





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_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_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






    """simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






    """simple docstring"""


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



class  __lowerCAmelCase	(      _a					):
   pass




class  __lowerCAmelCase	(      nn.Module					):






   def __init__(self ,							__magic_name__   )					->       List[str]:


       '''simple docstring'''



       super().__init__()

       snake_case_						:   Union[str, Any]           =  DiffusionAttnUnetaD(__magic_name__ ,							n_attn_layers=4   )
       snake_case_						:   List[str]           =  deepcopy(self.diffusion   )
       snake_case_						:   Optional[int]           =  torch.quasirandom.SobolEngine(1 ,							scramble=__magic_name__   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






    """simple docstring"""


    snake_case_						:   Dict           =  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 					lowerCamelCase_						(    _UpperCamelCase			)		->						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_						(    _UpperCamelCase			)		->						Dict:






    """simple docstring"""


    for key, value in ATTN_MAP.items():
        if name.startswith(_UpperCamelCase			) and not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
            return name.replace(_UpperCamelCase			,    _UpperCamelCase			)
        elif name.startswith(_UpperCamelCase			):
            return [name.replace(_UpperCamelCase			,    _UpperCamelCase			) for v in value]
    raise ValueError(f'''Attn error with {name}'''			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=13			)		->						Optional[int]:






    """simple docstring"""


    snake_case_						:   Optional[Any]           =  input_string

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

    snake_case_						:   int           =  0
    if string.startswith('''net.3.'''			):
        depth += 1
        snake_case_						:   Optional[Any]           =  string[6:]
    elif string.startswith('''net.'''			):
        snake_case_						:   Tuple           =  string[4:]

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

    if string.startswith('''main.'''			):
        snake_case_						:   Union[str, Any]           =  string[5:]

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

    if depth == max_depth:
        snake_case_						:   Optional[int]           =  MID_NUM_TO_LAYER[layer_num]
        snake_case_						:   int           =  '''mid_block'''
    elif depth > 0 and int(_UpperCamelCase			) < 7:
        snake_case_						:   Union[str, Any]           =  DOWN_NUM_TO_LAYER[layer_num]
        snake_case_						:   Dict           =  f'''down_blocks.{depth}'''
    elif depth > 0 and int(_UpperCamelCase			) > 7:
        snake_case_						:   Optional[Any]           =  UP_NUM_TO_LAYER[layer_num]
        snake_case_						:   str           =  f'''up_blocks.{max_depth - depth - 1}'''
    elif depth == 0:
        snake_case_						:   Optional[Any]           =  DEPTH_0_TO_LAYER[layer_num]
        snake_case_						:   str           =  f'''up_blocks.{max_depth - 1}''' if int(_UpperCamelCase			) > 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_						:   Optional[int]           =  string_left[1:]

    if "resnets" in new_layer:
        snake_case_						:   Optional[Any]           =  convert_resconv_naming(_UpperCamelCase			)
    elif "attentions" in new_layer:
        snake_case_						:   List[str]           =  convert_attn_naming(_UpperCamelCase			)
        snake_case_						:   List[Any]           =  new_string_left

    if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
        snake_case_						:   List[str]           =  prefix + '''.''' + new_layer + '''.''' + string_left
    else:
        snake_case_						:   str           =  [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
    return new_string






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






    """simple docstring"""


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

        snake_case_						:   Any           =  rename(_UpperCamelCase			)

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

    return new_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






    """simple docstring"""


    if len(_UpperCamelCase			) == 1:
        if len(v.shape			) == 3:
            # weight
            snake_case_						:   str           =  v[:, :, 0]
        else:
            # bias
            snake_case_						:   Optional[int]           =  v
    else:
        # qkv matrices
        snake_case_						:   Optional[int]           =  v.shape[0]
        snake_case_						:   List[str]           =  trippled_shape // 3
        for i in range(3			):
            if len(v.shape			) == 3:
                snake_case_						:   Tuple           =  v[i * single_shape : (i + 1) * single_shape, :, 0]
            else:
                snake_case_						:   Optional[int]           =  v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






