




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       :	List[Any]									=     {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 65_536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48_000,
        "sample_size": 131_072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16_000,
        "sample_size": 65_536,
    },
}

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any  ):


      '''simple docstring'''



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







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 pass







class 						A(		nn.Module     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[int]		,							A_      :						int						)     ->			List[str]:

       """simple docstring"""






       super().__init__()

       lowerCamelCase_	  =					DiffusionAttnUnetaD(A_		,							n_attn_layers=4						)
       lowerCamelCase_	  =					deepcopy(self.diffusion						)
       lowerCamelCase_	  =					torch.quasirandom.SobolEngine(1		,							scramble=A_						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					MODELS_MAP[model_name]['url']
      os.system(f"""wget {url} ./"""  )

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


lowerCamelCase       :	str									=     {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
lowerCamelCase       :	str									=     {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
lowerCamelCase       :	List[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",
}
lowerCamelCase       :	Union[str, Any]									=     {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[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     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''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     _SCREAMING_SNAKE_CASE    (							lowercase			: Any			,							lowercase			: Union[str, Any]=13  ):


      '''simple docstring'''



      lowerCamelCase_	  =					input_string

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

      lowerCamelCase_	  =					0
      if string.startswith('net.3.'  ):
            depth += 1
            lowerCamelCase_	  =					string[6:]
      elif string.startswith('net.'  ):
            lowerCamelCase_	  =					string[4:]

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

      if string.startswith('main.'  ):
            lowerCamelCase_	  =					string[5:]

      # mid block
      if string[:2].isdigit():
            lowerCamelCase_	  =					string[:2]
            lowerCamelCase_	  =					string[2:]
      else:
            lowerCamelCase_	  =					string[0]
            lowerCamelCase_	  =					string[1:]

      if depth == max_depth:
            lowerCamelCase_	  =					MID_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					'mid_block'
      elif depth > 0 and int(lowercase  ) < 7:
            lowerCamelCase_	  =					DOWN_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					f"""down_blocks.{depth}"""
      elif depth > 0 and int(lowercase  ) > 7:
            lowerCamelCase_	  =					UP_NUM_TO_LAYER[layer_num]
            lowerCamelCase_	  =					f"""up_blocks.{max_depth - depth - 1}"""
      elif depth == 0:
            lowerCamelCase_	  =					DEPTH_0_TO_LAYER[layer_num]
            lowerCamelCase_	  =					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}."""  )

      lowerCamelCase_	  =					string_left[1:]

      if "resnets" in new_layer:
            lowerCamelCase_	  =					convert_resconv_naming(lowercase  )
      elif "attentions" in new_layer:
            lowerCamelCase_	  =					convert_attn_naming(lowercase  )
            lowerCamelCase_	  =					new_string_left

      if not isinstance(lowercase			,							lowercase  ):
            lowerCamelCase_	  =					prefix + '.' + new_layer + '.' + string_left
      else:
            lowerCamelCase_	  =					[prefix + '.' + new_layer + '.' + s for s in string_left]
      return new_string

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



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

            lowerCamelCase_	  =					rename(lowercase  )

            # check if we need to transform from Conv => Linear for attention
            if isinstance(lowercase			,							lowercase  ):
                  lowerCamelCase_	  =					transform_conv_attns(lowercase			,							lowercase			,							lowercase  )
            else:
                  lowerCamelCase_	  =					v

      return new_state_dict

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Optional[Any]			,							lowercase			: Optional[Any]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

      lowerCamelCase_	  =					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_	  =					download(lowercase  )

      lowerCamelCase_	  =					MODELS_MAP[model_name]['sample_rate']
      lowerCamelCase_	  =					MODELS_MAP[model_name]['sample_size']

      lowerCamelCase_	  =					Object()
      lowerCamelCase_	  =					sample_size
      lowerCamelCase_	  =					sample_rate
      lowerCamelCase_	  =					0

      lowerCamelCase_	  =					UNetaDModel(sample_size=lowercase			,							sample_rate=lowercase  )
      lowerCamelCase_	  =					diffusers_model.state_dict()

      lowerCamelCase_	  =					DiffusionUncond(lowercase  )
      orig_model.load_state_dict(torch.load(args.model_path			,							map_location=lowercase  )['state_dict']  )
      lowerCamelCase_	  =					orig_model.diffusion_ema.eval()
      lowerCamelCase_	  =					orig_model.state_dict()
      lowerCamelCase_	  =					rename_orig_weights(lowercase  )

      lowerCamelCase_	  =					set(renamed_state_dict.keys()  ) - set(diffusers_state_dict.keys()  )
      lowerCamelCase_	  =					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":
                  lowerCamelCase_	  =					value.squeeze()

            lowerCamelCase_	  =					value

      diffusers_model.load_state_dict(lowercase  )

      lowerCamelCase_	  =					1_00
      lowerCamelCase_	  =					33

      lowerCamelCase_	  =					IPNDMScheduler(num_train_timesteps=lowercase  )

      lowerCamelCase_	  =					torch.manual_seed(lowercase  )
      lowerCamelCase_	  =					torch.randn([1, 2, config.sample_size]			,							generator=lowercase  ).to(lowercase  )

