



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': 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 	lowerCamelCase_   (  _a    :		Dict					,						_a    :		str  ):



    '''simple docstring'''




    return torch.atana(_lowercase					,						_lowercase  ) / math.pi * 2







def 	lowerCamelCase_   (  _a    :		Optional[Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Any   						= torch.sin(t * math.pi / 2  ) ** 2
    UpperCAmelCase_			:    List[str]   						= (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(_lowercase					,						_lowercase  )







class 	_snake_case							(			UpperCamelCase_      ):






   '''simple docstring'''






   pass







class 	_snake_case							(			nn.Module      ):






   '''simple docstring'''






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

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







def 	lowerCamelCase_   (  _a    :		Union[str, Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Any   						= 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 	lowerCamelCase_   (  _a    :		Optional[int]  ):



    '''simple docstring'''




    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_   (  _a    :		Union[str, Any]  ):



    '''simple docstring'''




    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 	lowerCamelCase_   (  _a    :		int					,						_a    :		Tuple=13  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Dict   						= input_string

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

    UpperCAmelCase_			:    Union[str, Any]   						= 0
    if string.startswith("""net.3."""  ):
        depth += 1
        UpperCAmelCase_			:    Tuple   						= string[6:]
    elif string.startswith("""net."""  ):
        UpperCAmelCase_			:    List[Any]   						= string[4:]

    while string.startswith("""main.7."""  ):
        depth += 1
        UpperCAmelCase_			:    Any   						= string[7:]

    if string.startswith("""main."""  ):
        UpperCAmelCase_			:    Tuple   						= string[5:]

    # mid block
    if string[:2].isdigit():
        UpperCAmelCase_			:    str   						= string[:2]
        UpperCAmelCase_			:    Dict   						= string[2:]
    else:
        UpperCAmelCase_			:    int   						= string[0]
        UpperCAmelCase_			:    str   						= string[1:]

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

    if "resnets" in new_layer:
        UpperCAmelCase_			:    int   						= convert_resconv_naming(_lowercase  )
    elif "attentions" in new_layer:
        UpperCAmelCase_			:    List[Any]   						= convert_attn_naming(_lowercase  )
        UpperCAmelCase_			:    Union[str, Any]   						= new_string_left

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







def 	lowerCamelCase_   (  _a    :		Optional[Any]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    int   						= {}
    for k, v in state_dict.items():
        if k.endswith("""kernel"""  ):
            # up- and downsample layers, don't have trainable weights
            continue

        UpperCAmelCase_			:    Union[str, Any]   						= rename(_lowercase  )

        # check if we need to transform from Conv => Linear for attention
        if isinstance(_lowercase					,						_lowercase  ):
            UpperCAmelCase_			:    List[Any]   						= transform_conv_attns(_lowercase					,						_lowercase					,						_lowercase  )
        else:
            UpperCAmelCase_			:    Optional[Any]   						= v

    return new_state_dict







def 	lowerCamelCase_   (  _a    :		List[Any]					,						_a    :		Any					,						_a    :		List[Any]  ):



    '''simple docstring'''




    if len(_lowercase  ) == 1:
        if len(v.shape  ) == 3:
            # weight
            UpperCAmelCase_			:    Dict   						= v[:, :, 0]
        else:
            # bias
            UpperCAmelCase_			:    Optional[int]   						= v
    else:
        # qkv matrices
        UpperCAmelCase_			:    int   						= v.shape[0]
        UpperCAmelCase_			:    List[str]   						= 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_			:    Optional[int]   						= v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict







def 	lowerCamelCase_   (  _a    :		Optional[int]  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Tuple   						= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )

    UpperCAmelCase_			:    int   						= 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_			:    List[str]   						= download(_lowercase  )

    UpperCAmelCase_			:    Union[str, Any]   						= MODELS_MAP[model_name]['''sample_rate''']
    UpperCAmelCase_			:    Optional[int]   						= MODELS_MAP[model_name]['''sample_size''']

    UpperCAmelCase_			:    str   						= Object()
    UpperCAmelCase_			:    Optional[int]   						= sample_size
    UpperCAmelCase_			:    int   						= sample_rate
    UpperCAmelCase_			:    Union[str, Any]   						= 0

    UpperCAmelCase_			:    Any   						= UNetaDModel(sample_size=_lowercase					,						sample_rate=_lowercase  )
    UpperCAmelCase_			:    Optional[int]   						= diffusers_model.state_dict()

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

    UpperCAmelCase_			:    Optional[int]   						= set(renamed_state_dict.keys()  ) - set(diffusers_state_dict.keys()  )
    UpperCAmelCase_			:    Union[str, Any]   						= 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_			:    int   						= value

    diffusers_model.load_state_dict(_lowercase  )

    UpperCAmelCase_			:    Dict   						= 100
    UpperCAmelCase_			:    List[Any]   						= 33

    UpperCAmelCase_			:    List[str]   						= IPNDMScheduler(num_train_timesteps=_lowercase  )

    UpperCAmelCase_			:    Union[str, Any]   						= torch.manual_seed(_lowercase  )
    UpperCAmelCase_			:    Optional[int]   						= torch.randn([1, 2, config.sample_size]					,						generator=_lowercase  ).to(_lowercase  )

    UpperCAmelCase_			:    str   						= torch.linspace(1					,						0					,						steps + 1					,						device=_lowercase  )[:-1]
    UpperCAmelCase_			:    Union[str, Any]   						= get_crash_schedule(_lowercase  )

    UpperCAmelCase_			:    Optional[Any]   						= DanceDiffusionPipeline(unet=_lowercase					,						scheduler=_lowercase  )

    UpperCAmelCase_			:    int   						= torch.manual_seed(33  )
    UpperCAmelCase_			:    Optional[int]   						= pipe(num_inference_steps=_lowercase					,						generator=_lowercase  ).audios

    UpperCAmelCase_			:    List[Any]   						= sampling.iplms_sample(_lowercase					,						_lowercase					,						_lowercase					,						{}  )
    UpperCAmelCase_			:    List[str]   						= generated.clamp(-1					,						1  )

    UpperCAmelCase_			:    Optional[Any]   						= (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__":
      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)

import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''		,  [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42		,  dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]		,  )
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							split_dict._to_yaml_list()
						assert len(_lowercase						) == len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							SplitDict._from_yaml_list(_lowercase						)
						for split_name, split_info in split_dict.items():
												# dataset_name field is deprecated, and is therefore not part of the YAML dump
												SCREAMING_SNAKE_CASE							:    Any							=							None
												# the split name of split_dict takes over the name of the split info object
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							split_name
						assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info'''		,  [SplitInfo(), SplitInfo(dataset_name=_lowercase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def       A	(    _lowercase						):
						# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
						# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
						SCREAMING_SNAKE_CASE							:    List[Any]							=							asdict(SplitDict({'''train''': split_info}						)						)
						assert "dataset_name" in split_dict_asdict["train"]
						assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





