
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


__lowerCamelCase				=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 13_10_72,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
}


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					Optional[int]       ):
       return torch.atana(lowercase__							,   lowercase__       ) / math.pi * 2


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]       ):
       snake_case			:  Tuple     							= torch.sin(t * math.pi / 2       ) ** 2
       snake_case			:  List[str]     							= (1 - sigma**2) ** 0.5
       return alpha_sigma_to_t(lowercase__							,   lowercase__       )






class     UpperCAmelCase						(		A_					):
   pass






class     UpperCAmelCase						(		nn.Module					):



   def __init__(self						:   Tuple       ,		snake_case__						:   Dict					)						->      Tuple:


          '''simple docstring'''


          super().__init__()

          snake_case			:  Union[str, Any]     							= DiffusionAttnUnetaD(A_       ,		n_attn_layers=4					)
          snake_case			:  Union[str, Any]     							= deepcopy(self.diffusion					)
          snake_case			:  Optional[Any]     							= torch.quasirandom.SobolEngine(1       ,		scramble=A_					)



def      UpperCamelCase   (       __lowerCamelCase	:					List[str]       ):
       snake_case			:  Union[str, Any]     							= MODELS_MAP[model_name]["url"]
       os.system(f"""wget {url} ./"""       )

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


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

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

__lowerCamelCase				=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def      UpperCamelCase   (       __lowerCamelCase	:					int       ):
       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      UpperCamelCase   (       __lowerCamelCase	:					str       ):
       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      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					Union[str, Any]=13       ):
       snake_case			:  Dict     							= input_string

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

       snake_case			:  Dict     							= 0
       if string.startswith("net.3."       ):
              depth += 1
              snake_case			:  List[str]     							= string[6:]
       elif string.startswith("net."       ):
              snake_case			:  str     							= string[4:]

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

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

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

       if depth == max_depth:
              snake_case			:  Union[str, Any]     							= MID_NUM_TO_LAYER[layer_num]
              snake_case			:  Any     							= "mid_block"
       elif depth > 0 and int(lowercase__       ) < 7:
              snake_case			:  List[str]     							= DOWN_NUM_TO_LAYER[layer_num]
              snake_case			:  Union[str, Any]     							= f"""down_blocks.{depth}"""
       elif depth > 0 and int(lowercase__       ) > 7:
              snake_case			:  int     							= UP_NUM_TO_LAYER[layer_num]
              snake_case			:  Optional[Any]     							= f"""up_blocks.{max_depth - depth - 1}"""
       elif depth == 0:
              snake_case			:  str     							= DEPTH_0_TO_LAYER[layer_num]
              snake_case			:  List[Any]     							= 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}."""       )

       snake_case			:  str     							= string_left[1:]

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

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


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]       ):
       snake_case			:  Union[str, Any]     							= {}
       for k, v in state_dict.items():
              if k.endswith("kernel"       ):
                     # up- and downsample layers, don't have trainable weights
                     continue

              snake_case			:  List[Any]     							= rename(lowercase__       )

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

       return new_state_dict


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					List[Any]       ):
       if len(lowercase__       ) == 1:
              if len(v.shape       ) == 3:
                     # weight
                     snake_case			:  List[str]     							= v[:, :, 0]
              else:
                     # bias
                     snake_case			:  str     							= v
       else:
              # qkv matrices
              snake_case			:  int     							= v.shape[0]
              snake_case			:  Optional[int]     							= trippled_shape // 3
              for i in range(3       ):
                     if len(v.shape       ) == 3:
                            snake_case			:  Optional[int]     							= v[i * single_shape : (i + 1) * single_shape, :, 0]
                     else:
                            snake_case			:  Any     							= v[i * single_shape : (i + 1) * single_shape]
       return new_state_dict


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):
       snake_case			:  List[Any]     							= torch.device("cuda" if torch.cuda.is_available() else "cpu"       )

       snake_case			:  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()}"""
              snake_case			:  Optional[Any]     							= download(lowercase__       )

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

       snake_case			:  Optional[int]     							= Object()
       snake_case			:  List[Any]     							= sample_size
       snake_case			:  Any     							= sample_rate
       snake_case			:  Tuple     							= 0

       snake_case			:  str     							= UNetaDModel(sample_size=lowercase__							,   sample_rate=lowercase__       )
       snake_case			:  str     							= diffusers_model.state_dict()

       snake_case			:  int     							= DiffusionUncond(lowercase__       )
       orig_model.load_state_dict(torch.load(args.model_path							,   map_location=lowercase__       )["state_dict"]       )
       snake_case			:  Optional[Any]     							= orig_model.diffusion_ema.eval()
       snake_case			:  Any     							= orig_model.state_dict()
       snake_case			:  str     							= rename_orig_weights(lowercase__       )

       snake_case			:  List[str]     							= set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
       snake_case			:  Tuple     							= 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":
                     snake_case			:  Any     							= value.squeeze()

              snake_case			:  Any     							= value

       diffusers_model.load_state_dict(lowercase__       )

       snake_case			:  List[Any]     							= 100
       snake_case			:  List[str]     							= 33

       snake_case			:  Optional[int]     							= IPNDMScheduler(num_train_timesteps=lowercase__       )

       snake_case			:  Optional[Any]     							= torch.manual_seed(lowercase__       )
       snake_case			:  int     							= torch.randn([1, 2, config.sample_size]							,   generator=lowercase__       ).to(lowercase__       )

       snake_case			:  Optional[Any]     							= torch.linspace(1							,   0							,   steps + 1							,   device=lowercase__       )[:-1]
       snake_case			:  int     							= get_crash_schedule(lowercase__       )

       snake_case			:  Any     							= DanceDiffusionPipeline(unet=lowercase__							,   scheduler=lowercase__       )

       snake_case			:  List[Any]     							= torch.manual_seed(33       )
       snake_case			:  Any     							= pipe(num_inference_steps=lowercase__							,   generator=lowercase__       ).audios

       snake_case			:  int     							= sampling.iplms_sample(lowercase__							,   lowercase__							,   lowercase__							,   {}       )
       snake_case			:  List[Any]     							= generated.clamp(-1							,   1       )

       snake_case			:  int     							= (generated - audio).abs().sum()
       snake_case			:  Tuple     							= (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__":
   __lowerCamelCase				=			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.""")
   __lowerCamelCase				=			parser.parse_args()

   main(args)









"""simple docstring"""




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

										super().__init__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1     ,							int((1.0 - self.overlap) * self.chunk_length   )   )




							@property
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




