


'''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


__UpperCAmelCase					       =							{
    "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       _snake_case     (  A				,					A )		->					int:
							return torch.atana(a_				,					a_ ) / math.pi * 2


def       _snake_case     (  A )		->					List[Any]:
							lowerCAmelCase__      =			torch.sin(t * math.pi / 2 ) ** 2
							lowerCAmelCase__      =			(1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(a_				,					a_ )




class 	a__       (							_UpperCAmelCase						):





							'''simple docstring'''






							pass




class 	a__       (							nn.Module						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__()

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


def       _snake_case     (  A )		->					Optional[int]:
							lowerCAmelCase__      =			MODELS_MAP[model_name]['''url''']
							os.system(F"""wget {url} ./""" )

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


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

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

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


def       _snake_case     (  A )		->					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       _snake_case     (  A )		->					Optional[Any]:






							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       _snake_case     (  A				,					A=13 )		->					Tuple:
							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       _snake_case     (  A )		->					Optional[Any]:
							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       _snake_case     (  A				,					A				,					A )		->					Union[str, Any]:






							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       _snake_case     (  A )		->					int:
							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__":
							__UpperCAmelCase					       =							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.''')
							__UpperCAmelCase					       =							parser.parse_args()

							main(args)



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




@slow
@require_torch_gpu
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2