


"""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


__snake_case         =					{
    """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		(		lowercase						:						List[Any]		,							lowercase						:						List[str]				)			->		Optional[int]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		Dict:


    """simple docstring"""





    snake_case					:   Any         =					torch.sin(t * math.pi / 2				) ** 2
    snake_case					:   Dict         =					(1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(lowercase		,							lowercase				)







class _lowerCAmelCase       (   snake_case_			):
   pass







class _lowerCAmelCase       (   nn.Module			):







   def __init__(			self		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       super().__init__()

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







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		int:


    """simple docstring"""





    snake_case					:   Tuple         =					MODELS_MAP[model_name]["url"]
    os.system(F'wget {url} ./'				)

    return F'./{model_name}.ckpt'


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

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

__snake_case         =					{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}







def 						__lowerCAmelCase		(		lowercase						:						int				)			->		List[Any]:


    """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		(		lowercase						:						Optional[Any]				)			->		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		(		lowercase						:						Union[str, Any]		,							lowercase						:						Optional[int]=13				)			->		Any:


    """simple docstring"""





    snake_case					:   List[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					:   Dict         =					string[6:]
    elif string.startswith("net."				):
        snake_case					:   List[Any]         =					string[4:]

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

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

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

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

    snake_case					:   Optional[int]         =					string_left[1:]

    if "resnets" in new_layer:
        snake_case					:   Optional[int]         =					convert_resconv_naming(lowercase				)
    elif "attentions" in new_layer:
        snake_case					:   str         =					convert_attn_naming(lowercase				)
        snake_case					:   Optional[int]         =					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 						__lowerCAmelCase		(		lowercase						:						Dict				)			->		Tuple:


    """simple docstring"""





    snake_case					:   Optional[int]         =					{}
    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					:   Tuple         =					transform_conv_attns(lowercase		,							lowercase		,							lowercase				)
        else:
            snake_case					:   Optional[int]         =					v

    return new_state_dict







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						Tuple		,							lowercase						:						Union[str, Any]				)			->		List[Any]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						List[str]				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

    snake_case					:   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					:   List[str]         =					download(lowercase				)

    snake_case					:   Dict         =					MODELS_MAP[model_name]["sample_rate"]
    snake_case					:   Any         =					MODELS_MAP[model_name]["sample_size"]

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

    snake_case					:   List[str]         =					UNetaDModel(sample_size=lowercase		,							sample_rate=lowercase				)
    snake_case					:   Tuple         =					diffusers_model.state_dict()

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

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

        snake_case					:   List[str]         =					value

    diffusers_model.load_state_dict(lowercase				)

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

    snake_case					:   str         =					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					:   Dict         =					torch.linspace(1		,							0		,							steps + 1		,							device=lowercase				)[:-1]
    snake_case					:   str         =					get_crash_schedule(lowercase				)

    snake_case					:   Dict         =					DanceDiffusionPipeline(unet=lowercase		,							scheduler=lowercase				)

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

    snake_case					:   Union[str, Any]         =					sampling.iplms_sample(lowercase		,							lowercase		,							lowercase		,							{}				)
    snake_case					:   List[str]         =					generated.clamp(-1		,							1				)

    snake_case					:   Optional[Any]         =					(generated - audio).abs().sum()
    snake_case					:   List[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__":
  __snake_case         =					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.""")
  __snake_case         =					parser.parse_args()

  main(args)









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   str         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





