels': jnp.array(features["category"]						,				dtype=jnp.intaa   ),
												}
												return batch




							def 						__snake_case(    self :       List[Any]						,				_UpperCamelCase :       list   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[self._fetch_inputs(_lowerCamelCase   ) for ids in input_ids]
												return zip(*_lowerCamelCase   )





							def 						__snake_case(    self :       Optional[Any]						,				_UpperCamelCase :       list   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[1 for _ in range(len(_lowerCamelCase   )   )]
												while len(_lowerCamelCase   ) < self.max_length:
																	input_ids.append(self.pad_id   )
																	attention_mask.append(0   )
												return input_ids, attention_mask





def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				Dict   ,			UpperCAmelCase__  :				Optional[Any]=None	):
					if seed is not None:
										SCREAMING_SNAKE_CASE           =							dataset.shuffle(seed=__lowercase	)
					for i in range(len(__lowercase	) // batch_size	):
										SCREAMING_SNAKE_CASE           =							dataset[i * batch_size : (i + 1) * batch_size]
										yield dict(__lowercase	)





@partial(jax.pmap   ,			axis_name="batch"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				List[Any]   ,			**UpperCAmelCase__  :				Any	):
					def loss_fn(UpperCAmelCase__  :				Union[str, Any]	):
										SCREAMING_SNAKE_CASE           =							model_inputs.pop("start_labels"	)
										SCREAMING_SNAKE_CASE           =							model_inputs.pop("end_labels"	)
										SCREAMING_SNAKE_CASE           =							model_inputs.pop("pooled_labels"	)

										SCREAMING_SNAKE_CASE           =							state.apply_fn(**__lowercase   ,			params=__lowercase   ,			dropout_rng=__lowercase   ,			train=__lowercase	)
										SCREAMING_SNAKE_CASE           =							outputs

										return state.loss_fn(
										    __lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			)

					SCREAMING_SNAKE_CASE           =							jax.random.split(__lowercase	)
					SCREAMING_SNAKE_CASE           =							jax.value_and_grad(__lowercase	)
					SCREAMING_SNAKE_CASE           =							grad_fn(state.params	)
					SCREAMING_SNAKE_CASE           =							jax.lax.pmean({"loss": loss}   ,			axis_name="batch"	)
					SCREAMING_SNAKE_CASE           =							jax.lax.pmean(__lowercase   ,			"batch"	)

					SCREAMING_SNAKE_CASE           =							state.apply_gradients(grads=__lowercase	)
					return state, metrics, new_drp_rng





@partial(jax.pmap   ,			axis_name="batch"	)
def 						__lowerCamelCase				(UpperCAmelCase__  :				str   ,			**UpperCAmelCase__  :				List[Any]	):
					SCREAMING_SNAKE_CASE           =							model_inputs.pop("start_labels"	)
					SCREAMING_SNAKE_CASE           =							model_inputs.pop("end_labels"	)
					SCREAMING_SNAKE_CASE           =							model_inputs.pop("pooled_labels"	)

					SCREAMING_SNAKE_CASE           =							state.apply_fn(**__lowercase   ,			params=state.params   ,			train=__lowercase	)
					SCREAMING_SNAKE_CASE           =							outputs

					SCREAMING_SNAKE_CASE           =							state.loss_fn(__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase	)
					SCREAMING_SNAKE_CASE           =							jax.lax.pmean({"loss": loss}   ,			axis_name="batch"	)
					return metrics


class     lowercase     (  train_state.TrainState  ):
							lowercase__    :							Dict										=				struct.field(pytree_node=A__  )