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

      lowerCamelCase_	  =					DanceDiffusionPipeline(unet=lowercase			,							scheduler=lowercase  )

      lowerCamelCase_	  =					torch.manual_seed(33  )
      lowerCamelCase_	  =					pipe(num_inference_steps=lowercase			,							generator=lowercase  ).audios

      lowerCamelCase_	  =					sampling.iplms_sample(lowercase			,							lowercase			,							lowercase			,							{}  )
      lowerCamelCase_	  =					generated.clamp(-1			,							1  )

      lowerCamelCase_	  =					(generated - audio).abs().sum()
      lowerCamelCase_	  =					(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       :	Union[str, Any]									=     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       :	Dict									=     parser.parse_args()

 main(args)









from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		['''pixel_values''']

 def __init__(       self      :						List[Any]		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						bool = True		,							A_      :						Union[int, float] = 1 / 255		,							A_      :						bool = True		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,							**A_      :						Tuple		,							)     ->			None:

       """simple docstring"""






       super().__init__(**A_						)
       lowerCamelCase_	  =					size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       lowerCamelCase_	  =					resample
       lowerCamelCase_	  =					do_center_crop
       lowerCamelCase_	  =					crop_size
       lowerCamelCase_	  =					do_rescale
       lowerCamelCase_	  =					rescale_factor
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowerCamelCase_	  =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Tuple		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
             lowerCamelCase_	  =					int((256 / 224) * size['shortest_edge']						)
             lowerCamelCase_	  =					get_resize_output_image_size(A_		,							size=A_		,							default_to_square=A_						)
             lowerCamelCase_	  =					{'height': output_size[0], 'width': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""						)
       return resize(
           A_		,							size=(size_dict['height'], size_dict['width'])		,							resample=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Any		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Any		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_						)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""						)
       return center_crop(A_		,							size=(size['height'], size['width'])		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Union[int, float]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Optional[int]		,							)     ->			np.ndarray:

       """simple docstring"""






       return rescale(A_		,							scale=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						List[str]		,							A_      :						np.ndarray		,							A_      :						Union[float, List[float]]		,							A_      :						Union[float, List[float]]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						str		,							)     ->			np.ndarray:

       """simple docstring"""






       return normalize(A_		,							mean=A_		,							std=A_		,							data_format=A_		,							**A_						)





 def        a__				(       self      :						Optional[int]		,							A_      :						ImageInput		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						PILImageResampling = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[float] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[TensorType] = None		,							A_      :						ChannelDimension = ChannelDimension.FIRST		,							**A_      :						List[Any]		,							)     ->			BatchFeature:

       """simple docstring"""






       lowerCamelCase_	  =					do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_	  =					resample if resample is not None else self.resample
       lowerCamelCase_	  =					do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_	  =					do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_	  =					rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_	  =					do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_	  =					image_std if image_std is not None else self.image_std

       lowerCamelCase_	  =					size if size is not None else self.size
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					make_list_of_images(A_						)

       if not valid_images(A_						):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'						)

       if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'						)

       if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'						)

       if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

       # All transformations expect numpy arrays.
       lowerCamelCase_	  =					[to_numpy_array(A_						) for image in images]

       if do_resize:
             lowerCamelCase_	  =					[self.resize(A_		,							A_		,							A_						) for image in images]

       if do_center_crop:
             lowerCamelCase_	  =					[self.center_crop(A_		,							A_						) for image in images]

       if do_rescale:
             lowerCamelCase_	  =					[self.rescale(A_		,							A_						) for image in images]

       if do_normalize:
             lowerCamelCase_	  =					[self.normalize(A_		,							A_		,							A_						) for image in images]

       lowerCamelCase_	  =					[to_channel_dimension_format(A_		,							A_						) for image in images]

       lowerCamelCase_	  =					{'pixel_values': images}
       return BatchFeature(data=A_		,							tensor_type=A_						)



