

"""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


__A						   =			{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		return torch.atana(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ) / math.pi * 2






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Dict:
		__lowerCAmelCase:       Optional[Any]          =			torch.sin(t * math.pi / 2 ) ** 2
		__lowerCAmelCase:       List[str]          =			(1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )





class     snake_case	(				__snake_case  ):
				pass





class     snake_case	(				nn.Module  ):

				def __init__(	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int])->   Union[str, Any]:



						'''simple docstring'''


						super().__init__()

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






def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:
		__lowerCAmelCase:       Optional[Any]          =			MODELS_MAP[model_name]["url"]
		os.system(F"wget {url} ./" )

		return F"./{model_name}.ckpt"


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

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

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






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		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__    (    __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		for key, value in ATTN_MAP.items():
				if name.startswith(__SCREAMING_SNAKE_CASE ) and not isinstance(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ):
						return name.replace(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
				elif name.startswith(__SCREAMING_SNAKE_CASE ):
						return [name.replace(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ) for v in value]
		raise ValueError(F"Attn error with {name}" )






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=1_3 )	->	str:
		__lowerCAmelCase:       Tuple          =			input_string

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

		__lowerCAmelCase:       Optional[int]          =			0
		if string.startswith("net.3." ):
				depth += 1
				__lowerCAmelCase:       Optional[Any]          =			string[6:]
		elif string.startswith("net." ):
				__lowerCAmelCase:       str          =			string[4:]

		while string.startswith("main.7." ):
				depth += 1
				__lowerCAmelCase:       Union[str, Any]          =			string[7:]

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

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

		if depth == max_depth:
				__lowerCAmelCase:       Any          =			MID_NUM_TO_LAYER[layer_num]
				__lowerCAmelCase:       List[str]          =			"mid_block"
		elif depth > 0 and int(__SCREAMING_SNAKE_CASE ) < 7:
				__lowerCAmelCase:       Any          =			DOWN_NUM_TO_LAYER[layer_num]
				__lowerCAmelCase:       List[str]          =			F"down_blocks.{depth}"
		elif depth > 0 and int(__SCREAMING_SNAKE_CASE ) > 7:
				__lowerCAmelCase:       List[str]          =			UP_NUM_TO_LAYER[layer_num]
				__lowerCAmelCase:       List[Any]          =			F"up_blocks.{max_depth - depth - 1}"
		elif depth == 0:
				__lowerCAmelCase:       Optional[Any]          =			DEPTH_0_TO_LAYER[layer_num]
				__lowerCAmelCase:       Optional[Any]          =			F"up_blocks.{max_depth - 1}" if int(__SCREAMING_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}." )

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

		if "resnets" in new_layer:
				__lowerCAmelCase:       List[Any]          =			convert_resconv_naming(__SCREAMING_SNAKE_CASE )
		elif "attentions" in new_layer:
				__lowerCAmelCase:       str          =			convert_attn_naming(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase:       List[str]          =			new_string_left

		if not isinstance(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase:       List[str]          =			prefix + "." + new_layer + "." + string_left
		else:
				__lowerCAmelCase:       Any          =			[prefix + "." + new_layer + "." + s for s in string_left]
		return new_string






def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       int          =			{}
		for k, v in state_dict.items():
				if k.endswith("kernel" ):
						# up- and downsample layers, don't have trainable weights
						continue

				__lowerCAmelCase:       int          =			rename(__SCREAMING_SNAKE_CASE )

				# check if we need to transform from Conv => Linear for attention
				if isinstance(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ):
						__lowerCAmelCase:       Optional[int]          =			transform_conv_attns(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
				else:
						__lowerCAmelCase:       Dict          =			v

		return new_state_dict






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	List[str]:
		if len(__SCREAMING_SNAKE_CASE ) == 1:
				if len(v.shape ) == 3:
						# weight
						__lowerCAmelCase:       int          =			v[:, :, 0]
				else:
						# bias
						__lowerCAmelCase:       List[Any]          =			v
		else:
				# qkv matrices
				__lowerCAmelCase:       Dict          =			v.shape[0]
				__lowerCAmelCase:       Tuple          =			trippled_shape // 3
				for i in range(3 ):
						if len(v.shape ) == 3:
								__lowerCAmelCase:       Optional[Any]          =			v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								__lowerCAmelCase:       Tuple          =			v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Tuple:
		__lowerCAmelCase:       Dict          =			torch.device("cuda" if torch.cuda.is_available() else "cpu" )