@dataclass
class     lowercase     :
							lowercase__    :							Union[str, Any]										=				42
							lowercase__    :							List[str]										=				42
							lowercase__    :							Tuple										=				42
							lowercase__    :							Optional[int]										=				42
							lowercase__    :							Tuple										=				42
							lowercase__    :							List[Any]										=				42
							lowercase__    :							List[str]										=				None




							def 						__snake_case(    self :       Optional[int]						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       Optional[int]						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       List[Any]=None   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							model.params
												SCREAMING_SNAKE_CASE           =							TrainState.create(
												    apply_fn=model.__call__						,				params=_lowerCamelCase						,				tx=_lowerCamelCase						,				loss_fn=_lowerCamelCase						,				)
												if ckpt_dir is not None:
																	SCREAMING_SNAKE_CASE           =							restore_checkpoint(_lowerCamelCase						,				_lowerCamelCase   )
																	SCREAMING_SNAKE_CASE           =							{
																	    'lr': args.lr,
																	    'init_lr': args.init_lr,
																	    'warmup_steps': args.warmup_steps,
																	    'num_train_steps': num_train_steps,
																	    'weight_decay': args.weight_decay,
																	}
																	SCREAMING_SNAKE_CASE           =							build_tx(**_lowerCamelCase   )
																	SCREAMING_SNAKE_CASE           =							train_state.TrainState(
																	    step=_lowerCamelCase						,				apply_fn=model.__call__						,				params=_lowerCamelCase						,				tx=_lowerCamelCase						,				opt_state=_lowerCamelCase						,				)
																	SCREAMING_SNAKE_CASE           =							args
																	SCREAMING_SNAKE_CASE           =							data_collator
																	SCREAMING_SNAKE_CASE           =							lr
																	SCREAMING_SNAKE_CASE           =							params
												SCREAMING_SNAKE_CASE           =							jax_utils.replicate(_lowerCamelCase   )
												return state




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Optional[int]						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       Dict   )       ->					Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.args
												SCREAMING_SNAKE_CASE           =							len(_lowerCamelCase   ) // args.batch_size

												SCREAMING_SNAKE_CASE           =							jax.random.PRNGKey(0   )
												SCREAMING_SNAKE_CASE           =							jax.random.split(_lowerCamelCase						,				jax.device_count()   )
												for epoch in range(args.max_epochs   ):
																	SCREAMING_SNAKE_CASE           =							jnp.array(0						,				dtype=jnp.floataa   )
																	SCREAMING_SNAKE_CASE           =							get_batched_dataset(_lowerCamelCase						,				args.batch_size						,				seed=_lowerCamelCase   )
																	SCREAMING_SNAKE_CASE           =							0
																	for batch in tqdm(_lowerCamelCase						,				total=_lowerCamelCase						,				desc=F"Running EPOCH-{epoch}"   ):
																						SCREAMING_SNAKE_CASE           =							self.data_collator(_lowerCamelCase   )
																						SCREAMING_SNAKE_CASE           =							self.train_step_fn(_lowerCamelCase						,				_lowerCamelCase						,				**_lowerCamelCase   )
																						running_loss += jax_utils.unreplicate(metrics["loss"]   )
																						i += 1
																						if i % args.logging_steps == 0:
																											SCREAMING_SNAKE_CASE           =							jax_utils.unreplicate(state.step   )
																											SCREAMING_SNAKE_CASE           =							running_loss.item() / i
																											SCREAMING_SNAKE_CASE           =							self.scheduler_fn(state_step - 1   )

																											SCREAMING_SNAKE_CASE           =							self.evaluate(_lowerCamelCase						,				_lowerCamelCase   )
																											SCREAMING_SNAKE_CASE           =							{
																											    'step': state_step.item(),
																											    'eval_loss': eval_loss.item(),
																											    'tr_loss': tr_loss,
																											    'lr': lr.item(),
																											}
																											tqdm.write(str(_lowerCamelCase   )   )
																											self.logger.log(_lowerCamelCase						,				commit=_lowerCamelCase   )

																						if i % args.save_steps == 0:
																											self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"						,				state=_lowerCamelCase   )