    """simple docstring"""


    snake_case_						:   Tuple           =  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)

    snake_case_						:   Tuple           =  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_						:   Union[str, Any]           =  download(_UpperCamelCase			)

    snake_case_						:   Dict           =  MODELS_MAP[model_name]['''sample_rate''']
    snake_case_						:   Union[str, Any]           =  MODELS_MAP[model_name]['''sample_size''']

    snake_case_						:   Optional[Any]           =  Object()
    snake_case_						:   List[str]           =  sample_size
    snake_case_						:   Any           =  sample_rate
    snake_case_						:   str           =  0

    snake_case_						:   Any           =  UNetaDModel(sample_size=_UpperCamelCase			,    sample_rate=_UpperCamelCase			)
    snake_case_						:   Any           =  diffusers_model.state_dict()

    snake_case_						:   Optional[Any]           =  DiffusionUncond(_UpperCamelCase			)
    orig_model.load_state_dict(torch.load(args.model_path			,    map_location=_UpperCamelCase			)['''state_dict''']			)
    snake_case_						:   Union[str, Any]           =  orig_model.diffusion_ema.eval()
    snake_case_						:   Optional[int]           =  orig_model.state_dict()
    snake_case_						:   Any           =  rename_orig_weights(_UpperCamelCase			)

    snake_case_						:   Dict           =  set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
    snake_case_						:   Any           =  set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

    assert len(_UpperCamelCase			) == 0, f'''Problem with {renamed_minus_diffusers}'''
    assert all(k.endswith('''kernel'''			) for k in list(_UpperCamelCase			)			), 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_						:   Optional[Any]           =  value.squeeze()

        snake_case_						:   Optional[Any]           =  value

    diffusers_model.load_state_dict(_UpperCamelCase			)

    snake_case_						:   Union[str, Any]           =  100
    snake_case_						:   Optional[int]           =  33

    snake_case_						:   Union[str, Any]           =  IPNDMScheduler(num_train_timesteps=_UpperCamelCase			)

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

    snake_case_						:   Optional[Any]           =  torch.linspace(1			,    0			,    steps + 1			,    device=_UpperCamelCase			)[:-1]
    snake_case_						:   Dict           =  get_crash_schedule(_UpperCamelCase			)

    snake_case_						:   Dict           =  DanceDiffusionPipeline(unet=_UpperCamelCase			,    scheduler=_UpperCamelCase			)

    snake_case_						:   Dict           =  torch.manual_seed(33			)
    snake_case_						:   str           =  pipe(num_inference_steps=_UpperCamelCase			,    generator=_UpperCamelCase			).audios

    snake_case_						:   Optional[Any]           =  sampling.iplms_sample(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    {}			)
    snake_case_						:   Tuple           =  generated.clamp(-1			,    1			)

    snake_case_						:   Optional[Any]           =  (generated - audio).abs().sum()
    snake_case_						:   Dict           =  (generated - audio).abs().max()

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

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

    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)







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


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					Any	      =				'''biogpt'''






			def __init__(self ,							__magic_name__=4_2384 ,							__magic_name__=1024 ,							__magic_name__=24 ,							__magic_name__=16 ,							__magic_name__=4096 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=1024 ,							__magic_name__=0.02 ,							__magic_name__=1e-12 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=2 ,							**__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   List[str]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   List[Any]           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  initializer_range
							snake_case_						:   Optional[int]           =  layer_norm_eps
							snake_case_						:   str           =  scale_embedding
							snake_case_						:   Optional[Any]           =  use_cache
							snake_case_						:   Optional[Any]           =  layerdrop
							snake_case_						:   Optional[Any]           =  activation_dropout
							super().__init__(pad_token_id=__magic_name__ ,							bos_token_id=__magic_name__ ,							eos_token_id=__magic_name__ ,							**__magic_name__   )
