


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     :    Dict 	=				{
    '''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 	_UpperCAmelCase							(  UpperCAmelCase   :      Optional[Any]    ,						UpperCAmelCase   :      List[str]   ):
      """simple docstring"""




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





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Optional[int]   ):
      """simple docstring"""




      __lowerCamelCase  :   Any							    =    torch.sin(t * math.pi / 2   ) ** 2
      __lowerCamelCase  :   str							    =    (1 - sigma**2) ** 0.5
      return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_    ,						SCREAMING_SNAKE_CASE_   )



class 				_UpperCamelCase			(    _a    ):

   '''simple docstring'''







   pass



class 				_UpperCamelCase			(    nn.Module    ):

   '''simple docstring'''







   def __init__(	self	:			Optional[int]							,	_lowerCamelCase	:			str				):






         '''simple docstring'''



         super().__init__()

         __lowerCamelCase  :   Tuple							    =    DiffusionAttnUnetaD(__UpperCamelCase							,	n_attn_layers=4				)
         __lowerCamelCase  :   int							    =    deepcopy(self.diffusion				)
         __lowerCamelCase  :   Union[str, Any]							    =    torch.quasirandom.SobolEngine(1							,	scramble=__UpperCamelCase				)





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Optional[Any]   ):
      """simple docstring"""




      __lowerCamelCase  :   List[Any]							    =    MODELS_MAP[model_name]["""url"""]
      os.system(f"""wget {url} ./"""   )

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


__UpperCamelCase     :    Tuple 	=				{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__UpperCamelCase     :    Any 	=				{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__UpperCamelCase     :    str 	=				{
    '''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     :    Optional[int] 	=				{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__UpperCamelCase     :    List[str] 	=				{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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





def 	_UpperCAmelCase							(  UpperCAmelCase   :      Union[str, 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 	_UpperCAmelCase							(  UpperCAmelCase   :      Optional[int]   ):
      """simple docstring"""




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





def 	_UpperCAmelCase							(  UpperCAmelCase   :      int    ,						UpperCAmelCase   :      str=13   ):
      """simple docstring"""




      __lowerCamelCase  :   Tuple							    =    input_string

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

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

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

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

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

      if depth == max_depth:
            __lowerCamelCase  :   Union[str, Any]							    =    MID_NUM_TO_LAYER[layer_num]
            __lowerCamelCase  :   Any							    =    """mid_block"""
      elif depth > 0 and int(SCREAMING_SNAKE_CASE_   ) < 7:
            __lowerCamelCase  :   Any							    =    DOWN_NUM_TO_LAYER[layer_num]
            __lowerCamelCase  :   Union[str, Any]							    =    f"""down_blocks.{depth}"""
      elif depth > 0 and int(SCREAMING_SNAKE_CASE_   ) > 7:
            __lowerCamelCase  :   int							    =    UP_NUM_TO_LAYER[layer_num]
            __lowerCamelCase  :   Any							    =    f"""up_blocks.{max_depth - depth - 1}"""
      elif depth == 0:
            __lowerCamelCase  :   Dict							    =    DEPTH_0_TO_LAYER[layer_num]
            __lowerCamelCase  :   Union[str, Any]							    =    f"""up_blocks.{max_depth - 1}""" if int(SCREAMING_SNAKE_CASE_   ) > 3 else """down_blocks.0"""

      if not string_left.startswith("""."""   ):
            raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""   )

      __lowerCamelCase  :   List[str]							    =    string_left[1:]

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

      if not isinstance(SCREAMING_SNAKE_CASE_    ,						SCREAMING_SNAKE_CASE_   ):
            __lowerCamelCase  :   Dict							    =    prefix + """.""" + new_layer + """.""" + string_left
      else:
            __lowerCamelCase  :   Union[str, Any]							    =    [prefix + """.""" + new_layer + """.""" + s for s in string_left]
      return new_string





def 	_UpperCAmelCase							(  UpperCAmelCase   :      str   ):
      """simple docstring"""




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

            __lowerCamelCase  :   Union[str, Any]							    =    rename(SCREAMING_SNAKE_CASE_   )

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

      return new_state_dict





def 	_UpperCAmelCase							(  UpperCAmelCase   :      List[Any]    ,						UpperCAmelCase   :      Union[str, Any]    ,						UpperCAmelCase   :      Union[str, Any]   ):
      """simple docstring"""




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





def 	_UpperCAmelCase							(  UpperCAmelCase   :      List[Any]   ):
      """simple docstring"""




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

      __lowerCamelCase  :   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()}"""
            __lowerCamelCase  :   str							    =    download(SCREAMING_SNAKE_CASE_   )

      __lowerCamelCase  :   List[str]							    =    MODELS_MAP[model_name]["""sample_rate"""]
      __lowerCamelCase  :   Union[str, Any]							    =    MODELS_MAP[model_name]["""sample_size"""]

      __lowerCamelCase  :   Optional[Any]							    =    Object()
      __lowerCamelCase  :   Union[str, Any]							    =    sample_size
      __lowerCamelCase  :   str							    =    sample_rate
      __lowerCamelCase  :   Tuple							    =    0

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

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

      __lowerCamelCase  :   Optional[Any]							    =    set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
      __lowerCamelCase  :   Tuple							    =    set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

      assert len(SCREAMING_SNAKE_CASE_   ) == 0, f"""Problem with {renamed_minus_diffusers}"""
      assert all(k.endswith("""kernel"""   ) for k in list(SCREAMING_SNAKE_CASE_   )   ), 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  :   str							    =    value.squeeze()

            __lowerCamelCase  :   Optional[Any]							    =    value

      diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_   )

      __lowerCamelCase  :   Union[str, Any]							    =    100
      __lowerCamelCase  :   Any							    =    33

      __lowerCamelCase  :   int							    =    IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_   )

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

      __lowerCamelCase  :   str							    =    torch.linspace(1    ,						0    ,						steps + 1    ,						device=SCREAMING_SNAKE_CASE_   )[:-1]
      __lowerCamelCase  :   Optional[Any]							    =    get_crash_schedule(SCREAMING_SNAKE_CASE_   )

      __lowerCamelCase  :   Tuple							    =    DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_    ,						scheduler=SCREAMING_SNAKE_CASE_   )

      __lowerCamelCase  :   List[str]							    =    torch.manual_seed(33   )
      __lowerCamelCase  :   Any							    =    pipe(num_inference_steps=SCREAMING_SNAKE_CASE_    ,						generator=SCREAMING_SNAKE_CASE_   ).audios

      __lowerCamelCase  :   List[str]							    =    sampling.iplms_sample(SCREAMING_SNAKE_CASE_    ,						SCREAMING_SNAKE_CASE_    ,						SCREAMING_SNAKE_CASE_    ,						{}   )
      __lowerCamelCase  :   int							    =    generated.clamp(-1    ,						1   )

      __lowerCamelCase  :   int							    =    (generated - audio).abs().sum()
      __lowerCamelCase  :   int							    =    (generated - audio).abs().max()

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

      print("""Diff sum"""    ,						SCREAMING_SNAKE_CASE_   )
      print("""Diff max"""    ,						SCREAMING_SNAKE_CASE_   )

      assert diff_max < 1e-3, f"""Diff max: {diff_max} is too much :-/"""

      print(f"""Conversion for {model_name} successful!"""   )


if __name__ == "__main__":
    __UpperCamelCase     :    Optional[int] 	=				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     :    Any 	=				parser.parse_args()

    main(args)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A		: Dict								=     {
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: Optional[Any]								=     [
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		A		: List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)