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       str						,				_UpperCamelCase :       List[Any]   )       ->					Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							get_batched_dataset(_lowerCamelCase						,				self.args.batch_size   )
												SCREAMING_SNAKE_CASE           =							len(_lowerCamelCase   ) // self.args.batch_size
												SCREAMING_SNAKE_CASE           =							jnp.array(0						,				dtype=jnp.floataa   )
												SCREAMING_SNAKE_CASE           =							0
												for batch in tqdm(_lowerCamelCase						,				total=_lowerCamelCase						,				desc="Evaluating ... "   ):
																	SCREAMING_SNAKE_CASE           =							self.data_collator(_lowerCamelCase   )
																	SCREAMING_SNAKE_CASE           =							self.val_step_fn(_lowerCamelCase						,				**_lowerCamelCase   )
																	running_loss += jax_utils.unreplicate(metrics["loss"]   )
																	i += 1
												return running_loss / i





							def 						__snake_case(    self :       List[Any]						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Tuple   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							jax_utils.unreplicate(_lowerCamelCase   )
												print(F"SAVING CHECKPOINT IN {save_dir}"						,				end=" ... "   )
												self.model_save_fn(_lowerCamelCase						,				params=state.params   )
												with open(os.path.join(_lowerCamelCase						,				"opt_state.msgpack"   )						,				"wb"   ) as f:
																	f.write(to_bytes(state.opt_state   )   )
												joblib.dump(self.args						,				os.path.join(_lowerCamelCase						,				"args.joblib"   )   )
												joblib.dump(self.data_collator						,				os.path.join(_lowerCamelCase						,				"data_collator.joblib"   )   )
												with open(os.path.join(_lowerCamelCase						,				"training_state.json"   )						,				"w"   ) as f:
																	json.dump({"step": state.step.item()}						,				_lowerCamelCase   )
												print("DONE"   )





def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]   ,			UpperCAmelCase__  :				str	):
					print(F"RESTORING CHECKPOINT FROM {save_dir}"   ,			end=" ... "	)
					with open(os.path.join(__lowercase   ,			"flax_model.msgpack"	)   ,			"rb"	) as f:
										SCREAMING_SNAKE_CASE           =							from_bytes(state.params   ,			f.read()	)

					with open(os.path.join(__lowercase   ,			"opt_state.msgpack"	)   ,			"rb"	) as f:
										SCREAMING_SNAKE_CASE           =							from_bytes(state.opt_state   ,			f.read()	)

					SCREAMING_SNAKE_CASE           =							joblib.load(os.path.join(__lowercase   ,			"args.joblib"	)	)
					SCREAMING_SNAKE_CASE           =							joblib.load(os.path.join(__lowercase   ,			"data_collator.joblib"	)	)

					with open(os.path.join(__lowercase   ,			"training_state.json"	)   ,			"r"	) as f:
										SCREAMING_SNAKE_CASE           =							json.load(__lowercase	)
					SCREAMING_SNAKE_CASE           =							training_state['step']

					print("DONE"	)
					return params, opt_state, step, args, data_collator





def 						__lowerCamelCase				(UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Tuple   ,			UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							num_train_steps - warmup_steps
					SCREAMING_SNAKE_CASE           =							optax.linear_schedule(init_value=__lowercase   ,			end_value=__lowercase   ,			transition_steps=__lowercase	)
					SCREAMING_SNAKE_CASE           =							optax.linear_schedule(init_value=__lowercase   ,			end_value=1e-7   ,			transition_steps=__lowercase	)
					SCREAMING_SNAKE_CASE           =							optax.join_schedules(schedules=[warmup_fn, decay_fn]   ,			boundaries=[warmup_steps]	)
					return lr





def 						__lowerCamelCase				(UpperCAmelCase__  :				List[str]   ,			UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				Optional[int]   ,			UpperCAmelCase__  :				int   ,			UpperCAmelCase__  :				Optional[Any]	):
					def weight_decay_mask(UpperCAmelCase__  :				List[str]	):
										SCREAMING_SNAKE_CASE           =							traverse_util.flatten_dict(__lowercase	)
										SCREAMING_SNAKE_CASE           =							{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
										return traverse_util.unflatten_dict(__lowercase	)