		__lowerCAmelCase:       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()}"
				__lowerCAmelCase:       str          =			download(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Optional[int]          =			MODELS_MAP[model_name]["sample_rate"]
		__lowerCAmelCase:       int          =			MODELS_MAP[model_name]["sample_size"]

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

		__lowerCAmelCase:       str          =			UNetaDModel(sample_size=__SCREAMING_SNAKE_CASE					, sample_rate=__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Union[str, Any]          =			diffusers_model.state_dict()

		__lowerCAmelCase:       Union[str, Any]          =			DiffusionUncond(__SCREAMING_SNAKE_CASE )
		orig_model.load_state_dict(torch.load(args.model_path					, map_location=__SCREAMING_SNAKE_CASE )["state_dict"] )
		__lowerCAmelCase:       Union[str, Any]          =			orig_model.diffusion_ema.eval()
		__lowerCAmelCase:       List[str]          =			orig_model.state_dict()
		__lowerCAmelCase:       Any          =			rename_orig_weights(__SCREAMING_SNAKE_CASE )

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

		assert len(__SCREAMING_SNAKE_CASE ) == 0, F"Problem with {renamed_minus_diffusers}"
		assert all(k.endswith("kernel" ) for k in list(__SCREAMING_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":
						__lowerCAmelCase:       str          =			value.squeeze()

				__lowerCAmelCase:       List[Any]          =			value

		diffusers_model.load_state_dict(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Optional[Any]          =			1_0_0
		__lowerCAmelCase:       Union[str, Any]          =			3_3

		__lowerCAmelCase:       Union[str, Any]          =			IPNDMScheduler(num_train_timesteps=__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       str          =			torch.manual_seed(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Tuple          =			torch.randn([1, 2, config.sample_size]					, generator=__SCREAMING_SNAKE_CASE ).to(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       List[Any]          =			torch.linspace(1					, 0					, steps + 1					, device=__SCREAMING_SNAKE_CASE )[:-1]
		__lowerCAmelCase:       Any          =			get_crash_schedule(__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Tuple          =			DanceDiffusionPipeline(unet=__SCREAMING_SNAKE_CASE					, scheduler=__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       Any          =			torch.manual_seed(3_3 )
		__lowerCAmelCase:       Optional[Any]          =			pipe(num_inference_steps=__SCREAMING_SNAKE_CASE					, generator=__SCREAMING_SNAKE_CASE ).audios

		__lowerCAmelCase:       Dict          =			sampling.iplms_sample(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, {} )
		__lowerCAmelCase:       Tuple          =			generated.clamp(-1					, 1 )

		__lowerCAmelCase:       Union[str, Any]          =			(generated - audio).abs().sum()
		__lowerCAmelCase:       List[Any]          =			(generated - audio).abs().max()

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

		print("Diff sum"					, __SCREAMING_SNAKE_CASE )
		print("Diff max"					, __SCREAMING_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__":
			__A						   =			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.")
			__A						   =			parser.parse_args()

			main(args)





"""simple docstring"""


from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url






@pytest.mark.parametrize("repo_id"					, ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path"					, ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametrize("revision"					, [None, "v2"] )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       Optional[Any]          =			hf_hub_url(repo_id=__SCREAMING_SNAKE_CASE					, path=__SCREAMING_SNAKE_CASE					, revision=__SCREAMING_SNAKE_CASE )
		assert url == F"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(__SCREAMING_SNAKE_CASE )}"


