


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	)						->   Any:
							return torch.atana(A			,   A	) / math.pi * 2


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




class 		a__    (							a__  ):





							'''simple docstring'''






							pass




class 		a__    (							nn.Module  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							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	)						->   List[Any]:
							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	)						->   str:
							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	)						->   int:
							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	)						->   Optional[int]:
							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[int]:
							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	)						->   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	)						->   Union[str, Any]:
							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 numpy as np

import datasets


__UpperCAmelCase       =							'''
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''

__UpperCAmelCase       =							'''\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
'''

__UpperCAmelCase       =							'''
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {\'mahalanobis\': array([0.5])}
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,    _KWARGS_DESCRIPTION  )
class 		a__    (							datasets.Metric  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														return datasets.MetricInfo(
														    description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
														        {
														            '''X''': datasets.Sequence(datasets.Value('''float''' ,   id='''sequence'''       ) ,   id='''X'''       ),
														        }       ) ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							str:
														# convert to numpy arrays
														lowerCAmelCase__	       =     np.array(lowerCamelCase_       )
														lowerCAmelCase__	       =     np.array(lowerCamelCase_       )

														# Assert that arrays are 2D
														if len(X.shape       ) != 2:
																					raise ValueError('''Expected `X` to be a 2D vector'''       )
														if len(reference_distribution.shape       ) != 2:
																					raise ValueError('''Expected `reference_distribution` to be a 2D vector'''       )
														if reference_distribution.shape[0] < 2:
																					raise ValueError(
																					    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''       )

														# Get mahalanobis distance for each prediction
														lowerCAmelCase__	       =     X - np.mean(lowerCamelCase_       )
														lowerCAmelCase__	       =     np.cov(reference_distribution.T       )
														try:
																					lowerCAmelCase__	       =     np.linalg.inv(lowerCamelCase_       )
														except np.linalg.LinAlgError:
																					lowerCAmelCase__	       =     np.linalg.pinv(lowerCamelCase_       )
														lowerCAmelCase__	       =     np.dot(lowerCamelCase_ ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     np.dot(lowerCamelCase_ ,   X_minus_mu.T       ).diagonal()

														return {"mahalanobis": mahal_dist}