					SCREAMING_SNAKE_CASE           =							scheduler_fn(__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase	)

					SCREAMING_SNAKE_CASE           =							optax.adamw(learning_rate=__lowercase   ,			weight_decay=__lowercase   ,			mask=__lowercase	)
					return tx, lr






"""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							=     {
    '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       A_  (				__lowercase						,	__lowercase	):
 return torch.atana(__lowercase						,	__lowercase	) / math.pi * 2




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=torch.sin(t * math.pi / 2	) ** 2
 UpperCamelCase_     :					Union[str, Any]   	=(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(__lowercase						,	__lowercase	)





class 				a__					(						A__       ):
     pass





class 				a__					(						nn.Module       ):



     def __init__(							self :Any      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__()

      UpperCamelCase_     :					Optional[int]   	=DiffusionAttnUnetaD(_lowerCamelCase      ,  n_attn_layers=4   )
      UpperCamelCase_     :					List[str]   	=deepcopy(self.diffusion   )
      UpperCamelCase_     :					int   	=torch.quasirandom.SobolEngine(1      ,  scramble=_lowerCamelCase   )




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=MODELS_MAP[model_name]['url']
 os.system(F'''wget {url} ./'''	)

 return F'''./{model_name}.ckpt'''


__SCREAMING_SNAKE_CASE							=     {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__SCREAMING_SNAKE_CASE							=     {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__SCREAMING_SNAKE_CASE							=     {
    '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							=     {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

__SCREAMING_SNAKE_CASE							=     {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

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




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




def       A_  (				__lowercase						,	__lowercase=13	):
 UpperCamelCase_     :					Optional[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_     :					str   	=string[6:]
 elif string.startswith('net.'	):
  UpperCamelCase_     :					List[str]   	=string[4:]

 while string.startswith('main.7.'	):
  depth += 1
  UpperCamelCase_     :					Optional[int]   	=string[7:]

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

 # mid block
 if string[:2].isdigit():
  UpperCamelCase_     :					List[str]   	=string[:2]
  UpperCamelCase_     :					Any   	=string[2:]
 else:
  UpperCamelCase_     :					List[Any]   	=string[0]
  UpperCamelCase_     :					Tuple   	=string[1:]

 if depth == max_depth:
  UpperCamelCase_     :					Optional[Any]   	=MID_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Optional[int]   	='mid_block'
 elif depth > 0 and int(__lowercase	) < 7:
  UpperCamelCase_     :					int   	=DOWN_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Dict   	=F'''down_blocks.{depth}'''
 elif depth > 0 and int(__lowercase	) > 7:
  UpperCamelCase_     :					Optional[Any]   	=UP_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Tuple   	=F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  UpperCamelCase_     :					Optional[int]   	=DEPTH_0_TO_LAYER[layer_num]
  UpperCamelCase_     :					List[str]   	=F'''up_blocks.{max_depth - 1}''' if int(__lowercase	) > 3 else 'down_blocks.0'

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

 UpperCamelCase_     :					Union[str, Any]   	=string_left[1:]

 if "resnets" in new_layer:
  UpperCamelCase_     :					Dict   	=convert_resconv_naming(__lowercase	)
 elif "attentions" in new_layer:
  UpperCamelCase_     :					List[Any]   	=convert_attn_naming(__lowercase	)
  UpperCamelCase_     :					str   	=new_string_left

 if not isinstance(__lowercase						,	__lowercase	):
  UpperCamelCase_     :					int   	=prefix + '.' + new_layer + '.' + string_left
 else:
  UpperCamelCase_     :					List[Any]   	=[prefix + '.' + new_layer + '.' + s for s in string_left]
 return new_string




