



"""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': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Tuple:
							return torch.atana(lowerCamelCase__				,      lowerCamelCase__			) / math.pi * 2

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






class      UpperCAmelCase__		(      UpperCamelCase     ):
		pass






class      UpperCAmelCase__		(      nn.Module     ):



		def __init__( self  :    Optional[int]      ,							snake_case  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									super().__init__()

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



def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:
							A         =					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   lowerCAmelCase__			(     lowerCamelCase__			) ->							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Optional[Any]:
							for key, value in ATTN_MAP.items():
														if name.startswith(lowerCamelCase__			) and not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
																					return name.replace(lowerCamelCase__				,      lowerCamelCase__			)
														elif name.startswith(lowerCamelCase__			):
																					return [name.replace(lowerCamelCase__				,      lowerCamelCase__			) for v in value]
							raise ValueError(f"""Attn error with {name}"""			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=13			) ->							Any:
							A         =					input_string

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

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

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

							if string.startswith('main.'			):
														A         =					string[5:]

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

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

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

							A         =					string_left[1:]

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

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

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Optional[Any]:
							A         =					{}
							for k, v in state_dict.items():
														if k.endswith('kernel'			):
																					# up- and downsample layers, don't have trainable weights
																					continue

														A         =					rename(lowerCamelCase__			)

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

							return new_state_dict

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							List[Any]:
							if len(lowerCamelCase__			) == 1:
														if len(v.shape			) == 3:
																					# weight
																					A         =					v[:, :, 0]
														else:
																					# bias
																					A         =					v
							else:
														# qkv matrices
														A         =					v.shape[0]
														A         =					trippled_shape // 3
														for i in range(3			):
																					if len(v.shape			) == 3:
																												A         =					v[i * single_shape : (i + 1) * single_shape, :, 0]
																					else:
																												A         =					v[i * single_shape : (i + 1) * single_shape]
							return new_state_dict

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

							A         =					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()}"""
														A         =					download(lowerCamelCase__			)

							A         =					MODELS_MAP[model_name]['sample_rate']
							A         =					MODELS_MAP[model_name]['sample_size']

							A         =					Object()
							A         =					sample_size
							A         =					sample_rate
							A         =					0

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

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

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

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

														A         =					value

							diffusers_model.load_state_dict(lowerCamelCase__			)

							A         =					100
							A         =					33

							A         =					IPNDMScheduler(num_train_timesteps=lowerCamelCase__			)

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

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

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

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

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

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

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

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

							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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A    =			logging.get_logger(__name__)






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     ["""pixel_values"""]



		def __init__( self  :    int      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Dict[str, int]] = None      ,							snake_case  :    PILImageResampling = PILImageResampling.BICUBIC      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						None:






									'''simple docstring'''

									super().__init__(**snake_case				)
									A         =					size if size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case				)
									A         =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
									A         =					get_size_dict(snake_case      ,							default_to_square=snake_case      ,							param_name='crop_size'				)

									A         =					do_resize
									A         =					do_rescale
									A         =					do_normalize
									A         =					do_center_crop
									A         =					crop_size
									A         =					size
									A         =					resample
									A         =					rescale_factor
									A         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    PILImageResampling = PILImageResampling.BILINEAR      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)

									if "shortest_edge" in size:
																A         =					get_resize_output_image_size(snake_case      ,							size=size['shortest_edge']      ,							default_to_square=snake_case				)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
									elif "height" in size and "width" in size:
																A         =					(size['height'], size['width'])
									else:
																raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""				)
									return resize(snake_case      ,							size=snake_case      ,							resample=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Dict[str, int]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    List[Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									A         =					get_size_dict(snake_case				)
									if "height" not in size or "width" not in size:
																raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
									return center_crop(snake_case      ,							size=(size['height'], size['width'])      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    np.ndarray      ,							snake_case  :    float      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Dict				)			->						np.ndarray:






									'''simple docstring'''

									return rescale(snake_case      ,							scale=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    np.ndarray      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Union[float, List[float]]      ,							snake_case  :    Optional[Union[str, ChannelDimension]] = None      ,							**snake_case  :    Union[str, Any]      ,							)			->						np.ndarray:






									'''simple docstring'''

									return normalize(snake_case      ,							mean=snake_case      ,							std=snake_case      ,							data_format=snake_case      ,							**snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    ImageInput      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    PILImageResampling = None      ,							snake_case  :    bool = None      ,							snake_case  :    int = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[float] = None      ,							snake_case  :    Optional[bool] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[float, List[float]]] = None      ,							snake_case  :    Optional[Union[str, TensorType]] = None      ,							snake_case  :    Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**snake_case  :    List[str]      ,							)			->						BatchFeature:






									'''simple docstring'''

									A         =					do_resize if do_resize is not None else self.do_resize
									A         =					do_rescale if do_rescale is not None else self.do_rescale
									A         =					do_normalize if do_normalize is not None else self.do_normalize
									A         =					do_center_crop if do_center_crop is not None else self.do_center_crop
									A         =					crop_size if crop_size is not None else self.crop_size
									A         =					get_size_dict(snake_case      ,							param_name='crop_size'      ,							default_to_square=snake_case				)
									A         =					resample if resample is not None else self.resample
									A         =					rescale_factor if rescale_factor is not None else self.rescale_factor
									A         =					image_mean if image_mean is not None else self.image_mean
									A         =					image_std if image_std is not None else self.image_std

									A         =					size if size is not None else self.size
									A         =					get_size_dict(snake_case				)

									if not is_batched(snake_case				):
																A         =					[images]

									if not valid_images(snake_case				):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'				)

									if do_resize and size is None:
																raise ValueError('Size must be specified if do_resize is True.'				)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'				)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

									# All transformations expect numpy arrays.
									A         =					[to_numpy_array(snake_case				) for image in images]

									if do_resize:
																A         =					[self.resize(image=snake_case      ,							size=snake_case      ,							resample=snake_case				) for image in images]

									if do_center_crop:
																A         =					[self.center_crop(image=snake_case      ,							size=snake_case				) for image in images]

									if do_rescale:
																A         =					[self.rescale(image=snake_case      ,							scale=snake_case				) for image in images]

									if do_normalize:
																A         =					[self.normalize(image=snake_case      ,							mean=snake_case      ,							std=snake_case				) for image in images]

									A         =					[to_channel_dimension_format(snake_case      ,							snake_case				) for image in images]

									A         =					{'pixel_values': images}
									return BatchFeature(data=snake_case      ,							tensor_type=snake_case				)