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	={}
 for k, v in state_dict.items():
  if k.endswith('kernel'	):
   # up- and downsample layers, don't have trainable weights
   continue

  UpperCamelCase_     :					Optional[Any]   	=rename(__lowercase	)

  # check if we need to transform from Conv => Linear for attention
  if isinstance(__lowercase						,	__lowercase	):
   UpperCamelCase_     :					Union[str, Any]   	=transform_conv_attns(__lowercase						,	__lowercase						,	__lowercase	)
  else:
   UpperCamelCase_     :					Union[str, Any]   	=v

 return new_state_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) == 1:
  if len(v.shape	) == 3:
   # weight
   UpperCamelCase_     :					Tuple   	=v[:, :, 0]
  else:
   # bias
   UpperCamelCase_     :					Any   	=v
 else:
  # qkv matrices
  UpperCamelCase_     :					Tuple   	=v.shape[0]
  UpperCamelCase_     :					Optional[Any]   	=trippled_shape // 3
  for i in range(3	):
   if len(v.shape	) == 3:
    UpperCamelCase_     :					str   	=v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    UpperCamelCase_     :					List[str]   	=v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict




def       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 UpperCamelCase_     :					Dict   	=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[Any]   	=download(__lowercase	)

 UpperCamelCase_     :					str   	=MODELS_MAP[model_name]['sample_rate']
 UpperCamelCase_     :					Any   	=MODELS_MAP[model_name]['sample_size']

 UpperCamelCase_     :					str   	=Object()
 UpperCamelCase_     :					List[str]   	=sample_size
 UpperCamelCase_     :					Optional[int]   	=sample_rate
 UpperCamelCase_     :					Tuple   	=0

 UpperCamelCase_     :					Optional[int]   	=UNetaDModel(sample_size=__lowercase						,	sample_rate=__lowercase	)
 UpperCamelCase_     :					Union[str, Any]   	=diffusers_model.state_dict()

 UpperCamelCase_     :					Any   	=DiffusionUncond(__lowercase	)
 orig_model.load_state_dict(torch.load(args.model_path						,	map_location=__lowercase	)['state_dict']	)
 UpperCamelCase_     :					List[str]   	=orig_model.diffusion_ema.eval()
 UpperCamelCase_     :					Tuple   	=orig_model.state_dict()
 UpperCamelCase_     :					Optional[int]   	=rename_orig_weights(__lowercase	)

 UpperCamelCase_     :					Any   	=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

 assert len(__lowercase	) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith('kernel'	) for k in list(__lowercase	)	), 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_     :					List[str]   	=value.squeeze()

  UpperCamelCase_     :					str   	=value

 diffusers_model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=1_00
 UpperCamelCase_     :					List[str]   	=33

 UpperCamelCase_     :					Any   	=IPNDMScheduler(num_train_timesteps=__lowercase	)

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

 UpperCamelCase_     :					str   	=torch.linspace(1						,	0						,	steps + 1						,	device=__lowercase	)[:-1]
 UpperCamelCase_     :					Dict   	=get_crash_schedule(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=DanceDiffusionPipeline(unet=__lowercase						,	scheduler=__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=torch.manual_seed(33	)
 UpperCamelCase_     :					Optional[Any]   	=pipe(num_inference_steps=__lowercase						,	generator=__lowercase	).audios

 UpperCamelCase_     :					Dict   	=sampling.iplms_sample(__lowercase						,	__lowercase						,	__lowercase						,	{}	)
 UpperCamelCase_     :					List[Any]   	=generated.clamp(-1						,	1	)

 UpperCamelCase_     :					Optional[int]   	=(generated - audio).abs().sum()
 UpperCamelCase_     :					Union[str, Any]   	=(generated - audio).abs().max()

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

 print('Diff sum'						,	__lowercase	)
 print('Diff max'						,	__lowercase	)

 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							=     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							=     parser.parse_args()

 main(args)
